Underwater walking

Underwater walking

Joseph Ayers*

Department of Biology, Marine Science Center, Northeastern University, East Point, Nahant, MA 01908, USA

Received 8 March 2004; accepted 25 May 2004

Abstract

Lobsters are generalist decapods that evolved in a broad variety of niches in the Northwestern Atlantic. Due to their inherent buoyancy

they have acquired adaptations to reduced traction and surge. We have developed a biomimetic robot based on the lobster that features

artificial muscle actuators and sensors employing labeled-line codes. The central controller for this robot is based on the command neuron,

coordinating neuron central pattern generator model. A library of commands is released by sensor feedback to mediate adaptive sequences

and goal achieving behavior. Rheotaxic behaviors can mediate adaptations to achieve some of the advantages of the biological models.

q 2004 Elsevier Ltd. All rights reserved.

Keywords: Lobster; Robot; Biomimetic; Sensors; Artificial muscle; Locomotion

1. Introduction

The littoral region of the oceans presents a challenging

environment for any organism. Fundamental problems exist

in the shallow inshore. Heavy turbulence caused by surge,

wave action, tides and currents can cause severe stability

problems. These problems have been overcome by bottom

dwelling organisms such as crabs and lobsters that live with

impunity in these complex conditions (Sandeman and

Atwood, 1982; Cobb and Phillips, 1980). Lobsters have

navigated over the ocean bottom formillions of years and have

evolved robust systems for control of locomotion, sensing,

searching, and adaptation to surge (Factor, 1995). Lobsters

oftenmigrate into littoral regionswhere thebidirectional surge

due to wave action and persistent currents due to tidal

movements in channels require ongoing compensation. The

necessity of dealing with surge and currents and the inherent

traction problems are the primary factor differentiating

underwater walking from terrestrial walking. The sprawled

posture of thewalking legs helps prevent overturning in strong

lateral currents (Alexander, 1971). The ambulatory locomo-

tory movements, combined with hydrodynamic adaptability

of decapods are a proven solution to the stability problem

posed by both littoral and stream environments (Maude and

Williams, 1983; Martinez, 2001).

Decapods have evolved other mechanisms to address

surge. Lobsters and crayfish use their claws, abdomen and

swimmerets as hydrodynamic control surfaces and thrusters

(Fig. 1) affording considerable adaptation relative to

hydrodynamic perturbation during locomotion (Davis,

1968; Maude and Williams, 1983). A hydrodynamically

adaptable shape provides negative lift (Martinez, 2001), and

can hold a decapod to the bottom rather than allowing

current and surge to displace it. Maude and Williams (1983)

found that crayfish species with lower drag coefficients were

able to inhabit streams with higher flow velocities.

Although lobsters and crayfish are generally in mechan-

ical contact with the sea floor, their traction is reduced

relative to terrestrial organisms due to their buoyancy.

Hydrodynamic translational forces can often exceed

gravitational forces (Martinez, 2001). Having eight legs in

contact with the substrate therefore gives them more

traction, as does a wedge-like posture that generates a

hydrodynamic thrust vector into the substrate that further

enhances traction (Fig. 1). The additional legs also provide

for more mechanical stability in the pitch plane relative to

the alternating tripod gait of insects.

The mechanisms underlying behavior in decapods have

been formalized into a general model, the command neuron,

coordinating neuron, central pattern generator (CPG) mode

(Kennedy and Davis, 1977; Stein, 1978; Evoy and Ayers,

1982). The central component consists of segmental CPGs

that control the motor neurons and muscles of each limb

1467-8039/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.asd.2004.06.001

Arthropod Structure & Development 33 (2004) 347–360

www.elsevier.com/locate/asd

* Tel.: þ1-617-581-7370x309; fax: þ1-617-581-6076.

E-mail address: [email protected] (J. Ayers).

http://www.elsevier.com/locate/asd

(Sillar and Skorupski, 1986), coordinating systems that

determine the phase relations or gaits between the CPGs of

different limbs and command systems that specify and

modulate the behavior generated by the CPGs. The

command systems represent the control locus at which the

decision to generate a particular behavior is achieved

(Edwards et al., 1999; Larimer, 2000). The peripheral

component consists of exteroceptive and proprioceptive

sensors (Atema and Voigt, 1995; Cattaert and Le Ray, 2001)

that generate adaptive reflexes at the command level

(exteroceptive or orientational reflexes), the CPG level

(phase modulating reflexes) and/or the motor neuron

level (amplitude modulating reflexes).

Arthropod systems have been subjected to considerable

reverse engineering over the past 25 years, adequate to

permit robust synthetic models of their underlying organ-

ization (Beer, 1991; Beer et al., 1998; Ayers et al., 2002). In

several cases the actual synaptic networks have been

established by electrophysiological stimulation and record-

ing (Burrows, 1996; Chrachri and Clarac, 1989; Cattaert and

Le Ray, 2001; Selverston, 1999). These biological models

can be readily adapted to robotic control (Beer et al., 1998;

Ayers et al., 2002). We submit that biologically based

reverse engineering is the most effective procedure both to

design autonomous underwater robots as well as to establish

detailed higher order control schemes for remote sensing

procedures. The control schemes by which a lobster

searches for and acquires prey provide excellent solutions

to the problem of how an underwater robot can successfully

search for and investigate underwater objects (Grasso,

2000).

2. Overview of the lobster walking system

The control of the decapod walking system has been

investigated at almost all levels, from the behavioral to the

neuromodulatory (Factor, 1995). Combined with compara-

tive data from other oscillatory systems in the crustacean

CNS, it is possible to construct reasonably complete control

structures based entirely on known components (Evoy and

Ayers, 1982; Ayers and Crisman, 1992; Ayers et al., 2002;

Cattaert and Le Ray, 2001; Beltz, 1995).

