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

2026-02-25

AI Summary

Generating trajectories that simultaneously satisfy obstacle avoidance, kinematic, and dynamic feasibility remains challenging for floating-base multi-link robots operating in high-dimensional, highly constrained environments. This work proposes a hierarchical trajectory planning framework that first constructs configuration-aware anchor states based on the base link and joint characteristics, then performs parallel optimization of trajectory segments while directly processing raw point cloud data to enable end-to-end planning. Notably, the method eliminates the need for manual obstacle modeling and, for the first time, demonstrates autonomous generation of continuous, collision-free, and dynamically feasible trajectories on a real floating-base multi-link robot directly from point cloud inputs. Experimental results show that the framework enables multi-link aerial robots to accomplish agile maneuvers through confined spaces by exploiting morphological adaptation—tasks that are infeasible for rigid-bodied robots.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• trajectory planning
• floating-base multi-link robot
• confined environments
• collision avoidance
• dynamic feasibility

Innovation

• hierarchical trajectory planning
• floating-base multi-link robot
• point-cloud-based planning
• dynamic feasibility
• configuration-aware optimization