Motion Planning With Gamma-Harmonic Potential Fields

TL;DR

This paper introduces gamma-harmonic potential fields (GHPF), an extension of harmonic potential fields for robot motion planning in complex, cluttered environments with probabilistic workspace descriptors, ensuring safe and goal-directed navigation.

Contribution

It presents a novel GHPF method that incorporates probabilistic workspace descriptors and physical analogies to improve planning in non-segmented, cluttered environments.

Findings

Successfully avoids threat regions during navigation

Proven convergence to the goal in simulations

Adaptable to environments with vector drift fields

Abstract

This paper extends the capabilities of the harmonic potential field (HPF) approach to planning. The extension covers the situation where the workspace of a robot cannot be segmented into geometrical subregions where each region has an attribute of its own. The suggested approach uses a task-centered, probabilistic descriptor of the workspace as an input to the planner. This descriptor is processed, along with a goal point, to yield the navigation policy needed to steer the agent from any point in its workspace to the target. The approach is easily adaptable to planning in a cluttered environment containing a vector drift field. The extension of the HPF approach is based on the physical analogy with an electric current flowing in a nonhomogeneous conducting medium. The resulting potential field is known as the gamma-harmonic potential (GHPF). Proofs of the ability of the modified approach to avoid zero-probability (definite threat) regions and to converge to the goal are provided. The capabilities of the planer are demonstrated using simulation.