TopAY: Efficient Trajectory Planning for Differential Drive Mobile Manipulators Via Topological Paths Search and Arc Length-Yaw Parameterization
Long Xu, Choi Lam Wong, Mengke Zhang, Junxiao Lin, Jialiang Hou, Fei Gao
AI summary
Problem
Planning trajectories for differential drive mobile manipulators is computationally expensive and prone to failure in complex environments due to high-dimensional state spaces, nonholonomic constraints, and numerical singularities in existing parameterization methods.
Approach
The framework hierarchically acquires initial paths by searching topological routes for the base and parallelizing manipulator sampling, then optimizes trajectories using a novel polynomial representation parameterized by arc length and yaw to avoid singularities.
Key results
- Hierarchical path acquisition combining topological base search with parallel manipulator sampling
- Novel arc length-yaw polynomial trajectory representation eliminating differential flatness singularities
- Parallelized optimization framework that reduces computational overhead
- Validated superior planning efficiency and success rates over state-of-the-art methods in simulations and real-world tests
Why it matters
Enables real-time, robust autonomous navigation and manipulation for complex mobile robots in cluttered industrial, healthcare, and agricultural environments.
Abstract
Differential drive mobile manipulators combine the mobility of wheeled bases with the manipulation capability of multi-joint arms, enabling versatile applications but posing considerable challenges for trajectory planning due to their high-dimensional state space and nonholonomic constraints. This paper introduces TopAY, an optimization-based planning framework designed for efficient and safe trajectory generation for differential drive mobile manipulators. The framework em- ploys a hierarchical initial value acquisition strategy, including topological paths search for the base and parallel sampling for the manipulator. A polynomial trajectory representation with arc length–yaw parameterization is also proposed to reduce optimization complexity while preserving dynamic feasibility. Extensive simulation and real-world experiments validate that TopAY achieves higher planning efficiency and success rates than state-of-the-art method in dense and complex scenar- ios. The source code is released at https://github.com/TopAY- Planner/TopAY.