Multi Robot Route Planning (MRRP): Extended SpatialTemporal Prioritized Planning Autonomous vehicles are, in contrast to classical automated guided vehicles (AGVs), less predictable in their behavior and drive time. Therefore, the issue of how to efficiently control these vehicles arises, because autonomous agents need to be coordinated and not controlled, to give autonomous behaviors and actions space. The scientific contribution of this paper is a novel approach, based on prioritized planning to target this issue as well as an open source framework for evaluation and comparison. Prioritized planning has the disadvantage of being neither optimal nor complete, however, it has the advantage of being computationally feasible. This work utilizes prioritized planning to significantly increase the set of feasible scenarios through collision prevention: by locally finding alternative routes and adding them to the search graph. The paper clearly formulates the extensions needed and delineates the approachxe2x80x99s limits, as it is neither optimal nor complete. More importantly, however, our method calculates routes for each vehicle with intervehicle synchronization, enabling vehicles to execute the plan in a distributed fashion without centralized control, thereby allowing autonomous behavior. Finally, results are verified by comparing our Multi Robot Router (MRR) proposed in this work to classical approaches. The software developed as well as the test sets are publicly available for ROS and the simulation environment.

Dynamic Realtime Multimodal Routing with Hierarchical Hybrid Planning We introduce the problem of Dynamic Realtime Multimodal Routing (DREAMR), which requires planning and executing routes under uncertainty for an autonomous agent. The agent has access to a timevarying transit vehicle network in which it can use multiple modes of transportation. For instance, a drone can either fly or ride on terrain vehicles for segments of their routes. DREAMR is a difficult problem of sequential decision making under uncertainty with both discrete and continuous variables. We design a novel hierarchical hybrid planning framework to solve the DREAMR problem that exploits its structural decomposability. Our framework consists of a global openloop planning layer that invokes and monitors a local closedloop execution layer. Additional abstractions allow efficient and seamless interleaving of planning and execution. We create a largescale simulation for DREAMR problems, with each scenario having hundreds of transportation routes and thousands of connection points. Our algorithmic framework significantly outperforms a receding horizon control baseline, in terms of elapsed time to reach the destination and energy expended by the agent.