2.1. Limb movements

Lobster walking movements are performed around three

major limb joints (Fig. 2): the thoraco-coxal joint mediates

protraction and retraction movements (Fig. 2a), the coxo-

basal joint mediates elevation and depression movements

(Fig. 2b) while the mero-carpopodite joint mediates

extension and flexion movements (Fig. 2c; Ayers and

Davis, 1977; Ayers and Clarac, 1978). The movements of

the coxo-basal and mero-carpopodite joints occur in the

same plane while movements of the thoraco-coxal joint

occur in a plane at right angles to the others. Thus there is a

clear demarcation between opposing movements of the

basilar leg joints.

The lobster step cycle consists of three phases: an early

swing phase lifts the limb tip toward the initial position of

the stance, an early stance phase drops the leg to initiate the

stance and during the late stance phase the limb applies

propulsive force and compensates for gravity. During

walking, the swing phase is always constant in duration

and variation in the step period is mediated by variation in

the duration of the stance phase (Ayers and Davis, 1977).

Cyclic elevation and depression movements of the coxo-

basal joints underlay the swing and stance phase movements

respectively for walking in all four directions (Fig. 2d and

g). Movements of the thoraco-coxal and mero-carpopodite

joints generate propulsive forces (Fig. 2e and i). During

forward and backward walking propulsive forces are

generated by torques about the thoraco-coxal joint. During

lateral walking propulsive forces are generated by the

torques about the mero-carpopodite joint. Propulsive forces

that are synergistic with depression for walking in one

direction (retraction for forward walking, protraction for

backward walking) become antagonistic for walking in the

opposite direction (Fig. 2e and i).

2.2. Neuronal circuits

Many acts of the lobster and crayfish behavioral

repertoire can be evoked in situ by stimulation of single

descending command neurons (Davis and Kennedy, 1972;

Fig. 1. Macruran compensatory postures to low (a), high speed (b) and

backward (c) water currents (from Maude and Williams, 1983).

J. Ayers / Arthropod Structure & Development 33 (2004) 347–360348

Bowerman and Larimer, 1974a,b; Larimer, 2000). There is a

population of command neurons that evoke forward

and backward walking with slightly different postures

comprising a parsimonious population code (Sparks et al.,

1997). Posture and some rhythmic behaviors can also be

regulated by neuromodulators (Harris-Warrick and Marder,

1991). Sillar and Skorupski (1986) developed a preparation

of the isolated crayfish thoracic chain that generated

elements of the walking motor program that differed from

the walking motor programs observed in vivo. Efforts to

reproduce the walking program with pharmacological

stimulation in this preparation were unsuccessful (Chrachri

and Clarac, 1990). These findings led to an assumption that

proprioceptive feedback was essential for the generation of

the walking program (Cattaert and Le Ray, 2001), a view

inconsistent with demonstrations in the rest of the animal

kingdom (Delcomyn, 1980).

An alternative view is that normal expression of the

walking program is mediated by command systems that

select the programs for walking in different directions

(Bowerman and Larimer, 1974b) from a thoracic CPG that

may produce multiple behavioral acts (Sillar and Skorupski,

1986). In the lobster abdomen, an excellent replica of the

swimmeret motor program can be generated by command

neuron stimulation (Davis and Kennedy, 1972), This

experiment has apparently never been attempted in the

thoracic ganglion preparation and is a key experiment for

future work.

The role of walking commands can be inferred

indirectly (Ayers and Davis, 1977). A hypothetical

neuronal network for the control of walking based on

neuronal oscillators, command and coordinating neurons

has been developed (Fig. 3). This hypothetical network

has been instantiated with electronic neurons and is

capable of controlling walking of a nitinol actuated

robot leg in four directions (Ayers et al., 2003). The

basis of the hypothetical network is direct connections

between oscillator neurons and propulsive force neurons

(Chrachri and Clarac, 1989) modulated by presynaptic

inhibition that is common in decapod thoracic ganglia

(El Manira et al., 1991). According to this model,

command systems specify the coordination pattern

through modulation of specific connections to specify

walking direction (Fig. 3). Command systems must also

Fig. 2. Joint movement and muscle synergy control patterns. (a–c) Indicate the angular coordinates of movement of the coxo-basal joint (a), the thoraco-coxal

joint (b) and the mero-carpopodite joint (c). (d–i) Indicates the angles of the coxo-basal, thoraco-coxal and mero-carpopodite joints plotted vs. phase in the step

cycle. Forward walking is indicated by closed circles, while backward walking is indicated by open circles. (j) Indicates the timing of the early swing phase,

late swing and stance phase (blue). (k) Indicates the timing of activity in the major muscle synergies elevator, depressor, swing and stance. Adapted from Ayers

and Davis, 1977.

J. Ayers / Arthropod Structure & Development 33 (2004) 347–360 349

specify the amplitude of propulsive movements through

recruitment from the motor unit pool (Davis and

Kennedy, 1972). This connectivity is capable of

explaining both the rapid changes in walking direction

seen in behaving specimens as well as diagonal walking

where both the thoraco-coxal and mero-carpopodite

joints participate in the generation of propulsive forces

(Ayers et al., 2002).

2.3. A biomimetic lobster robot

We have developed a biomimetic lobster-based robot

using the following process.

(1) Use of a biomimetic plant. We use the general body

form of the lobster. Our lobster robot has its legs

attached to a central hull that also supports claw and

tail-like hydrodynamic control surfaces.

