Intelligent Controller for Mobile Robot Based on Heuristic rule Base Network

Singh Mukesh Kumar1,a, Mishra Deepak Kumar 2,b, Parhi Dayal R.3,c and Singh Mahendra Prasad4,d

1Department of Mechanical Engineering,Govternment Engineering College ,Bilaspur (C.G.), India.

2Department of Mechanical Engineering, Ravishankar Institute of Technology & Management,Raipur (C.G.), India.

3Department of Mechanical Engineering,National Institute of Technology ,Rourkela (Orissa), India.

4Department of Mechanical Engineering, Priyadrashani College of Engineering, Nagpur (MS.), India.

a e-mail:mukesh3003@gmail.com , b e-mail:mishra4u@indiatimes.com, c e-mail:dayalparhi@yahoo.com, d e-mail:mpsingh3712@rediffmail.com

Keywords- Mobile robot, rule base network, back propagation algorithm.

Abstract— This paper is related to the human perception based idea by using heuristic information

for the navigation of mobile robots in cluttered dynamic environments which provides a general,

robust, safe and optimized path. The heuristic rule base network consists of a simple algorithm

which makes predefined estimation function very smaller. The estimation function should be

adequately defined for desired movement in the environments. A navigation system using rule

based technique that allows a mobile robot to travel in an environment about, which the robot has

no prior knowledge. This heuristic rule is applied in conjunction with artificial neural network. The

proposed intelligent controller provides an optimum trajectory which increases the effectiveness of

a mobile robot. A series of simulations test has been conducted to show the effectiveness of the

proposed algorithm.

Introduction

The current robot navigation systems require intelligent controller able to solve complex problems

under very uncertain and dynamic environmental situations. Navigation algorithms have been

investigated by many researchers [1-3]. Their methods are generally good for local navigation but

may not be optimal in a global sense. To navigate in an unknown or unstructured environment, it is

more desirable for the mobile robot to take intelligently decision based on its sensory information

[4]. In order to adapt the robot’s behavior to any complex, and dynamic environment without

further human intervention, it should be able to extract information from the environment

heuristically, to perceive, and act within the environment. As a result, designing of intelligent

controller of a mobile robot plays an important role in robotics and has thus attracted the attention

of researchers recently [5-7]. Existing approaches plan an initial path based on known information

about the environment, then modify the plan locally as the robot travels or reschedule the entire

path as the robot discovers obstacles with its sensors, sacrificing optimality or computational

efficiency, respectively [8].

Reactive obstacle avoidance is one of the most desirable characteristics of an autonomous mobile

robot. It is important for the robot to respond promptly to its surroundings, for instance, to avoid

unexpected obstacles and continue traveling toward the target [9]. A fuzzy context-dependent

blending of schemas has been proposed by Huq et al. [10]. Their comprehensive result eliminates

the drawbacks of motor schema approaches and provides collision free goal oriented robot

navigation. The Radio Frequency Identification technology algorithm is capable of reaching a target

point in its a priori unknown workspace without vision system, as well as tracking a desired

trajectory with a high precision [11]. The trajectory tracking and dynamic obstacle avoidance of a

Advanced Materials Research Vols. 403-408 (2012) pp 4777-4785Online available since 2011/Nov/29 at www.scientific.net© (2012) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMR.403-408.4777

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car-like mobile robot within distributed sensor-network spaces is developed by Hwang et al. [12]

and a sequence of experiments is carried out to confirm the performance of the proposed control

system. The general aim of automation is to avoid human interventions to control and to supervise

tasks. Ideally, it should be possible that robot can communicate with technical systems in a similar

way as they do with human assistants [13]. The speed function is proposed such that the minimum

cost path between the starting and target locations in the environment is the optimum planned path.

The speed function is controlled by one parameter, which takes one of three possible values to

generate the safest, the shortest, or the hybrid planned path [14]. The hybrid path is much safer than

the shortest path, but shorter than the safest one.

This paper introduces a control system for a mobile robot which provides heuristic learning

concretely. The method is simple and fast in execution using the perception based heuristic rule

concept. The algorithm computes the paths for the individual robots in the configuration-time space.

Thereby it trades off the distance to static objects as well as with other robots and the length of the

path to be traveled. Useful heuristic rules are hybridized with the ANN to build the desired mapping

between perception and motion. ANN consisting of four inputs the left obstacles distances

(Left_obs), right obstacles distances (Right_obs), front obstacles distances (Front_obs) and the

interim steering angle and an output of final steering angle. A series of simulation test has been

conducted to verify the effectiveness of the controller. In different simulation environment the

proposed approach is well suited to control the motions of a team of robots in a typical environment

and illustrate its advantages over other techniques developed so far.

PERCEPTION BASED HEURISTIC RULE

The heuristic rules are based on human perception (i.e. the working environment provides a fixed

referential frame for the rules). The sensor devices allow to measures the normal ambient light,

which is strongly influenced by the robot’s environment. Based on these sensors a human

perception based heuristic rule can be formulated. The methodology is a general, robust, and safer

which provides fast path planning framework for robotic navigation. If the target is located right

side then the robot will steer clockwise direction i.e. positive steering angle but it the target is

located left side then the robot will steer counter clock direction i.e negative. Human perception

based some of the heuristic rules based on Fig. 1(a) depicts the environment to avoid the obstacles

and motion control by the robots. Based on this scenario some formulated heuristic rules are listed

in Table 1. These parameters can be used to generate different perception based heuristic rules, for

example rule 1 is given below.

