Search-Based Spatiotemporal and Multi-Robot Motion Planning on Graphs of Space-Time Convex Sets
arXiv:2607.00444v1 Announce Type: cross Abstract: Spatiotemporal motion planning, especially in multi-robot settings, requires robots to reason about collision-free regions that change over time, which is challenging in continuous spaces when feasible regions are transient and geometrically constrained. We present an algorithmic framework based on graphs of space-time convex sets (ST-GCSs), where collision-free regions are represented as convex sets in space-time and trajectories correspond to paths on the graph together with continuous motions within the selected sets. We formulate time-optim
This research addresses a fundamental challenge in robotic motion planning, particularly crucial as multi-robot systems and autonomous agents become more complex and prevalent in real-world scenarios.
Advanced spatiotemporal multi-robot motion planning is essential for the reliable and efficient deployment of autonomous systems, directly impacting their commercial viability and safety in unstructured environments.
The proposed algorithmic framework, ST-GCSs, offers a novel approach to handle complex, time-varying collision constraints, potentially enabling more sophisticated and autonomous robotic operations than previously feasible.
- · Robotics industry
- · Logistics and manufacturing sectors
- · AI research institutions
- · Traditional human-supervised systems
- · Less efficient motion planning architectures
More robust and scalable multi-robot deployments in dynamic environments.
Accelerated development of general-purpose autonomous agents and robotic workforces.
Significant reduction in operational costs and increased output across various industries due to advanced automation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI