Research of path planning based on adaptive dynamic programming for bio-mimetic robot fish

In robot path planning, artificial potential field (APF) is used to describe complex environment information. This paper proposes an artificial potential field-based adaptive dynamic programming (APFADP) method and applies it to the bio-mimetic robot fish path planning. In ADP, according to Bellman optimal theory, system cost is conventionally used to present control action cost. In APFADP, a novel potential field is defined according to system cost which is based on APF and the description of environment information can be given through the learning process. In the proposed method, we use action-dependent heuristic dynamic programming (ADHDP) that consists of two neural networks: the critic network and action network. The critic network is designed to approximate system cost by learning from position variables and angle between robot fish movement and target. The action network is designed as a controller to find the optimal path, which produces control outputs by learning from position variables. Verification has been conducted to illustrate the good performance of the proposed method by experiment results on bio-mimetic robot fish path planning.