An intelligent and cooperative collision avoidance method is proposed based on a combination of enhanced potential fields and fuzzy inference systems (FIS), where a genetic algorithm is used to optimize the FISs. The proposed approach provides a near-optimal and collision-free path in an environment with static and dynamic obstacles taking into consideration potential uncertainties. Furthermore, it is able to resolve local minima and goal non-reachable with obstacles nearby issues that exist in the traditional artificial potential field approach with minimum computational burden. A simple scenario modeled based on the issues faced by UAVs in such environments is used for training the system, and a complex scenario containing a number of dynamic UAVs in the presence of static obstacles is considered to validate the performance of the proposed approach.
Decentralized Collision Avoidance via Fuzzy Potential Fields
16.08.2021
1847612 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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