Task allocation and collision-free path planning of centralized multi-robots system for industrial plant inspection using heuristic methods

https://doi.org/10.1016/j.robot.2016.02.003Get rights and content

Highlights

  • Centralized multi-robots system for industrial plant inspection.

  • Deals with task allocation and collision-free path planning.

  • Optimization problem.

  • Task allocation problem is solved using genetic algorithm.

  • Path planning problem is solved using A* algorithm.

Abstract

Multi-robots systems have been effectively employed in various application domains. This study aimed at developing some heuristic methods for the task allocation and collision-free path planning for three robots working in the common workspace. In an application domain, there were ninety fixed locations in a plant, which were to be inspected by three robots after traveling through the minimum distance. Moreover, overall task completion time was to be as minimum as possible. A genetic algorithm (GA) had been used for the task allocation, and A* algorithm was utilized for path planning. The previous work on the same problem (Liu and Kroll, 2012) did not address the issue of collision avoidance in detail, which had been attempted in this study. Results of this study were found to be better than those of the previous work (Liu and Kroll, 2012). It could happen so, due to the reason that the GA was utilized in this study not only to schedule the tasks but also to assign optimal number of tasks to each robot. Thus, more environmental conditions were encoded in the GA-string.

Introduction

In a multi-robots system, multiple robots share the common workspace to perform assigned task(s), which could be difficult to do for a single robot efficiently. A multi-robots system could be either a centralized or a decentralized one. In a centralized multi-robots system, control is done using a central computer, whereas there is no supervisory control in a de-centralized multi-robots system. Multi-robots systems had been used to solve a variety of problems, some of which are discussed in the next section. The present study deals with a centralized multi-robots system.

The problem of task allocation deals with assigning the tasks to multiple robots working in the common workspace. Finding an optimal allocation of tasks is an NP-hard problem. Hence, these kinds of problems could be solved using heuristic search methods.

Section snippets

Literature review

Multi-robots systems had been utilized to tackle a variety of problems. Some of those problems are discussed here. Meng and Gan  [1] investigated on decentralized coordination for multi-robot system used for cleaning up hazardous waste in dynamic environment. Their approach could achieve good levels of efficiency and robustness. Chakraborty et al.  [2] formulated the box-pushing problem using two robots as a multi-objective optimization one and presented Pareto-optimal front of solutions by

Tools and techniques used

In this study, A algorithm and GA had been used for path planning and task allocation, respectively, whose working principles are briefly discussed below.

Mathematical formulation of the problem

The following assumptions are made:

  • Each robot can execute only one task at a time.

  • Only one robot is required to execute each task.

  • Each task is executed only once.

  • All the tasks are to be executed.

  • All the robots start from the depots at the same time.

Let us consider an optimization problem, where a group of m robots R={R1,R2,,Ri,,Rm} are to be assigned inspection task at n locations T={T1,T2,,Tj,,Tn} in an optimal sense, such that either the completion time (that is, maximum of the

Developed algorithm

For solving the task allocation and path planning problems in centralized multi-robot systems, the programming was done in MATLAB. The developed algorithm consists of three components, namely environment representation, task allocation and path planning. The task allocation was done using the GA, and A algorithm helped in path planning. Task allocation would comprise of the sequence of tasks and the number of tasks allocated to each robot. Either total fuel consumption or completion time had

Results and discussion

The performance of a GA depends on the proper balance between its exploration and exploitation, and to ensure it, a thorough GA-parametric study had been carried out by changing one parameter at a time and keeping the others fixed. The following parameters were considered during this study:

  • pc1:

    Probability of Partially-Mapped Crossover used for the integer-coded portion of the string,

  • pc2:

    Probability of Uniform Crossover utilized for the binary-coded portion of the string,

  • pm1:

    Probability of Mutation used

Concluding remarks

Task allocation and collision-free path planning of multi-robot system had been dealt in this study, where A algorithm was used for path planning and a GA was utilized for task allocation. The algorithm used in this study could produce better results compared to that of the previous one. Besides determining the schedule, the GA took the responsibility of assigning the optimal number of tasks to each inspecting robot. Optimal paths for minimum time of completion and minimum fuel consumption

Kelin Jose received his M.Tech. in 2014 from IIT Kharagpur, India. He has special interest in optimization, soft computing and manufacturing science. He is now working in industry.

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Kelin Jose received his M.Tech. in 2014 from IIT Kharagpur, India. He has special interest in optimization, soft computing and manufacturing science. He is now working in industry.

Dilip Kumar Pratihar received his B.E. (Hons.) and M.Tech. from REC (NIT) Durgapur, India, in 1988 and 1994, respectively. He obtained his Ph.D. from IIT Kanpur, India in 2000. He received University Gold Medal, A.M. Das Memorial Medal, Institution of Engineers’ (I) Medal, and others. He completed his post-doctoral studies in Japan and then in Germany under the Alexander von Humboldt Fellowship Programme. He is working as a Professor of IIT Kharagpur, India. His research areas include robotics, soft computing and manufacturing science. He has published more than 170 papers, mostly in various international journals. He has written a textbook on “Soft Computing”, co-authored another textbook on “Analytical Engineering Mechanics”, edited a book on “Intelligent and Autonomous Systems”, co-authored reference books on “Modeling and Analysis of Six-legged Robots” and “Modeling and Simulations of Robotic Systems Using Soft Computing”. Recently, he has published another textbook named “Soft Computing: Fundamentals and Applications”. He has guided 15 Ph.D.s. He is in editorial board of 15 International Journals. He has been elected as FIE and MIEEE.

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