Multi-Goal Motion Memory

Autonomous mobile robots (e. g. , warehouse logistics robots) often need to traverse complex, obstacle-rich, and changing environments to reach multiple fixed goals (e. g. , ware-house shelves). Traditional motion planners need to calculate the entire multi-goal path from scratch in response to changes in the environment, which results in a large consumption of computing resources. This process is not only time-consuming but also may not meet real-time requirements in application scenarios that require rapid response to environmental changes. In this paper, we provide a novel Multi-Goal Motion Memory technique 1 1 https://github.com/yuanjielu-64/MGMM_ICRA2025.git that allows sampling-based motion planners to use previous planning experiences to accelerate future multi-goal planning in changing environments. This algorithm allows robots to use previous planning experiences to accelerate future multi-goal planning in changing environments. Specifically, our approach predicts dynamically feasible trajectories and distances between goal pairs to guide the sampling process to construct a motion map, to inform Traveling Salesman Problem (TSP) solvers to compute a tour, and to efficiently produce motion plans. Experiments conducted with a vehicle and a snake-like robot in obstacle-rich environments show that the proposed Motion Memory technique can substantially accelerate planning speed by up to 90%. Furthermore, the solution quality is comparable to state-of-the-art algorithms and even better in some environments.

Authors

• Yuanjie Lu
• Dibyendu Das
• Erion Plaku
• Xuesu Xiao

Keywords

• Heuristic algorithms
• Traveling salesman problems
• Prediction algorithms
• Real-time systems
• Planning
• Trajectory
• Mobile robots
• Logistics
• Autonomous robots
• Environmental Changes
• Complex Environment
• Future Plans
• Path Planning
• Environmental Planning
• Traveling Salesman Problem
• Automated Guided Vehicles
• State Space
• Shortest Path
• Latent Space
• Representation Of Space
• Entire Space
• Latent Representation
• Robot Model
• Planning Problem
• Dijkstra’s Algorithm
• Number Of Goals
• Cost Matrix
• Robot Dynamics
• Collision-free Path
• General Motion
• Classical Motion
• Node Mapping
• Collision-free Trajectory