# Reuleaux: Robot Base Placement by Reachability Analysis

arXiv: https://arxiv.org/abs/1710.01328 · HTML: https://mlsanity.com/paper/1710.01328/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2017.

## Built on by (most cited first, 11 of 11)

- [Robot Learning of Mobile Manipulation with Reachability Behavior Priors](https://mlsanity.com/paper/2203.04051/) (2022)
- [Open-vocabulary Mobile Manipulation in Unseen Dynamic Environments with 3D Semantic Maps](https://mlsanity.com/paper/2406.18115/) (2024)
- [Active-Perceptive Motion Generation for Mobile Manipulation](https://mlsanity.com/paper/2310.00433/) (2023)
- [Learning suction graspability considering grasp quality and robot reachability for bin-picking](https://mlsanity.com/paper/2111.02571/) (2021)
- [SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs](https://mlsanity.com/paper/2602.22716/) (2026)
- [Base Placement Optimization for Coverage Mobile Manipulation Tasks](https://mlsanity.com/paper/2304.08246/) (2023)
- [QuadWBG: Generalizable Quadrupedal Whole-Body Grasping](https://mlsanity.com/paper/2411.06782/) (2024)
- [GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning](https://mlsanity.com/paper/2509.11594/) (2025)
- [Simultaneous Localization and Affordance Prediction of Tasks from Egocentric Video](https://mlsanity.com/paper/2407.13856/) (2024)
- [Predictive Reachability for Embodiment Selection in Mobile Manipulation Behaviors](https://mlsanity.com/paper/2410.21059/) (2024)
- [Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing](https://mlsanity.com/paper/2502.19340/) (2025)

## 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.