2,661 research outputs found
Decentralized collaborative transport of fabrics using micro-UAVs
Small unmanned aerial vehicles (UAVs) have generally little capacity to carry
payloads. Through collaboration, the UAVs can increase their joint payload
capacity and carry more significant loads. For maximum flexibility to dynamic
and unstructured environments and task demands, we propose a fully
decentralized control infrastructure based on a swarm-specific scripting
language, Buzz. In this paper, we describe the control infrastructure and use
it to compare two algorithms for collaborative transport: field potentials and
spring-damper. We test the performance of our approach with a fleet of
micro-UAVs, demonstrating the potential of decentralized control for
collaborative transport.Comment: Submitted to 2019 International Conference on Robotics and Automation
(ICRA). 6 page
3D Interaction System with Multiple Identified,Small,Wireless,Battery-less,Occlusion-free Magnetic Markers
Tohoku University北村喜
A Novel Graph-based Motion Planner of Multi-Mobile Robot Systems with Formation and Obstacle Constraints
Multi-mobile robot systems show great advantages over one single robot in
many applications. However, the robots are required to form desired
task-specified formations, making feasible motions decrease significantly.
Thus, it is challenging to determine whether the robots can pass through an
obstructed environment under formation constraints, especially in an
obstacle-rich environment. Furthermore, is there an optimal path for the
robots? To deal with the two problems, a novel graphbased motion planner is
proposed in this paper. A mapping between workspace and configuration space of
multi-mobile robot systems is first built, where valid configurations can be
acquired to satisfy both formation constraints and collision avoidance. Then,
an undirected graph is generated by verifying connectivity between valid
configurations. The breadth-first search method is employed to answer the
question of whether there is a feasible path on the graph. Finally, an optimal
path will be planned on the updated graph, considering the cost of path length
and formation preference. Simulation results show that the planner can be
applied to get optimal motions of robots under formation constraints in
obstacle-rich environments. Additionally, different constraints are considered
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