Re-Active Vector Equilibrium: A Novel Method of Autonomous Vehicle Navigation using Artificial Potential Fields

Abstract

The use of potential field based navigation schemes in robotics has been limited by inherent local minima issues. Local minima traps, small passages, unstable motion, and targets positioned near objects all pose major concerns when using potential fields for local vehicle control. This work proposes a new algorithm, "Re-Active Vector Equilibrium" (RAVE) that mitigates many of these issues. The vehicle representation model is expanded to use multiple points subject to potential calculation and the addition of two forces, a velocity dependent "risk force" (F_rsk) and a velocity and direction dependent "tangential force" (F_tan). The vehicle representation model is also expanded from a single reactive point to a series of points that define the vehicle body, providing better and simpler vehicle control. This has the effect of simplifying the required calculations at the cost of increasing the calculation count. The risk force, F_rsk, allows for dynamic adaptation to the immediate environment by acting in opposition to the net obstacle force, and is inversely proportional to the vehicle speed. The tangential force, F_tan, encourages better wall-following behaviour and provides a biasing mechanism to resolve obstacle aligned with target local minima issues.