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

2026-04-16

AI Summary

This work addresses the challenge of trajectory planning for multi-quadrotor rigid-load systems in cluttered environments by proposing a two-stage planning framework. In the first stage, an enhanced Tube-RRT* algorithm is developed, incorporating active hybrid sampling and adaptive expansion strategies, while integrating trajectory smoothness costs into edge evaluation to suppress cable oscillations and efficiently generate safe virtual tunnels. The second stage formulates a convex quadratic program that jointly optimizes load dynamics and safety constraints to produce smooth, collision-free reference trajectories, with closed-loop performance validated via a centralized geometric controller. Experimental results demonstrate that the proposed method significantly outperforms STube-RRT* and AETube-RRT* in planning success rate and sampling efficiency, yielding shorter paths, reduced cumulative turning angles, and effective load attitude control in complex environments.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• trajectory planning
• multi-UAV
• rigid-payload
• cluttered environments
• cable-induced oscillations

Innovation

• Enhanced Tube-RRT*
• multi-UAV rigid-payload transportation
• trajectory smoothness optimization
• convex quadratic programming
• cable-induced oscillation mitigation