NEPTUNE: Nonentangling Trajectory Planning for Multiple Tethered Unmanned Vehicles

TL;DR

This paper introduces NEPTUNE, a comprehensive method for planning trajectories for multiple tethered robots that avoids entanglements and collisions, using a novel homotopy representation and decentralized online planning, validated through simulations and real UAV experiments.

Contribution

It presents a new multi-robot tether-aware homotopy representation and a decentralized planning framework for entanglement-free trajectories, advancing the state-of-the-art in tethered robot navigation.

Findings

Effective entanglement prevention in simulations with up to 8 UAVs

Real-time trajectory planning demonstrated on 3 tethered UAVs

Outperforms existing methods in efficiency and safety evaluations

Abstract

Despite recent progress on trajectory planning of multiple robots and path planning of a single tethered robot, planning of multiple tethered robots to reach their individual targets without entanglements remains a challenging problem. In this paper, we present a complete approach to address this problem. Firstly, we propose a multi-robot tether-aware representation of homotopy, using which we can efficiently evaluate the feasibility and safety of a potential path in terms of (1) the cable length required to reach a target following the path, and (2) the risk of entanglements with the cables of other robots. Then, the proposed representation is applied in a decentralized and online planning framework that includes a graph-based kinodynamic trajectory finder and an optimization-based trajectory refinement, to generate entanglement-free, collision-free and dynamically feasible trajectories. The efficiency of the proposed homotopy representation is compared against existing single and multiple tethered robot planning approaches. Simulations with up to 8 UAVs show the effectiveness of the approach in entanglement prevention and its real-time capabilities. Flight experiments using 3 tethered UAVs verify the practicality of the presented approach.