---
title: "Real-Time Multi-Robot Motion Planning with Safe-Interval Search and Learning-Guided Repair"
authors:
  - "Rajat Kumar"
  - "Kristin Predeck"
  - "Ken Meszaros"
  - "Trevor Dardik"
doi: "10.24963/ijcai.2026/842"
url: "https://ijcai.org/proceedings/2026/842"
pdf: "https://ijcai.org/proceedings/2026/0842.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "7575-7582"
track: "AI and Robotics"
keywords:
  - "Robot control, planning, and execution with guarantees: Integrated task and motion planning with feedback control"
  - "Robot control, planning, and execution with guarantees: Safe and robust control under uncertainty"
  - "AIR: Robot control, planning, and execution with guarantees"
---

# Real-Time Multi-Robot Motion Planning with Safe-Interval Search and Learning-Guided Repair

Rajat Kumar, Kristin Predeck, Ken Meszaros, Trevor Dardik

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 7575-7582. DOI: [10.24963/ijcai.2026/842](https://doi.org/10.24963/ijcai.2026/842)

## Abstract

Motion planning among multiple robots in a shared space is a fundamental yet computationally challenging problem in robotics, with applications ranging from warehouse automation to autonomous fleets. In this work, we introduce a fast, scalable motion planner that achieves real-time, collision-free trajectory planning via a two-staged algorithm combining deterministic search-based planning with machine learning-driven conflict resolution. We present a prioritized Safe Interval Path Planning algorithm (SIPP-PP) with a novel limited goal reservation strategy to prevent goal-blocking conflicts while allowing shared goal regions. We added a second layer of ML-guided Large Neighborhood Search (LNS) procedure to our SIPP-PP algorithm for improving success rates in highly congested environments via intelligent selection of conflict resolution actions. The result is a planning system that generates collision-free paths for multiple robots in complex environments within tens of milliseconds. For example, compared to recent advanced learning-based methods such as diffusion planners, our planner is two-to-three orders of magnitude faster. Our work demonstrates a multi-robot planner capable of real-time operation in dense scenarios, satisfying the stringent requirements of industrial applications such as drive units in fulfillment centers.