A comparison of obstacle dependant Gaussian and hybrid potential field methods for collision avoidance in multi-agent systems
Fuat Candan, Yonggang Peng, Lyudmila S. Mihaylova · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2021
In this paper collision avoidance methods - the velocity obstacle, obstacle dependant Gaussian potential field and hybrid potential field methods, are compared and tested on multiagent systems. Extensive evaluation is presented with a number of case studies with a different number of agents, with static and dynamic and obstacles. The advantages and disadvantages of each method are discussed. The optimisation of the static and dynamic coefficients of the hybrid potential field method is performed via a genetic algorithm. The results from the tests are from 1000 independent runs and show that the hybrid potential field method can avoid reliably collisions in multi-agent systems.