Trajectory Planning for a Multi-UAV Rigid-Payload Cascaded Transportation System Based on Enhanced Tube-RRT*

TL;DR

This paper introduces an enhanced two-stage trajectory planning framework for multi-UAV systems with rigid payloads, improving safety, efficiency, and smoothness in cluttered environments through novel algorithms and control schemes.

Contribution

The paper develops an Enhanced Tube-RRT* algorithm with hybrid sampling and adaptive expansion, and integrates a convex quadratic program for payload trajectory optimization in dense environments.

Findings

Enhanced Tube-RRT* outperforms existing methods in success and sampling rates.

The optimized payload trajectory is smooth, collision-free, and respects dynamic constraints.

Simulation validates the effectiveness of the planning and control framework.

Abstract

This paper presents a two-stage trajectory planning framework for a multi-UAV rigid-payload cascaded transportation system, aiming to address planning challenges in densely cluttered environments. In Stage I, an Enhanced Tube-RRT* algorithm is developed by integrating active hybrid sampling and an adaptive expansion strategy, enabling rapid generation of a safe and feasible virtual tube in environments with dense obstacles. Moreover, a trajectory smoothness cost is explicitly incorporated into the edge cost to reduce excessive turns and thereby mitigate cable-induced oscillations. Simulation results demonstrate that the proposed Enhanced Tube-RRT* achieves a higher success rate and effective sampling rate than mixed-sampling Tube-RRT* (STube-RRT*) and adaptive-extension Tube-RRT* (AETube-RRT*), while producing a shorter optimal path with a smaller cumulative turning angle. In Stage II, a convex quadratic program is formulated by considering payload translational and rotational dynamics, cable tension constraints, and collision-safety constraints, yielding a smooth, collision-free desired payload trajectory. Finally, a centralized geometric control scheme is applied to the cascaded system to validate the effectiveness and feasibility of the proposed planning framework, offering a practical solution for payload attitude maneuvering in densely cluttered environments.