Hierarchical Trajectory Planning of Floating-Base Multi-Link Robot for Maneuvering in Confined Environments

Abstract

Floating-base multi-link robots can change their shape during flight, making them well-suited for applications in confined environments such as autonomous inspection and search and rescue. However, trajectory planning for such systems remains an open challenge because the problem lies in a high-dimensional, constraint-rich space where collision avoidance must be addressed together with kinematic limits and dynamic feasibility. This work introduces a hierarchical trajectory planning framework that integrates global guidance with configuration-aware local optimization. First, we exploit the dual nature of these robots—the root link as a rigid body for guidance and the articulated joints for flexibility—to generate global anchor states that decompose the planning problem into tractable segments. Second, we design a local trajectory planner that optimizes each segment in parallel with differentiable objectives and constraints, systematically enforcing kinematic feasibility and maintaining dynamic feasibility by avoiding control singularities. Third, we implement a complete system that directly processes point-cloud data, eliminating the need for handcrafted obstacle models. Extensive simulations and real-world experiments confirm that this framework enables an articulated aerial robot to exploit its morphology for maneuvering that rigid robots cannot achieve. To the best of our knowledge, this is the first planning framework for floating-base multi-link robots that has been demonstrated on a real robot to generate continuous, collision-free, and dynamically feasible trajectories directly from raw point-cloud inputs, without relying on handcrafted obstacle models. Note to Practitioners—Robots that can change their shape while moving have strong potential for tasks such as inspection, search and rescue, and exploration in spaces that are too tight for conventional drones. The key challenge is coordinating shape changes with safe and efficient navigation in cluttered environments. Our work addresses this by introducing a planning method that generates feasible trajectories considering both flight and configuration changes. For practitioners, the main benefit is improved reliability when operating in confined or unpredictable spaces: the robot can adapt its body to clear obstacles and complete maneuvering that would be impossible for rigid robots. The framework also reduces the need for manual modeling of environments by working directly with standard 3D sensor data. While these capabilities represent a meaningful advance, the current system still relies on accurate sensing and controlled test conditions. Adapting it to real-world uncertainty and longer, more complex missions remains an open direction. Future advances may enable wider deployment in industrial inspection, infrastructure maintenance, or hazardous-area exploration, as well as inspire similar solutions for other transformable robotic platforms.