Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field

Artificial potential field has long been proposed in the field of robot path planning. But the well-known drawbacks like local minimal problem and low efficiency prevent its wide application. In this paper, we propose a particle swarm optimized tangent vector based artificial potential field path planning algorithm (PSO-TVAPF) to solve those problems. A tangent vector based on obstacles' information is added into artificial potential field (APF) model as an auxiliary force for obstacle avoiding process. This makes the original model, tangent vector based artificial potential field (TVAPF). To achieve more intelligent and efficient TVAPF, map and path information are taking into consideration dynamically while calculating tangent vector. In addition, particle swarm optimization has been used to refine TVAPF, which leads to the final model named PSO-TVAPF. Simulation experiments and physical validation results indicate that the proposed algorithm can overcome classic APF's drawbacks and improve path planning efficiency significantly.