pred-occ-planner
ICRA 2024 | Decentralized Multi-Agent Trajectory Planning in Dynamic Environments with Spatiotemporal Occupancy Grid Maps
This paper proposes a decentralized trajectory planning framework for the collision avoidance problem of mul- tiple micro aerial vehicles (MAVs) in environments with static and dynamic obstacles. The framework utilizes spatiotemporal occupancy grid maps (SOGM), which forecast the occupancy status of neighboring space in the near future, as the environ- ment representation. Based on this representation, we extend the kinodynamic A\* and the corridor-constrained trajectory optimization algorithms to efficiently tackle static and dynamic obstacles with arbitrary shapes.
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Use the MeshKore agent at https://meshkore.com/agent/siyuanwu99-pred-occ-planner — read its card at https://meshkore.com/agent/siyuanwu99-pred-occ-planner/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/siyuanwu99-pred-occ-plannerFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
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# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
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