Abstract
Dealing with uncertainties along with high-efficiency planning for task assignment problem is still challenging, especially for multi-agent systems. In this paper, two frameworks—Compromise View model and the Nearest-Neighbour Search model—are analyzed and compared for co-operative path planning combined with task assignment of a multi-agent system in dynamic environments. Both frameworks are capable of dynamically controlling a number of autonomous agents to accomplish multiple tasks at different locations. Furthermore, these two models are capable of dealing with dynamically changing environments. In both approaches, the Particle Swarm Optimization-based method is applied for path planning. The path planning approach combined with the obstacle avoidance strategy is integrated with the task assignment problem. In one framework, the Compromise View model is used for completing the tasks and a combination of clustering method with the Nearest-Neighbour Search model is used to assign tasks to the other framework. The frameworks are compared in terms of computational time and the resulting path length. Results indicate that the Nearest-Neighbour Search model is much faster than the Compromise View model. However, the Nearest-Neighbour Search model generates longer paths to accomplish the mission. By following the Nearest-Neighbour Search approach, agents can successfully accomplish their mission, even under uncertainties such as malfunction of individual agents. The Nearest-Neighbour Search framework is highly effective due to its reactive structure. As per requirements, to save time, after completing its own tasks, one agent can complete the remaining tasks of other agents. The simulation results show that the Nearest-Neighbour Search model is an effective and robust way of solving co-operative path planning combined with task assignment problems.
Highlights
There is an increasing trend towards using autonomous systems cross a wide range of real-world applications where human presence is deemed unnecessary or dangerous
The simulation results show that the Nearest-Neighbour Search model is an effective and robust way of solving co-operative path planning combined with task assignment problems
The extension of the work in [10] is developed in this paper. Both frameworks are based on the Particle Swarm Optimization (PSO) method and are used for efficient cooperative path planning for a multi-agent system
Summary
There is an increasing trend towards using autonomous systems cross a wide range of real-world applications where human presence is deemed unnecessary or dangerous. As the mission becomes more complicated, they may need to accomplish multiple tasks in dynamic environments. An effective cooperative path planning with task assignment should be needed to make the mission successful. The extension of the work in [10] is developed in this paper Both frameworks are based on the Particle Swarm Optimization (PSO) method and are used for efficient cooperative path planning for a multi-agent system. In terms of path planning, it leverages the advantages of the heuristic and random search strategies used in PSO Both frameworks can efficiently work in complex environments. Simultaneous replanning is applied to replan a new path by avoiding both static and dynamic obstacles Both approaches are capable of avoiding collisions among agents.
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