(2) Use of a neuronal circuit-based controller. The brains

of our robot consist of a set of finite state machines

based on the command neuron, coordinating neuron,

CPG model of the organization of motor systems

(Stein, 1978).

(3) Use of myomorphic actuators. We employ artificial

muscle based linear actuators that can recruit force in

the same manner as lobster neuromuscular systems

(Atwood, 1976).

(4) Use of neuromorphic sensors. We employ sensors that

code environmental information in the same fashion as

the sensors of the model organism. They use a labeled

line code that quantizes the timing, amplitude and

orientation of the input stimulus (Bullock, 1978).

(5) Use of a behavioral library based on reverse

engineered sequences of behavior of the target

organisms (Ayers et al., 2002). Much of the behavior

of lobsters consists of sequences of stereotyped acts

that are triggered by environmental input. We are

developing an extensible library of command state

sequences that underlie different acts and are associ-

ated with sensor-based releasers.

The physical plant of this robot is composed of watertight

electronics and battery bays on which 8 three-degree of

freedom nitinol-actuated walking legs are mounted (Fig. 4).

The robot has anterior and posterior hydrodynamic control

surfaces to mimic the chelae and abdomen of the model

organism. Motor drives move these control surfaces as well

as a pair of flexible antennae that use strain gauges to

monitor their bending in response to contact and flow. A

sonar transponder allows supervisory control.

2.4. Neural circuit-based controller

Most previous biomimetic robots use behavioral

based controllers where finite state machines focus on

the implementation of different behavioral acts (Brooks,

1989). Other investigators have used genetic algorithms.

In contrast, we implemented the command neuron,

coordinating neuron central pattern generator (CCCPG)

model for omnidirectional walking as a finite state

machine (Ayers et al., 2002). Since the controller

communicates with the interface boards using a serial

bus the robot can be operated from an external lap top

computer or from an embedded controller (Persistor,

Fig. 3. Hypothetical neuronal network for the control of omnidirectional stepping (Adapted from Ayers and Davis, 1977; Ayers et al., 2002, 2003). Circles

indicate presynaptic or conventional inhibitory connections. Triangles indicate excitatory connections. The orange rectangle encloses an interneuron network

that is modulated to generate the rhythm.


Motorola 68020). The program treats the CCCPG model

components as objects which pass messages with regard

to status changes. Changes in walking direction, speed,

load etc., are effected by keystrokes on the laptop,

sensor input or sonar supervisory commands to the

embedded processor. The program generates strip chart

displays that are the basis of many of the figures of

experimental results in this manuscript.

At the single limb control level, the ambulation

controller relies on three major classes of components that

control the elevator, depressor, protractor, retractor, exten-

sor and flexor synergies (Fig. 5). The oscillator component

is a software clock that regulates the period of stepping as

well as the duration of the swing or elevator phase fraction

of the stepping cycle based on the dynamics of an

endogenous pacemaker neuron (Ayers and Selverston,

1979). The oscillator is embodied as the elevator synergy

and is the site of action of coordinating input from other

oscillators and phase modulating feedback.

The second major component of the ambulation

controller is the pattern generator. It determines the

pattern of discharge of bifunctional synergies based on

command input. The pattern generator determines

through a truth table what synergies should be active

and sets or clears the Boolean variables associated with

the on or off state of the different bifunctional

synergies. The truth table implements the presynaptic

inhibitory logic of the neuronal circuit model (Fig. 3)

and specifies the excitatory connections that would be

disabled by the directional command.

The third major component of the ambulation controller

is the recruiter that determines which of the elements of the

propulsive force synergies are active (Fig. 6a). The recruiter

responds to the desired period parameter and recruits motor

Fig. 4. The lobster-based robot.

Fig. 5. Organization of finite state machine controller. A neuronal oscillator

organizes the four-phase pattern. The neuronal oscillator receives phase

modulating inputs from coordinating neurons and proprioceptors as well as

parametric modulation from command neurons. The pattern generator

selects patterns for sub-behaviors in response to command logic. The

compensator receives inputs from pitch and roll sensors to modulate

posture. The propulsive recruiter responds to parametric modulation and

load sensors to control the synergies producing propulsive force. The

compensator end propulsive recruiter activate motor synergies controlling

different leg joints.


units of increasing size (Davis, 1971). Fig. 6b and c is a

graphical realization of the output of the ambulation

controller demonstrating adaptation to speed during forward

and backward walking. In propulsive force synergies the

width of the trace indicates the degree of recruitment within

the pool. Notice that during increase in speed the period of

stepping decreases while propulsive force synergies are

selectively recruited.

The outputs of the finite state machine are control signals

that specify the timing and amplitude of actuator action.

These signals are used to gate power transistors at different

duty cycles to activate contractions of the artificial muscle

just as motor neuron action potentials activate muscle. Fig.

6d–g indicates the signals controlling the thoraco-coxal,

coxo-basal and mero-carpopodite joints during walking in

all four directions. Note that movement synergies that are

synergistic for walking in one direction become antagonistic

for walking in the opposite direction. Antagonist muscles of

joints that serve a postural function in a particular walking

direction are co-activated at low amplitude (Ayers and

Clarac, 1978).