TABLE I. HUMAN PERCEPTION BASED HEURISTIC RULE FORMATION FOR OBSTACLE

AVOIDANCE FIG. 2(A)

R.N Left_obs

(mm)

Right_obs

(mm)

Front_obs

(mm)

Tar_ang

(Degree)

Steering angle

(Degree)

1 600 150 600 7 7

2 700 150 150 0 -80

3 515 150 550 90 50

4 420 150 440 53 53

Rule 1: If left_obs = 600 mm and right_obs = 150 mm and front_obs =600 mm and tar_ang = 7˚

Then Change in steering angle = 7˚

Similarly Fig. 1(b) illustrates the environment if the robot enters into U shaped wall, in such a

situation, the robot keep on heading towards the goal position. But when it moves towards the goal

position, it also comes closer to the obstacles. Any obstacle-avoidance behavior except wall-

following behavior would make the robot divert from its goal position. To avoid such situation

some of the rules are listed in Table 2. These parameters can be used to formulate heuristic rule for

wall following, for example rule 5 is given below.

4778 MEMS, NANO and Smart Systems

TABLE II. HUMAN PERCEPTION BASED HEURISTIC RULE FORMATION FOR WALL

FOLLOWING FIG. 2(B)

R.N. Left_obs

(mm)

Right_obs

(mm)

Front_ obs

(mm)

Tar_ang

(Degree)

Steering angle

(Degree)

5 680 1160 1220 13 13

6 780 835 200 0 77

7 80 1510 285 -112 90

8 155 1490 660 168 -90

9 250 530 1085 -117 90

10 235 515 No obstacle -54 -54

Rule 5: If left_obs = 680 mm and right_obs = 1160 mm and front_obs = 600 mm and tar_ang =

13˚ Then Change in steering angle = 13˚

(a) (b)

Figure 1. Human perception based rule formation (a).Rule formation for obstacle avoidance

(b).Rule formation for wall following.

The rules used for navigation of mobile robots are generated by induction from examples.

Approximately one thousand five hundred rules are fed into the induction program, within the

Clementine data ROBNAV software package. The examples present the situations encountered by a

robot while moving in a multi-robot, highly cluttered environment, and the actions that each robot

has to take to avoid colliding with other robots as well as with static obstacles.

BACK PROPAGATION ALGORITHMS

The network is trained to navigate by presenting it with 200 patterns representing typical scenarios,

some of which are depicted in Fig. 2. For example, Fig. 3(a) shows a robot advancing towards an

obstacle, another obstacle being on its right hand side. There are no obstacles to the left of the robot

and no target within sight. The neural network is trained to output a command to the robot to steer

towards its left. During training and during normal operation; the input patterns fed to the neural

network comprise the following components:

= Y }1{1 Left obstacle distance from the robot (1a)

= Y }1{2 Front obstacle distance from the robot (1b)

= Y }1{3 Right obstacle distance from the robot (1c)

= Y }1{4 Target bearing of the robot (1d)

Advanced Materials Research Vols. 403-408 4779

These input values are distributed to the hidden neurons which generate outputs given by

) f(V Y {lay}j

{lay}2 = (2)

where }1{lay-

i. {lay}

jii

{lay}

j YWV ∑= (3)

lay = layer number (2 or 3)

j = label for jth

neuron in hidden layer ‘lay’

i = label for ith

neuron in hidden layer ‘lay-1’

{lay}jiW = weight of the connection from neuron i in layer ‘lay-1’ to neuron j in layer ‘lay’

f(.) = activation function, chosen in this work as the hyperbolic tangent function

xx

xx

ee

ee f(x)

−

−

+

−= (4)

During training, the network output actual may differ from the desired output θdesired as specified

in the training pattern presented to the network. A measure of the performance of the network is the

instantaneous sum-squared difference between θdesired and θactual for the set of presented training

patterns

2actualdesired

sng patternall traini

rr )θ(θ2

1 E −= ∑ (5)

Left_obs

Interim

Steering

Angle

Final

Steering

Angle

Front_obs

Right_obs

Rule Layer

IF…THEN…

Left_obs

Right_obs

Front_obs

Tar_ang

Input

Layer

Output

First hidden layer

Second

hidden

layer

Figure 2. Four-layer heuristic rule neural network for robot navigation

4780 MEMS, NANO and Smart Systems

Robot

Target

Robot

Robot Robot Robot

Robot

Robot

Robot Robot Robot

Robot Robot Robot

Target

Target Target

(a)

(j)

(i) (h) (g)

(f) (e) (d)

(c) (b)

(l) (k)

(o) (n) (m

)

