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This paper presents a path-planning approach to enable a swarm of robots move to a goal region while avoiding collisions with static and dynamic obstacles. To provide scalability and account for the complexity of the interactions in the swarm, the proposed approach combines probabilistic roadmaps with potential fields. The underlying idea is to provide the swarm with a series of intermediate goals...
This paper focuses on motion-planning problems for high-dimensional mobile robots with nonlinear dynamics operating in complex environments. It is motivated by a recent framework that combines sampling-based motion planning in the state space with discrete search over a workspace decomposition. Building on this line of work, the premise of this paper is that the computational efficiency can be significantly...
To compute collision-free and dynamically-feasibile trajectories that satisfy high-level specifications given in a planning-domain definition language, this paper proposes to combine sampling-based motion planning with symbolic action planning. The proposed approach, Sampling-based Motion and Symbolic Action Planner (SMAP), leverages from sampling-based motion planning the underlying idea of searching...
Robotic surgical assistants offer the possibility of automating portions of a task that are time consuming and tedious in order to reduce the cognitive workload of a surgeon. This paper proposes using programming by demonstration to build generative models and generate smooth trajectories that capture the underlying structure of the motion data recorded from expert demonstrations. Specifically, motion...
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