Rapid Path Planning for Dubins Vehicles under Environmental Currents

TL;DR

This paper introduces a fast, real-time path planning method for Dubins vehicles in environments with currents, using only two analytically solvable path types extended to ensure full reachability and lower travel times.

Contribution

It proposes a novel approach that simplifies Dubins path planning under currents by using extended LSL and RSR paths, enabling real-time solutions with guaranteed reachability.

Findings

Achieves real-time path planning with reduced computation time.

Guarantees full reachability of any goal pose.

Produces paths with lower time costs compared to traditional methods.

Abstract

This paper presents a rapid (real time) solution to the minimum-time path planning problem for Dubins vehicles under environmental currents (wind or ocean currents). Real-time solutions are essential in time-critical situations (such as replanning under dynamically changing environments or tracking fast moving targets). Typically, Dubins problem requires to solve for six path types; however, due to the presence of currents, four of these path types require to solve the root-finding problem involving transcendental functions. Thus, the existing methods result in high computation times and their applicability for real-time applications is limited. In this regard, in order to obtain a real-time solution, this paper proposes a novel approach where only a subset of two Dubins path types (LSL and RSR) are used which have direct analytical solutions in the presence of currents. However, these two path types do not provide full reachability. We show that by extending the feasible range of circular arcs in the LSL and RSR path types from $2\pi$ to $4\pi$: 1) full reachability of any goal pose is guaranteed, and 2) paths with lower time costs as compared to the corresponding $2\pi$-arc paths can be produced. Theoretical properties are rigorously established, supported by several examples, and evaluated in comparison to the Dubins solutions by extensive Monte-Carlo simulations.