TopAY: Efficient Trajectory Planning for Differential Drive Mobile Manipulators via Topological Paths Search and Arc Length-Yaw Parameterization

TL;DR

TopAY tackles trajectory planning for differential drive mobile manipulators by decoupling base-path search from manipulator sampling and adopting arc length-$s$ and yaw-$\theta$ trajectory parameterization to address nonholonomic constraints efficiently. It combines a hierarchical initial-value acquisition with topological base-path search in $SE(2)$ with parallel manipulator sampling and resolves the optimization with a polynomial trajectory representation and a PH-ALM/L-BFGS solver, achieving improved feasibility and speed. Real-world and simulation experiments show higher planning efficiency and success rates in dense, cluttered environments compared with state-of-the-art methods, with code released at https://github.com/TopAY-Planner/TopAY. This approach enables safer, real-time autonomous manipulation tasks in indoor settings, and offers a practical path toward scalable multi-arm or more complex mobile bases.

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 employs 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 scenarios. The source code is released at https://github.com/TopAY-Planner/TopAY .

Figures (5)

• Figure 1: A differential drive mobile manipulator delivers a mouse from the reception to a workstation in an office using the proposed planner. The red curve in Figure (a) represents the trajectory of the gripper. Figure (b) shows the motion curves of the base in arc length-yaw and Cartesian space.
• Figure 2: Planning framework.
• Figure 3: Hardware settings and software system of the DDMoMa used in real-world experiments.
• Figure 4: Real-world experiments. The DDMoMa successfully performs pick-and-place tasks. Snapshots of filmed videos and RViz visualizations are presented in chronological order. In RViz visualizations, orange boxes illustrate the 3D occupancy grid map being updated in real-time. The blue silhouette represents the current state of the DDMoMa. The series of red silhouettes represent the trajectory currently tracked by the DDMoMa.
• Figure 5: Motion trajectories of the DDMoMa (upper half) and time plots of the DDB's kinematic variables (lower half).