2.5. Coordinating systems

Coordinating systems operate by providing timing

information from a governing oscillator to a governed

oscillator. The governed oscillator responds by resetting it’s

timing to match the period of the governing oscillator

(Stein, 1976; Ayers and Selverston, 1979; Pinsker and

Ayers, 1983). The emergent behavior of the known

intersegmental coordinating interneurons is the alternating

tetrapod gait (Wilson, 1967). In the walking system, there

are coordinating systems between contralateral pattern

generators of the same segment as well as coordinating

systems between adjacent segments. Although the network

basis is not established, contralateral coordination appears

Fig. 6. Motor programs for omnidirectional walking generated by the finite state machine. (a) Recruitment circuit that mediates size-ordered recruitment of

actuators. (b and c) Adaptation to speed indicating size ordered recruitment of propulsive synergies indicated by the width of the trace. (d–g) Omnidirectional

patterns indicating co-activation of antagonists for joints that serve a postural function. Ele, elevator synergy; Dep, depressor synergy; Pro, protractor synergy;

Ret, retractor synergy; Ext, extensor synergy; Flx, flexor synergy. The time marks occur once/second.


to be mediated by reciprocal inhibition (Sillar et al., 1987).

Intersegmental connections consist of a central synchronous

component that is probably most significant in the context of

leg waving behavior (Pasztor and Clarac, 1983), as well as

an effective indirect inhibitory component mediated by the

load sensitive funnel canal organs in the dactyl of each limb

which is appropriate to the gaits seen during walking

(Libersat et al., 1987).

In the controller, the limb coordinating systems are

implemented as the elevator synergies that pass messages to

the elevators of adjacent limbs (Fig. 7a). Fig. 7b and c

indicates stable alternating tetrapod coordination generated

by the ambulation controller with the empirically estab-

lished contralateral phase lag of 0.5 and an ipsilateral phase

lag of 0.4 (Clarac and Barnes, 1985).

2.6. Myomorphic actuators

We have had excellent results with artificial muscle

fabricated from the shape memory alloy nitinol (Fig. 8a).

Shape memory metals undergo a state transformation

(Fig. 8b) from a deformable state (martensite) to a

remembered state (austenite). When formed into wires,

nitinol may change its length by as much as 5% during

contraction (Duering et al., 1990). Nitinol has a maximum

and minimum range of action specified by its stretched and

remembered lengths. Within this range its control is

proportional, depending upon the level of current passed

or the linear extent of the wire through which current is

passed (Witting and Safak, 2000).

Nitinol wires can be activated to contract through the

heat generated by the pulses of electrical current. We use

pulse width duty cycle modulation to grade the proportion

of martensite that transforms to austenite. Discrete duty

cycles are used to mimic small medium and large motor

units (Fig. 8c). Increases in duty cycle increase the speed

and amplitude of contraction (Fig. 8d). These properties

map directly on the architecture of the ambulation

controller.

2.7. Neuromorphic sensors

We have developed several biomimetic sensors necess-

ary to mediate reactive tactile navigation on the ocean

bottom. All sensors code information with a labeled line

code (Bullock, 1978). Each sensor is represented by a byte,

Fig. 7. (a) Messages passed by the coordinating systems. The bars indicate the time of activity in the elevator synergy of legs 1–4 on the left and right sides. (b)

Coordination during increasing speed of walking. (c) Interlimb coordination during a turn to the left indicated by the horizontal bar.


each bit of which corresponds to a labeled line. The labeled

line represents three characteristics of the stimulus (1) The

sensory modality, (2) the receptive field or orientation

relative to the body and (3) the amplitude of the stimulus.

All sensors are polled by the state machine and return a byte

representing their status. This byte is compared to bit masks

representing different releasers.

2.7.1. Antennae

To realize antennal sensors (Fig. 4), we rely on the use of

strain gauges to monitor bending. The strain gauge is

mounted in the middle of the antennae and interfaced to the

mothor board using a Wheatstone bridge circuit. A PIC

microcontroller digitizes the analog signal converts it to

seven discrete states represented by a seven bit labeled line

code. Each of the bits represents one of seven bend states:

straight, three magnitudes of bending to lateral or medial

and an eighth bit that indicates when the antenna is buckled

by a head on collision.

These antennae can be projected at four different angles

by the motor drive depending on context. They are capable

of distinguishing water currents from collisions with solid

objects such as rocks. The byte masks associated with the

two antennae must be interpreted in the context of the

position of the antennae relative to the hull. For example,

the buckling bit can be set by a head-on collision if the

antennae are projected forward or by a collision to either

side if they are projected laterally. Thus identification of

releasers requires that the returned data are interpreted

through a decision tree based on the position or movement

of the antennae. Responses must also be interpreted in the

context of movements between positions that can bend the

antennae.

2.7.2. Active touch function

The antennae are controlled by a motor drive that allows

them to sweep over arbitrary ranges between four basic

positions. If the antennae collide with an object in a sweep

range it will indicate bending to high magnitudes and a

relative position is indicated by the time of the bending

within the sweep (Zeil et al., 1985).

2.7.3. Collision detection function

One of the characteristics of the underwater operation of

these sensors is that they buckle when they make a head on

collision with an object. This is associated with a rapid left–

right bend of the antennae. The PIC microcontroller is

programmed to recognize such events and sets the eighth bit

for each antennae for 150 ms following such collisions.

2.7.4. Water motion function

The antennae have been calibrated in a laminar flow

system and also indicate water flow. For example if the

antennae are statically projected forward and both are bent

to the right this would indicate flow from the left. If the

antennae are projected statically laterally to the body axis,

they become a flow sensor for axial currents and can

Fig. 8. (a) A nitinol myomorphic actuator. (b) The shape memory effect (from Duering et al., 1990). (c) Pulse width duty cycle modulation. (d) Graded

contractions produced by a nitinol muscle element. The upper trace in each panel represents the shortening of a muscle produced by the pulse trains in the lower

trace. Upward deflections indicate shortening. The duty cycle is indicated to the right of each panel.


indicate when the vehicle is oriented into flow or surge on

the basis of symmetric deflections on both sides.

