Adaptive Path Planning for Multi-Agent Systems Using Improved Artificial Potential Field with Neural Network Approximation.

This paper studies obstacle and collision avoidance strategies for nonlinear second-order multi-agent systems (MAS) formation control. Due to the uncertainties and complexities in nonlinear systems, including external disturbances and communication delays, radial basis function (RBF) neural network control is employed to address the control requirements of nonlinear terms in the system. In addition, as traditional artificial potential fields (APF) based obstacle avoidance algorithms have limitations, this paper applies an improved APF algorithm for collision avoidance and obstacle avoidance in multi-agent systems formation control. The stability and feasibility of the proposed approach are proved based on the Lyapunov stability theory. Simulation experiments further validate the effectiveness of the formation control strategy. [ABSTRACT FROM AUTHOR]