Reuleaux: Robot Base Placement by Reachability Analysis
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Abstract (arXiv)
Before beginning any robot task, users must position the robot's base, a task that now depends entirely on user intuition. While slight perturbation is tolerable for robots with moveable bases, correcting the problem is imperative for fixed-base robots if some essential task sections are out of reach. For mobile manipulation robots, it is necessary to decide on a specific base position before beginning manipulation tasks. This paper presents Reuleaux, an open source library for robot reachability analyses and base placement. It reduces the amount of extra repositioning and removes the manual work of identifying potential base locations. Based on the reachability map, base placement locations of a whole robot or only the arm can be efficiently determined. This can be applied to both statically mounted robots, where position of the robot and work piece ensure the maximum amount of work performed, and to mobile robots, where the maximum amount of workable area can be reached. Solutions are not limited only to vertically constrained placement, since complicated robotics tasks require the base to be placed at unique poses based on task demand. All Reuleaux library methods were tested on different robots of different specifications and evaluated for tasks in simulation and real world environment. Evaluation results indicate that Reuleaux had significantly improved performance than prior existing methods in terms of time-efficiency and range of applicability.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Oct 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 4; 2025: 2; 2026: 1.
11 papers.
- 2022Robot Learning of Mobile Manipulation with Reachability Behavior Priorscited by 22
- 2024Open-vocabulary Mobile Manipulation in Unseen Dynamic Environments with 3D Semantic Mapscited by 13
- 2023Active-Perceptive Motion Generation for Mobile Manipulationcited by 10
- 2021Learning suction graspability considering grasp quality and robot reachability for bin-pickingcited by 5
- 2026SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMscited by 2
- 2023Base Placement Optimization for Coverage Mobile Manipulation Taskscited by 1
- 2024QuadWBG: Generalizable Quadrupedal Whole-Body Graspingcited by 1
- 2025GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learningcited by 1
- 2024Simultaneous Localization and Affordance Prediction of Tasks from Egocentric Videocited by 0
- 2024Predictive Reachability for Embodiment Selection in Mobile Manipulation Behaviorscited by 0
- 2025Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturingcited by 0
- 2026SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs26 Feb 2026
- 2025GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning15 Sep 2025
- 2025Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing26 Feb 2025
- 2024QuadWBG: Generalizable Quadrupedal Whole-Body Grasping11 Nov 2024
- 2024Predictive Reachability for Embodiment Selection in Mobile Manipulation Behaviors28 Oct 2024
- 2024Simultaneous Localization and Affordance Prediction of Tasks from Egocentric Video18 Jul 2024
- 2024Open-vocabulary Mobile Manipulation in Unseen Dynamic Environments with 3D Semantic Maps26 Jun 2024
- 2023Active-Perceptive Motion Generation for Mobile Manipulation30 Sep 2023
- 2023Base Placement Optimization for Coverage Mobile Manipulation Tasks17 Apr 2023
- 2022Robot Learning of Mobile Manipulation with Reachability Behavior Priors8 Mar 2022
- 2021Learning suction graspability considering grasp quality and robot reachability for bin-picking4 Nov 2021