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Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

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Front. Mech. Eng.    2014, Vol. 9 Issue (4) : 317-330    https://doi.org/10.1007/s11465-014-0304-z
RESEARCH ARTICLE
A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm
Prases K. MOHANTY(),Dayal R. PARHI
Robotics Laboratory, National Institute of Technology, Rourkela, 769008, India
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Abstract

Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.
Keywords mobile robot      obstacle avoidance      Invasive Weed Optimization      navigation     
Corresponding Author(s): Prases K. MOHANTY   
Online First Date: 14 August 2014    Issue Date: 19 December 2014
 Cite this article:   
Prases K. MOHANTY,Dayal R. PARHI. A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm[J]. Front. Mech. Eng., 2014, 9(4): 317-330.
 URL:  
https://academic.hep.com.cn/fme/EN/10.1007/s11465-014-0304-z
https://academic.hep.com.cn/fme/EN/Y2014/V9/I4/317
Fig.1  Seed production procedure in a colony of weeds
Fig.2  Pseudo code for IWO algorithm
Fig.3  Flow chart for mobile robot navigation based on IWO algorithm
Fig.4  Obstacle avoidance behavior and target seeking by single robot using IWO algorithm
Fig.5  Target seeking by single robot using IWO algorithm
Fig.6  Wall following behavior by single robot using IWO algorithm
Fig.7  Single robot escaping from a trap situation using IWO algorithm
Fig.8  Single robot navigating in a cluttered environment using IWO algorithm
Fig.9  Path generated for single robot by varying the parameters of IWO algorithm
Symbol Quantity Value
No. Number of initial populations 20
itermax Maximum number of iterations 300
pmax Maximum number of plant populations 30
Smax Maximum number of seeds 10
Smin Minimum number of seeds 0
n Non-linear modular index 3
σinitial Initial value of standard deviation 4
σfinal Final value of standard deviation 0.01
α Controlling parameter-1 1
β Controlling parameter-2 1×10-6
Tab.1  Details of parameter values used in IWO algorithm
Fig.10  Path genrated for single robot using BFO
Fig.11  Path generated for single robot using current investigation compared with Fig. 10
Fig.12  Path generated for single robot using ACO
Fig.13  Path generated for single robot using current investigation compared with Fig. 12
Fig.14  Path generated for single robot using GA
Fig.15  Path generated for single robot using current investigation compared with Fig. 14
Figure Path length coveredby the robot/Pixcel Methods used for navigation
10 192.21 BFO
11 171.46 IWO
12 174.81 ACO
13 169.07 IWO
14 174.51 GA
15 159.55 IWO
Tab.2  Comparison of simulation results in terms of path length
Fig.16  Khepra-III mobile robot with specifications
Component Specification
Processor DsPIC 30F5011 at 60 MHz
RAM 4 KB on DsPIC
Speed Max 0.5 m/s
Sensors 10 Infra-red proximity and ambient light sensors with up to 30 cm range, 5 Ultrasonic sensors with range 20 cm to 4 m
Power Power Adapter Swapable Lithium-Polymer battery pack (1350 mA·h)
SizeDiameter 130 mm height70 mm
Weight Approx. 690 g
Payload Approx. 2000 g
Tab.3  Specification of the robot
Fig.17  Experimental set up for navigation of mobile robot in the similar environment shown in Fig. 11
Fig.18  Experimental set up for navigation of mobile robot in the similar environment shown in Fig. 13
Fig.19  Experimental set up for navigation of mobile robot in the similar environment shown in Fig. 15
SL. No. Path length in simulation/m Path length in real time experiment/s Error/%
Scenario-1 1.64 (Fig. 11) 1.71 (Fig. 17) 4.26
Scenario-2 1.61 (Fig. 13) 1.68 (Fig. 18) 4.10
Scenario-3 1.56 (Fig. 15) 1.62 (Fig. 19) 3.70
Tab.4  Path length covers by robot in simulation and experiment to reach target
SL. No. Time taken by the robot in simulation/s Time taken by the robot in real time experiment/s Error/%
Scenario-1 8.20 (Fig. 11) 8.55 (Fig.17) 4.26
Scenario-2 8.05 (Fig. 13) 8.40 (Fig.18) 4.10
Scenario-3 7.80 (Fig. 15) 8.10 (Fig.19) 3.70
Tab.5  Time taken by robot in simulation and experiment to reach target
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