Robot

Robot

Figure 3. Some example of training patterns

The error back propagation method is employed to train the network [15]. This method requires

the computation of local error gradients in order to determine appropriate weight corrections to

reduce Err. For the output layer, the error gradient "{4} is

)θ) (θ (V f δ actualdesired}4{

1'}4{ −= (6)

The local gradient for neurons in hidden layer {lay} is given by

( )}1{laykj

}1{lay

kk

{lay}j

'{lay}j Wδ ) (V f δ ++

∑= (7)

The synaptic weights are updated according to the following expressions

1)(t∆W (t) W 1)(tW ijijij ++=+ (8)

and 1}{lay

i{lay}jijij Y∆ηδ (t)α∆W 1)(t∆W −+=+ (9)

where α = momentum coefficient (chosen empirically as 0.2 in this work)

η = learning rate (chosen empirically as 0.35 in this work) t = iteration number, each iteration consisting of the presentation of a training pattern and

correction of the weights. The final output from the neural network is

)f(V θ 41actual = (10)

where }3{

i}4{

i1i

41 YW V ∑= (11)

It should be noted learning can take place continuously even during normal target seeking

behaviour. This enables the controller to adopt the changes in the robot’s path while moving

towards target.

Advanced Materials Research Vols. 403-408 4781

Simulation results and discussions

The series of simulations results have been obtained by using ROBNAV software has been

developed using C++ [16]. To demonstrate the effectiveness and the robustness of the proposed

method, simulation results on mobile robot navigation in various environments are exhibited.

The first priority of the robot is to avoid static as well as dynamic obstacle. When arrays of

sensor receive the information of object (which is too close to the robot), it avoids a collision by

moving away from it in the opposite direction. Collision avoidance has the highest priority for

mobile robot. And therefore, it can override other behaviors; in this case, the obstacle avoidance

behavior is activated when the readings from any sensors are less than the minimum threshold

values as shown in Fig. 4(a). Another special condition appears as the mobile robot detects an

obstacle in the front while the target tracking control mode is on operation. In this case, the fixed

wall following behavior should be performed first, that is, the mobile robot must rotate clockwise or

counterclockwise such that it can align and move along the wall shown in Fig. 4(b). When the

acquired information from the sensors shows that there are no obstacles around robot, its main

reactive behavior is target steer. Rule based intelligent controller mainly adjusts robots motion

direction and quickly moves it towards the target if there are no obstacles around the robot as shown

in Fig. 5(a).

The results from the proposed method for real time navigation of mobile robot have been

compared with the result from fuzzy controller by Pradhan et al. [7]shown in Fig. 5(a). During

comparisons it has been found that the developed rule based controller has performed better than

the fuzzy controller in terms of path and time optimization as shown in Fig. 5(b).

Obstacles

Figure 4. Simulation result of perception based heuristic rule network (a). Simulation result of

obstacle avoidance (b). Simulation result of wall following.

(b) (a)

Figure 5. Simulation results path comparisons of mobile robot using different controller (a)

Fuzzy controller by Pradhan et al. [7] and (b) Proposed heuristic rule network base controller

4782 MEMS, NANO and Smart Systems

(a)

(b)

Figure 6. Simulation results of Ayari et al. [17] collision free goal reaching in learned

environment (b). Simulation results of proposed method collision free goal reaching.

Table 3 shows a comparison between fuzzy, and proposed rule based network approach. From

table 3 the path traveled by robot using fuzzy controller by Pradhan et al. [7] is 13840 mm and time

taken is 14.67 sec. to reach the target, and whereas the proposed intelligent controller based on

heuristic rule network provides total travel path length 5623 mm and time taken is 5.96 sec. only. A

comparison has also been done between the results obtained by Ayari et al. [17] for navigation of

mobile robot in collision free goal reaching in learned environment and the results from the

developed heuristic rule based intelligent controller (Fig. 6). The effectiveness of the developed

controller has been tested in the series of simulation test and they are in good agreement.

TABLE III. RESULTS COMPARISONS WITH DIFFERENT CONTROLLER.

SN Navigation

observations

Comparisons with various approaches

Fuzzy controller by

Pradhan et al.[7] (Fig.

5(a))

Proposed perception based

heuristic rule network

controller (Fig. 5(a))

1 Length of path (in mm) 13840 5623

2 Time taken (seconds) 14.67 5.96

3 Percentage of result

deviation with proposed

approach (%)

46.13% ---

Advanced Materials Research Vols. 403-408 4783

Conclusions

From the theoretical and simulations analyses the methodology is a general, robust, and safest

which provides fast path planning framework for robotic navigation using human perception based

heuristic rule base network controller. The method is simple but efficient tool for mobile robot

navigation, especially in a real world dynamic environment. The proposed methodology is

successfully applied for navigation in dynamic as well as static environments. The robot rapidly

recognizes their surroundings which provide sufficient information for path optimization during

navigation. The training patterns of ANN can be generated by simulation rather than by

experiments, saving considerable time and effort. Comparison of results between the current

developed and with other techniques have shown good agreement. This validates the authenticity of

the proposed technique. This paper depicts a methodology to achieve the co-operation navigation in

pragmatic way.

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Advanced Materials Research Vols. 403-408 4785

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