# Safe Interval Path Planning With Kinodynamic Constraints

arXiv: https://arxiv.org/abs/2302.00776 · HTML: https://mlsanity.com/paper/2302.00776/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/1812.06355/) (2018)
- [Prioritized Multi-agent Path Finding for Differential Drive Robots](https://mlsanity.com/paper/1911.10578/) (2019)
- [Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles](https://mlsanity.com/paper/2104.06681/) (2021)
- [Revisiting Bounded-Suboptimal Safe Interval Path Planning](https://mlsanity.com/paper/2006.01195/) (2020)
- [Prioritized SIPP for Multi-Agent Path Finding With Kinematic Constraints](https://mlsanity.com/paper/2108.05145/) (2021)

## Built on by (most cited first, 4 of 4)

- [Neural Potential Field for Obstacle-Aware Local Motion Planning](https://mlsanity.com/paper/2310.16362/) (2023)
- [WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning](https://mlsanity.com/paper/2508.01495/) (2025)
- [Dynamic Neural Potential Field: Online Trajectory Optimization in the Presence of Moving Obstacles](https://mlsanity.com/paper/2410.06819/) (2024)
- [Shared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2610.09034/) (2026)

## Abstract (arXiv)

Safe Interval Path Planning (SIPP) is a powerful algorithm for solving single-agent pathfinding problem when the agent is confined to a graph and certain vertices/edges of this graph are blocked at certain time intervals due to dynamic obstacles that populate the environment. Original SIPP algorithm relies on the assumption that the agent is able to stop instantaneously. However, this assumption often does not hold in practice, e.g. a mobile robot moving with a cruising speed is not able to stop immediately but rather requires gradual deceleration to a full stop that takes time. In other words, the robot is subject to kinodynamic constraints. Unfortunately, as we show in this work, in such a case original SIPP is incomplete. To this end, we introduce a novel variant of SIPP that is provably complete and optimal for planning with acceleration/deceleration. In the experimental evaluation we show that the key property of the original SIPP still holds for the modified version -- it performs much less expansions compared to A* and, as a result, is notably faster.