ReloPush: Multi-object Rearrangement in Confined Spaces with a Nonholonomic Mobile Robot Pusher

2024-09-26

arXiv.org

AI Summary

Multi-object rearrangement for non-holonomic mobile pushing robots in confined, high-clutter environments remains challenging due to coupled motion planning, contact dynamics, and physical feasibility constraints. Method: This paper proposes a novel approach integrating environment push-traversability modeling with joint motion–physics optimization. We introduce the Push-Traversability Graph to explicitly encode push feasibility across configurations. For the first time, we unify environmental pushability, robot kinematics (via Dubins-curve path planning), and rigid-body dynamic stability constraints within a single graph-based optimization framework, solved via efficient graph search. Results: On dense 8-object scenarios, our method achieves orders-of-magnitude speedup over baseline planners, enabling real-time rearrangement. Physical experiments demonstrate significantly improved contact maintenance, reduced execution time, and enhanced robustness compared to prior approaches.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We focus on the problem of rearranging a set of objects within a confined space with a nonholonomically constrained mobile robot pusher. This problem is relevant to many real-world domains, including warehouse automation and construction. These domains give rise to instances involving a combination of geometric, kinematic, and physics constraints, which make planning particularly challenging. Prior work often makes simplifying assumptions like the use of holonomic mobile robots or dexterous manipulators capable of unconstrained overhand reaching. Our key insight is we can empower even a constrained mobile pusher to tackle complex rearrangement tasks by enabling it to modify the environment to its favor in a constraint-aware fashion. To this end, we describe a Push-Traversability graph, whose vertices represent poses that the pusher can push objects from and edges represent optimal, kinematically feasible, and stable push-rearrangements of objects. Based on this graph, we develop ReloPush, a planning framework that leverages Dubins curves and standard graph search techniques to generate an efficient sequence of object rearrangements to be executed by the pusher. We evaluate ReloPush across a series of challenging scenarios, involving the rearrangement of densely cluttered workspaces with up to eight objects by a 1tenth mobile robot pusher. ReloPush exhibits orders of magnitude faster runtimes and significantly more robust execution in the real world, evidenced in lower execution times and fewer losses of object contact, compared to two baselines lacking our proposed graph structure.

Problem

• Multi-object rearrangement in confined spaces using a nonholonomic robot.
• Addressing geometric, kinematic, and physics constraints in rearrangement planning.
• Developing a graph-based framework for efficient and robust object pushing tasks.

Innovation

• Push-Traversability graph for unified constraints representation
• Graph-based planning framework for multi-object rearrangement
• Efficient real-world execution with reduced runtime and robust performance