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Titlebook: Distributed Autonomous Robotic Systems; 15th International S Fumitoshi Matsuno,Shun-ichi Azuma,Masahito Yamamot Conference proceedings 2022

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31#
發(fā)表于 2025-3-26 22:02:44 | 只看該作者
32#
發(fā)表于 2025-3-27 02:56:29 | 只看該作者
33#
發(fā)表于 2025-3-27 09:09:30 | 只看該作者
34#
發(fā)表于 2025-3-27 10:51:07 | 只看該作者
Decentralized Multi-robot Planning in Dynamic 3D Workspaces,putation on an invariant planning representation (invariant geometric tree) for low latency online planning and replanning amidst unpredictably moving dynamic obstacles to generate kinodynamically feasible and collision-free time-parameterized polynomial trajectories. Simulation results with up?to 1
35#
發(fā)表于 2025-3-27 14:40:33 | 只看該作者
Optimized Direction Assignment in Roadmaps for Multi-AGV Systems Based on Transportation Flows,strial environments. Considering the desired flows among different locations in the environment, we model the problem as a multi-commodity concurrent flow problem, which allows us to assign the directions of the paths in an optimized manner. The proposed solution is validated by means of simulations
36#
發(fā)表于 2025-3-27 20:23:48 | 只看該作者
,Datom: A Deformable Modular Robot for?Building Self-reconfigurable Programmable Matter,le must keep the connection to, at least, one other module. In order to miniaturize each module to few millimeters, we have proposed a design which is using electrostatic actuator. However, this movement is composed of several attachment, detachment creating the movement and each small step can fail
37#
發(fā)表于 2025-3-28 01:34:08 | 只看該作者
,The Impact of?Network Connectivity on?Collective Learning,-level interactions are themselves often governed by an underlying network structure. These networks are particularly important for collective learning and decision-making whereby agents must gather evidence from their environment and propagate this information to other agents in the system. Models
38#
發(fā)表于 2025-3-28 02:36:00 | 只看該作者
39#
發(fā)表于 2025-3-28 10:10:23 | 只看該作者
40#
發(fā)表于 2025-3-28 11:31:48 | 只看該作者
Evolving Robust Supervisors for Robot Swarms in Uncertain Complex Environments,f swarms to promote robust performance. In this paper, we design artificial supervision of swarms to enable an agent to interact with a swarm of robots and command it to efficiently search complex partially known environments. This is implemented through artificial evolution of human readable behavi
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