2.7.5. Inclinometers

Lobsters normally sense the orientation of their bodies

using vestibular organs called the statocysts (Cohen, 1955).

Here individual labeled lines code for orientation relative to

gravity in the pitch and roll planes. For biomimetic

implementation we use a MEMs accelerometer and convert

the analog signal into eight discrete sectors of resolution for

an 808 range of motion relative to vertical. The labeled lines

are represented by eight bits for each of the eight sectors.

2.7.6. Bump sensors

Observations of movements of blinded lobsters using a

‘lobster cam’ indicate that they use a ‘bump’ sense mediated

by their chelipeds to navigate around low objects like rocks.

We have implemented bump sensors for the claws based on

a solid-state accelerometer. These sensors take the analog

signal coming form the accelerometer, full wave rectify it,

low pass filter it and threshold the low pass signal to

generate a bump signal.

2.7.7. Compass

It has recently been demonstrated that spiny lobsters can

sense the earths magnetic field and orient their migrations

relative to it (Boles and Lohmann, 2003). To confer the

robot with a similar ‘sense of direction’ we have integrated a

flux gate compass with interface that provides three bits of

resolution (eight heading sectors).

2.8. Behavioral controller

We are currently in the process of implementing the

behavioral controller of the robot. All of the behavior of

the robot can currently be controlled by teleoperation,

pre-programmed behavioral sequences can be played

and all of the sensors are functional. We are currently

in the process of implementing the sensory-motor

reflexes by optimizing the connections between sensor

objects and command objects. In the following sections

I present a progress report of the present organization of the

software and our plans for implementation of reactive

autonomous behavior.

The higher order controller of the ambulatory vehicle is

based on command objects. The operational state of the

appendages is specified by a set of 11 internal state variables

that configure the appendage state machines on the fly. The

basic structure of the controller is a real time loop that polls

sensors, manages a command stack, selects behavioral acts

and updates the leg state machines. An attention module

polls each of the sensors at a different rate depending on

behavioral context. The attention module keeps a list of the

clock time at which subsequent poll of a sensor is to occur

in temporal order and updates this list every time a sensor

is read.

Fig. 9. Behavioral quantization. Upper left panel: Screen shot of the program ColorImage in the process of decomposing a behavioral sequence into underlying

commands. The output of ColorImage is a command sequence table (bottom panel), where each row is a frame of the movie and each column represents the

changes underlying rheotaxic behavior. The upper right panel is a cartoon of a rheotaxic sequence that can be displayed on an animator.


2.9. Behavioral libraries

The control architecture includes a command sequencing

capability that can store pre-programmed action patterns,

reverse engineered from observations of the adaptations of

the animal model that form the basis of a behavioral library

(Ayers et al., 2002). The fundamental assumption of this

behavioral control model is that the posture and action of the

different body parts is specified by a set of command

systems (Bowerman and Larimer, 1974a,b) that command

the task group to generate different states. To derive the

underlying commands states, we determine the posture or

action of the different appendages in each of the frames of a

movie. We categorize these actions or movements into one

of a set of states (see below). The movies are selected to

capture the model organism performing the act that one

wants the vehicle to mimic (Fig. 9). This analysis therefore

represents an inference of the states of the underlying

command neuron activity. We have found the following set

of command states adequate to define the ongoing behavior

of both the lobster and the robotic vehicle.

Thorax Pitch: rostrum up, level, rostrum down.

Thorax Roll: left up, level, right up

Thorax Yaw: hard left, easy left, straight, easy right, hard

right

Thorax Height: high, normal, low

Walking direction: standing, forward, backward, lateral

leading lateral trailing

Walking speed: slow, medium, fast, stop

Claw Pitch: up, normal, down

Claw Yaw: (left and right): extended, normal, meral

spread, lateral spread

Antennae Yaw: protracted, normal, lateral, retracted

Uropod Posture: flared, normal, adducted

Abdominal Pitch: extended, elevated, normal,

depressed, flexed

To derive robotic control sequences from video data, we

perform finite state analysis of the task groups that mediate

locomotion and searching individually to determine which

synergistic sets are operant during different behavioral acts.

A panel of buttons that represent different states of the task

groups (e.g. elevation vs. depression of the chelipeds, etc.)

are available to the investigator to specify which groups are

active (Fig. 9). By clicking on the appropriate buttons for

each frame, it is possible to efficiently quantify the activity

of all task groups at high temporal resolution from

videotapes of specimens behaving in a variety of situations.

The result of this analysis is a command sequence; a table of

numbers where each row represents a frame of video and

each column represents the state of a particular task group.

These tables are abstracted by the sequencer to the start and

stop times of particular commands for sequencing. This

expanding set of command sequences constitutes the

behavioral library of the vehicles. These libraries are

evoked by sensor input and played out by a sequencer that

is composed of the following components.

2.9.1. Releasers

When a sensor is polled, the result is compared to a list of

byte masks defining particular releasers. For example if an

antennal poll detected that the left antenna is bending

medially and the right antenna is bending laterally this

would constitute the releaser for a rheotaxic rotational turn

to the left. When the attention module identifies a behavioral

releaser, a look up table is used to identify the behavioral

sequence table associated with that particular releaser.

2.9.2. Sequencer

Whenever a releaser is identified the sequencer sets up

the sequence of command state changes that mediate the

evoked behavior. The sequencer actually performs two

tasks. First the releaser may specify a suppressor or table of

incompatible action components. For example, if the

evoked behavior involves forward walking, any instances

of backward walking command action components must be

cleared from the command stack. This suppressor action is

the locus of implementation of behavioral choice between

incompatible commands. Secondly, the releaser sets up the

behavior by pushing the behavioral state changes on the

stack in temporal order.

