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Found 8 papers, 7 papers with code
Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks
The second stage solves only for drones, by routing them over a composite of the road network and the transit network defined by truck paths from the first stage.
Scalable Anytime Planning for Multi-Agent MDPs
We also introduce a multi-drone delivery domain with dynamic, i. e., state-dependent coordination graphs, and demonstrate how our approach scales to large problems on this domain that are intractable for other MCTS methods.
Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints
We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty.
Adaptive Informative Path Planning with Multimodal Sensing
AIPPMS requires reasoning jointly about the effects of sensing and movement in terms of both energy expended and information gained.
Efficient Large-Scale Multi-Drone Delivery Using Transit Networks
Our results show that the framework computes solutions typically within a few seconds on commodity hardware, and that drones travel up to $360 \%$ of their flight range with public transit.
Hybrid Planning for Dynamic Multimodal Stochastic Shortest Paths
Sequential decision problems in applications such as manipulation in warehouses, multi-step meal preparation, and routing in autonomous vehicle networks often involve reasoning about uncertainty, planning over discrete modes as well as continuous states, and reacting to dynamic updates.
Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning
We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR), which requires planning and executing routes under uncertainty for an autonomous agent.
Anytime Motion Planning on Large Dense Roadmaps with Expensive Edge Evaluations
We propose an algorithmic framework for efficient anytime motion planning on large dense geometric roadmaps, in domains where collision checks and therefore edge evaluations are computationally expensive.
Robotics
