A New Approach to Time-Optimal Path Parameterization based on Reachability Analysis
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Abstract (arXiv)
Time-Optimal Path Parameterization (TOPP) is a well-studied problem in robotics and has a wide range of applications. There are two main families of methods to address TOPP: Numerical Integration (NI) and Convex Optimization (CO). NI-based methods are fast but difficult to implement and suffer from robustness issues, while CO-based approaches are more robust but at the same time significantly slower. Here we propose a new approach to TOPP based on Reachability Analysis (RA). The key insight is to recursively compute reachable and controllable sets at discretized positions on the path by solving small Linear Programs (LPs). The resulting algorithm is faster than NI-based methods and as robust as CO-based ones (100% success rate), as confirmed by extensive numerical evaluations. Moreover, the proposed approach offers unique additional benefits: Admissible Velocity Propagation and robustness to parametric uncertainty can be derived from it in a simple and natural way.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Jul 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 3; 2025: 3; 2026: 4.
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- 2024Grasping Trajectory Optimization with Point Cloudscited by 2
- 2024Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learningcited by 2
- 2026Make Code as Policy Great Again: Frontier Agents Write, Call, and Evolve Robot Toolscited by 1
- 2022Learning Time-optimized Path Tracking with or without Sensory Feedbackcited by 0
- 2025Vision-based automatic fruit counting with UAVcited by 0
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- 2026Policy-Calibrated DAgger: Offline Calibrated Noise Injection for Imitation Learning24 Sep 2026
- 2026ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients30 Jun 2026
- 2025What Can RL Bring to VLA Generalization? An Empirical Study26 May 2025
- 2025The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning6 May 2025
- 2025Vision-based automatic fruit counting with UAV17 Mar 2025
- 2024The Art of Imitation: Learning Long-Horizon Manipulation Tasks from Few Demonstrations18 Jul 2024
- 2024Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning28 Jun 2024
- 2024Grasping Trajectory Optimization with Point Clouds8 Mar 2024
- 2023Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation6 Sep 2023
- 2022Learning Time-optimized Path Tracking with or without Sensory Feedback3 Mar 2022
- 2024The Art of Imitation: Learning Long-Horizon Manipulation Tasks from Few Demonstrationscited by 11
- 2021Learning suction graspability considering grasp quality and robot reachability for bin-pickingcited by 5
- 2021Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectorycited by 2
- 2024Grasping Trajectory Optimization with Point Cloudscited by 2
- 2026Make Code as Policy Great Again: Frontier Agents Write, Call, and Evolve Robot Toolscited by 1
- 2022Learning Time-optimized Path Tracking with or without Sensory Feedbackcited by 0
- 2025Vision-based automatic fruit counting with UAVcited by 0
- 2026Make Code as Policy Great Again: Frontier Agents Write, Call, and Evolve Robot Tools30 Sep 2026
- 2025Vision-based automatic fruit counting with UAV17 Mar 2025
- 2024The Art of Imitation: Learning Long-Horizon Manipulation Tasks from Few Demonstrations18 Jul 2024
- 2024Grasping Trajectory Optimization with Point Clouds8 Mar 2024
- 2022Learning Time-optimized Path Tracking with or without Sensory Feedback3 Mar 2022
- 2021Learning suction graspability considering grasp quality and robot reachability for bin-picking4 Nov 2021
- 2021Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory2 Jul 2021