Animals normally can evoke behavioral acts at different

levels of intensity and this is typically mediated by

neuromodulators. The behavioral sequencer allows the

command state transitions to be pushed on the stack at

different temporal compressions to vary intensity. In

addition, the pulse width duty cycles associated with the

low, medium and high levels of recruitment of the nitinol

actuators can be varied. Thus at high intensity the sequence

of command state changes are compressed on the stack and

the recruitment state pulse width duty cycle mapped to a

high level of intensity. In contrast, at low intensity the

sequence of command state changes are expanded on the

stack and the recruitment state pulse width duty cycle

mapped to a low level of intensity.

During operation, releasers are detected by sensors and

evoke the sequencer to push the sequence of command state

changes on the command stack. As the robot continues

operation the command state changes are played out in

temporal order to modulate the operation of the appendage

state machines. As behaviors may superimpose or switch in

response to sensor input these operations are mediated by

the sequencer.

2.10. Sensory-motor reflexes

The robot features an expanding set of exteroceptive

reflexes in response to deviations from a desired heading or

posture, collision and surge. Exteroceptive reflexes act on

command systems and or intersegmental modulatory

interneurons by inducing lateral biases (Davis and Ayers,


1972; Kennedy and Davis, 1977). The way we have

implemented these reflexes can be best illustrated by control

of responses to surge by the antennae.

Depending on the habitat, the robot may be exposed to

low flow (Fig. 10a), unidirectional flow from tides (Fig. 10b)

or bidirectional surging flow from waves (Fig. 10c). In

surging flow, the releaser is flow detected by the antennae

that are held laterally. Antennal labeled lines responding to

lateral bending of the antennae project to the contralateral

walking command while labeled lines responding to medial

bending project to the ipsilateral forward walking command

(Fig. 10d). Due to this logic, if the surge is approaching from

off the midline, it will bias the output of the walking

commands such that the vehicle will yaw into the flow if the

flow is coming from the front but yaw away from the flow if

it is coming from the rear. This combination of reflexes

facilitates locomotion in surging water. When surge is

coming from the front, the vehicle will load compensate by

increasing the output of the walking commands. When

surge is coming from the rear, the vehicle will run with it.

Examples of control signals to the left and right leg when

yawing are shown in Fig. 10d. The robot has been modeled

dynamically to account for both hydrodynamic and

frictional forces (Safak, 2001). In still water the simulation

indicates the robot can locomote at speeds of up to,6 cm/s

which is above the observed speed of the present vehicle

(,3 cm/s). In the absence of hydrodynamic compensation,

the simulation indicates that the vehicle will stall when

walking into flow velocities of between 10 and 12 cm/s

depending on the coefficient of friction, and a maximum

speed of ,20 cm/s when walking with a 30 cm/s assisting

flow (Safak, 2001). Above opposing flow velocities of

12 cm/s the vehicle will slip.

In addition to yaw plane compensation, lobsters and

crayfish use their chelae, abdomen and uropods as

hydrodynamic control surfaces (Fig. 1) and in real lobsters

these surfaces are large relative to the thorax (Fig. 11a).

Compensatory reflex responses to water currents involve

simultaneous compensation of the thorax in the pitch plane

as well as postural responses of the abdomen and chelipeds.

Fig. 11 b and c indicates the compensatory response of the

anterior and posterior control surfaces in the lobster robot.

The ambulation controller supports antigravity recruiters for

each leg that act on the depressor synergy and mediates

pitch and roll compensation. Pitch control ‘interneurons’

bias the depressor recruiters along the body axis and

mediate pitch compensation (Fig. 11d–f). Pitching the hull

forward will provide additional hydrodynamic compen-

sation (Martinez, 2001). These compensatory responses to

water currents and surge can simultaneously integrate both

yaw and pitch plane components independent of the

direction and speed of walking.

2.11. Navigation

The yaw components of navigation are mediated in

biological systems by taxes and kineses (Loeb, 1918;

Braitenberg, 1984). Positive yaw taxes or attraction occur

Fig. 10. (a–c) Hydrodynamic environments that the robot might be expected to operate in (from Martinez, 2001). (a) Still water. (b) Unidirectional flow. (c)

Bidirectional surge. (d) Reflex circuitry to mediate rheotaxic exteroceptive reflexes resulting from water currents from off center. See text for description of

operation. (d) Yaw plane modulating motor patterns during forward walking. The traces indicate the elevator, depressor and retractor synergies of the left and

right sides.


when sensor bias directs locomotion toward source and are

generally mediated by contralateral causality between

sensors and effectors. Negative yaw taxes or avoidance

occur when sensor bias directs locomotion away from

source and are generally mediated by ipsilateral causality

between sensors and effectors. In the ambulation controller,

such inputs send messages to both the parametric command

as well as the recruiting component. In attractive reflexes

the messages are sent to the contralateral command objects

while during avoidance reflexes (negative taxis) the

messages are sent to the ipsilateral command objects. As a

result, the controller both biases the period on the two sides

(walking faster on the side turned away from) as well as the

recruiters on the two sides to generate more propulsive force

on the faster side (Fig. 9e). Control schemes like that of

Fig. 9d can be easily adapted to a variety of beacon tracking,

avoidance, docking and search behavior through acoustic,

magnetic, optical, tactile, gravitational, flow and chemical

sensors.

Acknowledgements

Supported by the Defense Advanced Research Projects

Agency and the Office of Naval Research.

References

Alexander, R., 1971. Size and Shape, Edward Arnold, London, p. 59.

