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When directly applying these models for multigroup UAV scenarios, the deadlock situation may happen. However, traditional collision-avoidance models for UAV swarm tend to focus on avoidance at individual UAV level, and no explicit strategy is designed for avoidance among multiple UAV groups.
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Significant improvements of both feasibility and success rate are shown, in comparison with other state-of-the-art methods and especially in crowded and high-speed scenarios.Ĭollision-avoidance control for UAV swarm has recently drawn great attention due to its significant implications in many industrial and commercial applications. Comprehensive simulation and experiment studies are conducted over large-scale multi-robot systems.
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Moreover, it is fully distributed and requires only local inter-robot communication. The method is based on formulating a convex optimization over the proposed modified buffered Voronoi cells in each planning horizon. It can provably ensure recursive feasibility and effectively resolve deadlocks online in addition to the handling of input and model constraints. Towards this end, we propose a systematic method called infinite-horizon model predictive control with deadlock resolution (IMPC-DR). Furthermore, when applied in a distributed manner, deadlocks often occur where several robots block each other indefinitely without resolution. However, despite of their versatility, many widely-used methods based on model predictive control (MPC) lack theoretical guarantees on the feasibility of underlying optimization. Using numerical simulations, we show that the proposed architecture can handle tasks of increased complexity while responding to unanticipated adverse configurations.Ĭollision-free trajectory generation within a shared workspace is fundamental for most multi-robot applications. The overall architecture can handle environmental and sensing uncertainty online, as the robot explores its workspace. To face these challenges, we propose a hybrid control architecture, where a symbolic controller generates high-level manipulation commands (e.g., grasp an object) based on environmental feedback, an informative planner designs paths to actively decrease the uncertainty of objects of interest, and a continuous reactive controller tracks the sparse waypoints comprising the informative paths while avoiding a priori unknown obstacles. Existing algorithms either do not scale well or neglect sensing and/or environmental uncertainty. In particular, we consider mobile sensing manipulators operating in environments with unknown geometry and uncertain movable objects, while being responsible for accomplishing tasks requiring grasping and releasing objects in a logical fashion. In this paper we address mobile manipulation planning problems in the presence of sensing and environmental uncertainty.