Planning is an essential element of autonomous systems. This work presents a dynamic path planning algorithm for an unmanned autonomous vehicle to execute a set of assigned tasks in a changing environment. This problem comprises path planning and task sequencing. The approach adopted here is to solve these subproblems simultaneously using an evolutionary planning algorithm and a stochastic model of the environment. During the mission, the planner replans and adapts the path in response to changes in the environment. Simulation results demonstrate that the path planning algorithm can compute feasible effective solutions to path planning problems. These include planning with timing constraints and dynamic planning with moving targets and obstacles. The vehicle is able to autonomously travel from the initial location to the goal location while avoiding obstacles and performing the assigned tasks.
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Pongpunwattana, A., Rysdyk, R. (2007). Evolution-based Dynamic Path Planning for Autonomous Vehicles. In: Chahl, J.S., Jain, L.C., Mizutani, A., Sato-Ilic, M. (eds) Innovations in Intelligent Machines - 1. Studies in Computational Intelligence, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72696-8_5
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