Safe Interval Path Planning

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8 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

MeshSIPP: Efficient Lattice Planning in Dynamic Environment
Oct 7, 2026Marat Agranovskiy, Konstantin YakovlevSafe Interval Path Planning Motion Planning
Optimal Planning in a Dynamic World
Oct 2, 2026Devin Wild Thomas, Solomon Eyal Shimony, Wheeler Ruml +3Safe Interval Path Planning Motion Planning
University of New Hampshire, USA · Ben-Gurion University of the Negev, Israel · Technion - Israel Institute of Technology, Israel +3
Path Planning with Motion Primitives in Dynamic Environments: SIPP on Lattices
Sep 25, 2026Marat AgranovskiySafe Interval Path Planning Motion Planning
Markov Lab, Saint-Petersburg University.
AOC-CBS: Anytime-Optimal Continuous-time Conflict-Based Search for Generalised Multi-Agent Path Finding
Aug 8, 2026Alvin Combrink, Sabino Francesco Roselli, Martin FabianRobust MAPF Conflict-Based Search
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
StochSIPP: Safe Interval Path Planning in Stochastic Dynamic Environments
Aug 1, 2026Ajith Kemisetti, Shahaf S. Shperberg, Yoonchang SungRoadmap-Based Motion Planning Safe Motion Planning
The University of Texas at Austin · Ben Gurion University of the Negev · Nanyang Technological University
Optimal any-angle path planning in static and dynamic environments
Jun 30, 2026Yiyuan Zou, Clark BorstGraph-Based Planning Motion Planning
Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands
Multi-Agent Pathfinding with Non-Unit Integer Edge Costs via Enhanced Conflict-Based Search and Graph Discretization
Apr 7, 2026Hongkai Fan, Qinjing Xie, Bo Ouyang +4Safe Interval Path Planning Multi-Agent Path Finding
College of Electrical and Information Engineering at Hunan University · China Mobile Group Hunan Company Limited, Changsha 410082, China
Precomputing Multi-Agent Path Replanning Using Temporal Flexibility
Jan 8, 2026Issa Hanou, Eric Kemmeren, Devin Wild Thomas +1Safe Interval Path Planning Multi-Agent Path Finding
Delft University of Technology · University of New Hampshire

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MeshSIPP: Efficient Lattice Planning in Dynamic Environment

Marat Agranovskiy, Konstantin Yakovlev

Safe Interval Path Planning Motion Planning Robot Motion Planning Safe Motion Planning Dynamic Obstacle Avoidance

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Autonomous navigation in dynamic environments requires computing spatiotemporal trajectories that satisfy non-holonomic motion constraints. When the trajectories of the moving obstacles are predictable or known, a promising approach is to rely on the combination of state lattices constructed from precomputed feasible motion primitives and Safe Interval Path Planning -- a search-based algorithm with strong theoretical guarantees. While this approach yields feasible paths, the rich primitive sets needed for smooth navigation induce a large branching factor, which becomes costly when coupled with time-dependent obstacle intervals. To this end, we present MeshSIPP, an efficient planner that removes the computational bottleneck by exploiting the fact that many primitives sweep the same regions and can therefore be validated together. MeshSIPP propagates primitives as spatial bundles, screens them with lightweight bounding-interval checks, and defers the expensive exact departure-time search until a primitive reaches its terminal state. A time-aware pruning rule additionally discards redundant space-time branches early in the search. We prove that the resulting search is complete and optimal. Extensive experiments over more than 6,000 benchmark instances and real-time ROS~2 simulations show that MeshSIPP achieves up to a 3

\times

speedup over state-of-the-art spatiotemporal planners.