# Efficient Path Planning in Narrow Passages for Robots with Ellipsoidal Components

arXiv: https://arxiv.org/abs/2104.04658 · HTML: https://mlsanity.com/paper/2104.04658/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2021.

## Built on by (most cited first, 7 of 7)

- [Marching-Primitives: Shape Abstraction from Signed Distance Function](https://mlsanity.com/paper/2303.13190/) (2023)
- [Distance and Collision Probability Estimation from Gaussian Surface Models](https://mlsanity.com/paper/2402.00186/) (2024)
- [A Structured Prediction Approach for Robot Imitation Learning](https://mlsanity.com/paper/2309.14829/) (2023)
- [URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2505.20175/) (2025)
- [Why is That a Good or Not a Good Frying Pan? -- Knowledge Representation for Functions of Objects and Tools for Design Understanding, Improvement, and Generation](https://mlsanity.com/paper/2303.06152/) (2023)
- [Deep Polycuboid Fitting for Compact 3D Representation of Indoor Scenes](https://mlsanity.com/paper/2503.14912/) (2025)
- [A Holistic Method for Superquadric Fitting Using Unsupervised Clustering Analysis](https://mlsanity.com/paper/2605.16779/) (2026)

## Abstract (arXiv)

Path planning has long been one of the major research areas in robotics, with PRM and RRT being two of the most effective classes of planners. Though generally very efficient, these sampling-based planners can become computationally expensive in the important case of "narrow passages". This paper develops a path planning paradigm specifically formulated for narrow passage problems. The core is based on planning for rigid-body robots encapsulated by unions of ellipsoids. Each environmental feature is represented geometrically using a strictly convex body with a C1 boundary (e.g., superquadric). The main benefit of doing this is that configuration-space obstacles can be parameterized explicitly in closed form, thereby allowing prior knowledge to be used to avoid sampling infeasible configurations. Then, by characterizing a tight volume bound for multiple ellipsoids, robot transitions involving rotations are guaranteed to be collision-free without needing to perform traditional collision detection. Furthermore, by combining with a stochastic sampling strategy, the proposed planning framework can be extended to solving higher dimensional problems in which the robot has a moving base and articulated appendages. Benchmark results show that the proposed framework often outperforms the sampling-based planners in terms of computational time and success rate in finding a path through narrow corridors for both single-body robots and those with higher dimensional configuration spaces. Physical experiments using the proposed framework are further demonstrated on a humanoid robot that walks in several cluttered environments with narrow passages.