Atema, J., Voigt, R., 1995. Behavior and sensory biology. In: Factor, J.R.,

(Ed.), The Biology of the Lobster, Homarus americanus, Academic

Press, San Diego, pp. 313–344.

Atwood, H.L., 1976. Organization and synaptic physiology of crustacean

neuromuscular systems. Progress in Neurobiology 7, 291–391.

Fig. 11. Hydrodynamic compensation. (a) Dorsal view of Homarus indicating the relatively large hydrodynamic control surfaces formed by the chelae,

abdomen and uropods (adapted from Huxley, 1973). (b) The lobster robot with abdomen and ‘chelae’ in the horizontal position. (c) The lobster robot with

abdomen and chelae in the wedge configuration (see Fig. 1). (d–f) Network combinatorics between modulatory interneurons and depressor motor synergies to

mediate rostrum (row) down (d), normal (e) and rostrum up (f) postures.


Ayers, J., Clarac, F., 1978. Neuromuscular strategies underlying different

behavioral acts in a multifunctional crustacean leg joint. Journal of

Comparative Physiology A: Sensory, Neural and Behavioral Physi-

ology 128, 81–94.

Ayers, J., Crisman, J., 1992. The lobster as a model for an omnidirectional

robotic ambulation control architecture. In: Beer, R., Ritzmann, R.,

McKenna, T. (Eds.), Biological Neural Networks in Invertebrate

Neuroethology and Robots, pp. 287–316.

Ayers, J., Davis, W.J., 1977. Neuronal control of locomotion in the lobster,

Homarus americanus. I. Motor programs for forward and backward

walking. Journal of Comparative Physiology A: Sensory, Neural and

Behavioral Physiology 115, 1–27.

Ayers, J., Selverston, A.I., 1979. Monosynaptic entrainment of an

endogenous pacemaker network: a cellular mechanism for von Holst’s

magnet effect. Journal of Comparative Physiology A: Sensory, Neural

and Behavioral Physiology 129, 5–17.

Ayers, J., Davis, J., Rudolph, A., 2002. Neurotechnology for Biomimetic

Robots, MIT Press, Cambridge.

Ayers, J., Volkovski, A., Rulkov, N., Selverston, A., Abarbanel, H.,

Rabinovich, M., 2003. Building a CNS for the lobster robot with INLS

electronic neurons. Society for Neuroscience Abstracts 27: 278. 14.

Beer, R.D., 1991. Intelligence as Adaptive Behavior: An Experiment in

Computational Neuroethology, Academic Press, New York.

Beer, R.D., Chiel, H.J., Quinn, R.D., Ritzmann, R.E., 1998. Biorobotic

approaches to the study of motor systems. Current Opinion in

Neurobiology 8, 777–782.

Beltz, B., 1995. Neurobiology and neuroendocrinology. In: Factor, J.R.,

(Ed.), The Biology of the Lobster, Homarus americanus, Academic

Press, San Diego, pp. 267–285.

Boles, L.C., Lohmann, K.J., 2003. True navigation and magnetic maps in

spiny lobsters. Nature 421, 60–63.

Bowerman, R.F., Larimer, J.L., 1974a. Command fibers in the circumoe-

sophageal connectives of crayfish. I. Tonic fibers. Journal of

Experimental Biology 60, 95–117.

Bowerman, R.F., Larimer, J.L., 1974b. Command fibers in the circumoe-

sophageal connectives of crayfish. II. Phasic fibers. Journal of


Braitenberg, V., 1984. Vehicles: Experiments in Synthetic Psychology,

MIT Press, Cambridge.

Brooks, R.A., 1989. A robot that walks; emergent behaviors from a

carefully evolved network. Neural Computation 1, 253–262.

Bullock, T.H., 1978. An Introduction to Neuroscience, Freeman, San

Francisco.

Burrows, M., 1996. The Neurobiology of an Insect Brain, Oxford

University Press, Oxford, 696 pages.

Cattaert, D., Le Ray, D., 2001. Adaptive motor control in crayfish. Progress

in Neurobiology 63, 199–240.

Chrachri, A., Clarac, F., 1989. Synaptic connections between motor

neurons and interneurons in the fourth thoracic ganglion of the crayfish,

Procambarus clarkii. Journal of Neurophysiology 62, 1237–1250.

Chrachri, A., Clarac, F., 1990. Fictive locomotion in the fourth thoracic

ganglion of the crayfish, Procambarus clarkii. The Journal of

Neuroscience 10, 707–719.

Clarac, F., Barnes, W.J.P., 1985. Peripheral influences on the coordination

of the legs during walking in decapod crustaceans. In: Bush, B.M.H.,

Clarac, F. (Eds.), Coordination of Motor Behavior, Cambridge

University Press, Cambridge, pp. 249–269.

Cobb, J., Phillips, B. (Eds.), 1980. The Biology and management of

lobsters, Physiology and Behavior, Vol. I. Academic Press, New York.

Cohen, M.J., 1955. The function of receptors in the statocyst of the lobster,

(Homarus americanus). Journal of Physiology 130, 9–33.

Davis, W.J., 1968. Lobster righting responses and their neural control.

Proceedings of the Royal Society B 70, 435–456.

Davis, W.J., 1971. Functional significance of motor neuron size and soma

position in the swimmeret system of the lobster. Journal of

Neurophysiology 34, 274–288.

Davis, W.J., Ayers, J., 1972. Locomotion: control by positive feedback

optokinetic responses. Science 177, 183–185.

Davis, W.J., Kennedy, D., 1972. Command interneurons controlling

swimmeret movements in the lobster 1. Types of effects on

motoneurons. Journal of Neurophysiology 35, 1–12.

