Task allocation and collision-free path planning of centralized multi-robots system for industrial plant inspection using heuristic methods
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, 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:
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Each robot can execute only one task at a time.
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Only one robot is required to execute each task.
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Each task is executed only once.
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All the tasks are to be executed.
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All the robots start from the depots at the same time.
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 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:
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Probability of Partially-Mapped Crossover used for the integer-coded portion of the string,
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Probability of Uniform Crossover utilized for the binary-coded portion of the string,
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Probability of Mutation used
Concluding remarks
Task allocation and collision-free path planning of multi-robot system had been dealt in this study, where 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.