Adaptive artificial potential field method for small autonomous vehicles

Artificial Potential Field (APF) is one of the path planning and obstacle avoidance methods used for its simplicity and effectiveness. The goal’s attractive force and the obstacles’ repulsive forces are modeled and considered to act upon the vehicle or robot. However, in practice, the classical APF faces challenges like local minima. We propose to study several enhancements and their combinations in order to show how they can create even more efficient algorithms to overcome these challenges. Those enhancements include tangential force, inertia-inspired force, and a local minima detection (l.m.d.) and reaction scheme by adding virtual obstacles and dynamically changing coefficients. All of them keep the methods as lightweight as possible while improving the classical APF. The tangential force provides smoother paths and avoids local minima cases. The dynamic change of APF’s parameters, coupled with the addition of virtual obstacles when detecting local minima, provides an efficient way to escape them. The inertia-inspired force can be used to smooth the trajectory when only obstacles in front of the vehicle are taken into account. We defined performance metrics to assess the path completion, path quality, and processing time to compare the proposed enhancements with the base case of classical APF. We benchmarked the proposed methods in different environments for a holonomic robot and a simplified bicycle. The proposed adaptive APF with inertial force extension completed 87.5% of the tests while the classical APF completed only 43.8% of them. On the other side, tangential versions of APF reduce the path length deviation by 8% and the curvature by 20% in simple cases. The code is available on: https://github.com/Glawal/APFproject/tree/paper1.