Delcomyn, F., 1980. Neural basis of rhythmic behavior in animals. Science

210, 492–498.

Duering, T.M., Stockel, K.N., Wayman, D.C.M., 1990. Engineering

Aspects of Shape Memory Alloys, Butterworth Heinemann, Ltd,

London.

Edwards, D.H., Heitler, W.J., Krasne, F.B., 1999. Fifty years of the

command neuron: the neurobiology of escape behavior in the crayfish.

Trends in Neuroscience 22, 153–161.

El Manira, A., DiCaprio, R.A., Cattaert, D., Clarac, F., 1991. Monosynaptic

interjoint reflexes and their central modulation during fictive

locomotion in crayfish. European Journal of Neuroscience 3,

1219–1231.

Evoy, W., Ayers, J., 1982. Locomotion and control of limb movements. In:

Atwood, D.C., Sandeman, H. (Eds.), The Biology of Crustacea, Neural

Integration and Behavior, Vol. 4. Academic Press, New York,

pp. 62–106.

Factor, J.R., 1995. Biology of the Lobster, Homarus americanus, Academic

Press, New York.

Grasso, F.W., 2000. Flow and chemo-sense for robot and lobster guidance

in tracking chemical sources of turbulence. In: Ayers, J., Davis, J.,

Rudolph, A. (Eds.), Neurotechnology for Biomimetic Robots, MIT

Press, Cambridge, pp. 553–571.

Harris-Warrick, R.M., Marder, E., 1991. Modulation of neural networks for

behavior. Annual Review of Neuroscience 14, 39–57.

Huxley, T.H., 1973. The crayfish: an introduction to the study of zoology,

MIT Press, Cambridge.

Kennedy, D., Davis, W.J., 1977. Organization of invertebrate motor

systems. In: Geiger, R., Kandel, E., Brookhart, J.M. (Eds.), Handbook

of Physiology, American Physiology Society, Bethesda, MD, pp.

1023–1087.

Larimer, J.L., 2000. The interneurons of the abdominal positioning system

of the crayfish. Brain, Behavior and Evolution 55, 241–247.

Libersat, F., Clarac, F., Zill, S., 1987. Force sensitive mechanoreceptors of

the dactyl of the crab: single unit responses during walking and

evaluation of function. Journal of Neurophysiology 57, 1601–1607.

Loeb, J., 1918. Forced Movements, Tropisms and Animal Conduct,

Lippincott, Philadelphia.

Martinez, M.M., 2001. Running in the surf: hydrodynamics of the shore

crab Grapsus tenuicrustatus. Journal of Experimental Biology 204,

3097–3112.

Maude, S.H., Williams, D.D., 1983. Behavior of crayfish in water currents:

hydrodynamics of eight species with reference to their distribution

patterns in Southern Ontario. Canadian Journal of Fisheries and Aquatic

Sciences 40, 68–77.

Pasztor, V.M., Clarac, F., 1983. An analysis of waving behavior: an

alternative motor program for the thoracic appendages of decapod

crustacea. Journal of Experimental Biology 102, 59–77.

Pinsker, H.M., Ayers, J., 1983. Neuronal Oscillators. In: Willis, W.D.,

(Ed.), The Clinical Neurosciences. Section Five. Neurobiology,

Chapter, Churchill Livingstone Publishers, London, pp. 203–266,

Chapter 9.

Safak, K., 2001. Modeling and Analysis of a Biomimetic Underwater

Robot, PhD Dissertation Northeastern University.

Sandeman, D.C., Atwood, H.L., 1982. The Biology of crustacea, Neural

Integration and Behavior, Vol. 4. Academic Press, New York.

Selverston, A., 1999. General principles of rhythmic motor pattern

generation derived from invertebrate CPGs. Progress in Brain Research

123, 247–257.

Sillar, K.T., Skorupski, P., 1986. Central input to primary afferent

neurons in crayfish, Pacifastacus lenuisculus, is correlated with

rhythmic motor output of thoracic ganglia. Journal of Neurophysiology

55, 678–688.


Sillar, K.T., Clarac, F., Bush, B.M.H., 1987. Intersegmental coordination of

central neural oscillators for rhythmic movements of the walking legs in

crayfish, Pacifastacus lenuisculus. Journal of Experimental Biology

131, 245–264.

Sparks, D.L., Kristan, W.B., Shaw, B.K., 1997. The role of population

coding in the control of movement. In: Stein, P.S.G., Grillner, S.,

Selverston, A., Stuart, D. (Eds.), Neurons Networks and Motor

Behavior, MIT Press, Cambridge, MA, pp. 21–32.

Stein, P.S.G., 1976. Mechanisms for interlimb phase control. In: Herman,

R.M., Grillner, S., Stein, P.S.G., Stuart, D.G. (Eds.), Neural Control of

Locomotion, Plenun Publishing, New York, pp. 465–487.

Stein, P.S.G., 1978. Motor systems, with specific reference to the control of

locomotion. Annual Review of Neuroscience 1, 61–81.

Wilson, D.M., 1967. Stepping patterns in tarantula spiders. Journal of


Witting, J., Safak, K., 2000. Shapememory alloy actuators applied to biomimetic

underwater robots. In: Ayers, J., Davis, J., Rudolph, A. (Eds.), Neurotechnol-

ogy for Biomimetic Robots, MIT Press, Cambridge, pp. 117–135.

Zeil, J., Sandeman, R., Sandeman, D., 1985. Tactile localization: the

function of active antennal movements in the crayfish Cherax

destructor. Journal of Comparative Physiology A: Sensory, Neural

and Behavioral Physiology 157, 607–617.


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