Multi-goal Rapidly Exploring Random Tree with Safety and Dynamic Constraints for UAV Cooperative Path Planning

2025-04-16

IEEE Transactions on Vehicular Technology

AI Summary

This paper addresses the multi-UAV cooperative path planning problem for complex missions requiring synchronized arrival at multiple targets while satisfying dynamic feasibility and safety constraints. We propose MultiRRT, a novel sampling-based algorithm built upon the RRT framework, featuring synchronized multi-agent sampling and expansion, explicit modeling of vehicle dynamics and obstacle avoidance constraints. Our key contributions include: (i) a node-pruning strategy and Bézier interpolation method, with theoretical guarantees ensuring strict dynamic feasibility and collision-free trajectories; and (ii) integrated collision checking and cooperative constraint optimization. Extensive simulations and real-world flight experiments demonstrate that MultiRRT generates zero-collision paths with shorter length and superior curvature properties compared to Theta-RRT, FN-RRT, RRT*, and RRT*-Smart. The implementation is publicly available.

Technology Category

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

Cooperative path planning is gaining its importance due to the increasing demand on using multiple unmanned aerial vehicles (UAVs) for complex missions. This work addresses the problem by introducing a new algorithm named MultiRRT that extends the rapidly exploring random tree (RRT) to generate paths for a group of UAVs to reach multiple goal locations at the same time. We first derive the dynamics constraint of the UAV and include it in the problem formulation. MultiRRT is then developed, taking into account the cooperative requirements and safe constraints during its path-searching process. The algorithm features two new mechanisms, node reduction and Bezier interpolation, to ensure the feasibility and optimality of the paths generated. Importantly, the interpolated paths are proven to meet the safety and dynamics constraints imposed by obstacles and the UAVs. A number of simulations, comparisons, and experiments have been conducted to evaluate the performance of the proposed approach. The results show that MultiRRT can generate collision-free paths for multiple UAVs to reach their goals with better scores in path length and smoothness metrics than state-of-the-art RRT variants including Theta-RRT, FN-RRT, RRT*, and RRT*-Smart. The generated paths are also tested in practical flights with real UAVs to evaluate their validity for cooperative tasks. The source code of the algorithm is available at https://github.com/duynamrcv/multi-target_RRT

Problem

• Develops MultiRRT for UAV cooperative path planning with multiple goals
• Ensures path feasibility with safety and dynamic constraints
• Optimizes path length and smoothness for multiple UAVs

Innovation

• Extends RRT for multi-goal UAV path planning
• Incorporates dynamics and safety constraints
• Uses node reduction and Bezier interpolation