Task Allocation and Path Planning of a Multi-Robot System Using Heuristic Coupled Particle Swarm Optimization Algorithm

Task Allocation and Path Planning of a Multi-Robot System Using Heuristic Coupled Particle Swarm Optimization Algorithm

Arindam Majumder (National Institute of Technology, Agartala, India) and Rajib Ghosh (National Institute of Technology, Agartala, India)
DOI: 10.4018/978-1-7998-1831-1.ch009
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This study deals with a plant layout where there were ninety predefined locations which have to be inspected by using three multiple robots in such a way that there would not be any collisions between the robots. A heuristic integrated multiobjective particle swarm optimization algorithm (HPSO) is developed for allocating tasks to each robot and planning of path while moving from one task location to another. For optimal path planning of each robot the research utilized A* algorithm. The task allocation for each robot is carried out using a modified multiobjective particle swarm optimization algorithm where the earliest completion time (ECT) inspired technique is used to make the algorithm applicable in multi robot task allocation problems. At the later stage of this study, in order to test the capability of HPSO an instance is solved by the algorithm and is compared with the existing solutions of a genetic algorithm with the A* algorithm. The computational results showed the superiority of the proposed algorithm over existing algorithms.
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Multi robot system is a group of robots working together and which can coordinate and communicate to each other to perform certain task through co-operation. With the use of multi robot system we can perform several tasks continuously that couldn’t be possible by using a single manipulator. For example, with an appropriate design of control and coordination multiple robot manipulators could move a long or heavy object, no matter how capable or advance a single mono robot system is it has always limitation on many aspects. Some task requires the use of tool that can handle two or more robots in coordination in these cases the use of these tools may be possible because of the rigidity that provides by the multi robot system. Multi robot system can be either centralized one or de centralized one. In centralized multi robot system control is done by using central computer every agent maintains a connection to the central agent. So, there is a supervisory control by the computer over the system whereas in de centralized multi robot system there is no central connection between the agents to the computer. For the following benefits of the MRS this topic has become more popular among the researchers. Some task which are quite complex for a single to do can be easily be done by multi robot system, some complex task can be of distributed nature or the diversity in terms different require requirements. The time taken to complete the task can be reduced if multiple robots cooperates together par alley. It increases the system reliability because using single robot may work as a bottleneck during critical conditions. But when we implement multiple robots doing a similar task if one gets fails the task can be easily completed by using others. Having a single powerful robot will be complex in design and costlier too use than small simple multiple robots. Thus, multi robot systems have been used in various missions like Distributed controllers for the deployment of mobile sensor networks, agile coordinated mobile robot control, multi robot control with adversaries and environment hazards, persistent monitoring persistent environmental sampling with robots, information based active sensing estimation, multi robot manipulation, path planning of multiple AUV’s under marginal acoustic communication condition, detection and tracking any object in different unwanted environment conditions, surveillance, reconnaissance, and continuous area sweeping. A numerous number of problems can be solved by deploying and developing multi robot systems (MRS) in real world applications. Davidson and Murry (1998) proposed a fully automated mobile robot system which can detect, store and track suitable landmark features during goal directed navigation. Liu and Bucknlal (2018) introduced multi unmanned surface vehicle for carrying out dangerous missions such as the maritime portal, protection in hazardous waste environments and rescue in post-disaster scenarios. Caloud et al. (1990) implemented multi robot systems for transportation of objects, performing operations, detection of hazard, maintenance and cleaning in indoor environments. Parker (1994) showed the applicability of multi robot system for cleaning up of hazardous waste situated in different locations. Ju and Son (2018) introduced multi aerial robot system for farming application. The other problems to be solved through MRS include, task allocation, group formation, cooperative object detection and tracking, communication relaying and self-organization.

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