Abstract
In this research, the path planning and obstacle avoidance problem for Swarm Robotics is addressed. Our contribution consists in a Discrete adaptation of the well-known Input-Space Sampling method (DISS). The DISS is integrated into a Swarm Intelligence approach, named Multi-Bee Swarm Optimization (MBSO) that must be embedded into autonomous mobile robots. The MBSO algorithm, in turn, is multi-swarm adaptation of the well-known Bee Swarm Optimization. It aims to resolve the Target Detection Problem by exploring a 2D discrete, complex, and unknown environment. Thus, the search strategy must be provided with an adequate path planning and obstacles avoidance strategy which is a determinant factor to the robots’ efficiency in their search mission. The experimental results show that, by varying the obstacle density in the search environments MBSO’s performances are not much affected. Our path planning and obstacle avoidance strategy offer an efficient and effective navigation capability to the robots of MBSO.
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Houacine, N.A., Drias, H. (2023). DISS: A Discrete Input-Space Sampling Path Planning and Obstacle Avoidance Strategy for Swarm Robotics. In: Drias, H., Yalaoui, F., Hadjali, A. (eds) Artificial Intelligence Doctoral Symposium. AID 2022. Communications in Computer and Information Science, vol 1852. Springer, Singapore. https://doi.org/10.1007/978-981-99-4484-2_12
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