Continuous-Space Roadmap Generation for Mobile Robot Fleets with Distance Constraints and Geometry-Aware Discretization

Marvin Rüdt; Constantin Enke; Kai Furmans

Home/Archive/arXiv

arXiv · 2511.07175

Continuous-Space Roadmap Generation for Mobile Robot Fleets with Distance Constraints and Geometry-Aware Discretization

Marvin Rüdt·Constantin Enke·Kai Furmans

View original source ↗Read source document ↗Export metadata ↓

Abstract

Efficient routing of mobile robot fleets requires roadmaps with high redundancy, short path lengths, and sufficient node and edge clearance for conflict-free operation. Existing grid-based methods sacrifice geometric fidelity and impose Manhattan-distance path length constraints, whereas existing continuous-space methods neglect minimum distance constraints and transport demand. This paper proposes a continuous-space roadmap generation method that addresses this gap by placing nodes at convex corner points of the free space and at station interaction points, discretizing free space via local grid expansion, enforcing minimum inter-node and node-edge distance constraints derived from robot dimensions, and applying transport demand-driven K-shortest path pruning. The method is evaluated across three intralogistics environments using two multi-agent pickup and delivery (MAPD) solvers against three baselines: a reaction-diffusion sampling method (GSRM), an 8-connected grid, and random sampling. Under Priority Inheritance with Backtracking (PIBT), the proposed method outperforms GSRM by 1.2-23.4 % at maximum fleet size, the grid by at least 9.1 %, and random sampling by more than 10.4 % across all environments, with a space-time A* solver confirming these results. It further attains near-optimal normalized path lengths of 1.03-1.05 and the highest inter-station connectivity at comparable roadmap complexity.

Explore related subjects

cs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Marvin Rüdt, Constantin Enke, Kai Furmans. 2026-06-30. Continuous-Space Roadmap Generation for Mobile Robot Fleets with Distance Constraints and Geometry-Aware Discretization. https://arxiv.org/abs/2511.07175

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Highly-Efficient Differentiable Simulation for Robotics

Robotics simulators have improved significantly in computational speed and scalability, enabling them to generate years of simulated data for complex systems in minutes or hours. Despite these advances, efficiently and accurately computing simulation derivatives remains an open challenge. Addressing this would accelerate the convergence of reinforcement learning and trajectory optimization algorithms, particularly for contact-rich problems. This paper introduces a unifying framework for robotic simulation that accounts for all factors, including dynamics, collisions, and friction. The resulting algorithm computes analytical derivatives of the simulation by implicit differentiation, explicitly handling the intrinsic non-smoothness of the collision and frictional stages while exploiting the sparsity induced by the multi-body structure. Benchmark results demonstrate state-of-the-art performance, with timings ranging from $5\,μ$s for a 7-dof manipulator to $95\,μ$s for a 36-dof humanoid, an improvement of at least two orders of magnitude over alternative methods. Implemented in C++, the code will be open-sourced after the review process to support applications such as simulation-driven learning and real-time control.

cs.RO↗

arXiv2026

GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots

Most modern approaches to quadruped locomotion focus on using Deep Reinforcement Learning (DRL) to learn policies from scratch, in an end-to-end manner. Such methods often fail to scale, as every new problem or application requires time-consuming and iterative reward definition and tuning. We present Generalist Contact-Conditioned Policy (GeCCo) --- a low-level policy trained with Deep Reinforcement Learning that is capable of tracking arbitrary contact points with a quadruped robot. We shift from task-specific end-to-end learning to a modular hierarchy in which a single planner-agnostic, contact-conditioned tracking policy serves as a robust interface between planning and control. By maintaining stable contact execution under dynamic uncertainty and planner mismatch, the policy enables high-level planners (e.g., handcrafted, learned, or optimization-based) to be composed and swapped without retraining. We demonstrate the scalability and robustness of our method by evaluating on a wide range of locomotion and manipulation tasks in a common framework and under a single generalist policy. These include a variety of gaits, traversing complex terrains (e.g., stairs and slopes) as well as previously unseen stepping-stones and narrow beams, and interacting with objects (e.g., pressing a button, pushing a barrel). Our framework acquires new behaviors more efficiently, simply by combining a task-specific high-level contact planner and the pre-trained generalist policy. Project website: https://vassil-atn.github.io/gecco.github.io

cs.RO↗

arXiv2026

ERUPT: An Open Toolkit for Interfacing with Robot Motion Planners in Extended Reality

We present the Extended Reality Universal Planning Toolkit (ERUPT), an extended reality (XR) system for interactive motion planning. This paper serves to introduce our open-source system to others who can use it as a base to develop immersive robot interaction applications. Our system allows users to create and dynamically reconfigure environments while they plan robot paths. ERUPT uses XR to provide a broad range of natural interaction capabilities, allowing users to grab and adjust objects in the environment similar to interaction in the real world, rather than using a mouse and keyboard with the scene projected onto a 2D computer screen. Our system integrates with MoveIt, a manipulation planning framework, allowing users to send motion planning requests and visualize the resulting robot paths in virtual or augmented reality. We provide a broad range of interaction modalities, allowing users to modify objects in the environment and interact with a virtual robot. Our system allows operators to visualize robot motions, ensuring desired behavior as it moves throughout the environment, without risk of collisions within a virtual space, and to then deploy planned paths on physical robots in the real world. Code can be found at https://github.com/parasollab/erupt.

cs.RO↗