Distributed Swarm Trajectory Planning for Autonomous Surface Vehicles in Complex Sea Environments

Abstract

In this paper, a swarm trajectory-planning method is proposed for multiple autonomous surface vehicles (ASVs) in an unknown and obstacle-rich environment. Specifically, based on the point cloud information of the surrounding environment obtained from local sensors, a kinodynamic path-searching method is used to generate a series of waypoints in the discretized control space at first. Next, after fitting B-spline curves to the obtained waypoints, a nonlinear optimization problem is formulated to optimize the B-spline curves based on gradient-based local planning. Finally, a numerical optimization method is used to solve the optimization problems in real time to obtain collision-free, smooth and dynamically feasible trajectories relying on a shared network. The simulation results demonstrate the effectiveness and efficiency of the proposed swarm trajectory-planning method for a network of ASVs.

Local Path Planning Method for Unmanned Ship Based on Encounter Situation Inference and COLREGS Constraints

“…The algorithm's benefits in terms of the path length, number of turns, units, and coverage were experimentally verified. A method for multi-ship swarm path planning based on local sensor information was proposed by Wang et al [22], achieving collision-free, smooth, and dynamically feasible path generation through dynamic path search and B-spline curve optimization. Yang et al [23] improved the A* algorithm based on the artificial potential field method to consider the influence of ocean currents and static obstacles on path planning, and smoothed the generated paths.…”