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Deep Reinforcement Learning for Dynamic Obstacle Avoidance of Mobile Robots in Indoor Environments: A Review. [PDF]
Sensors (Basel)Zhao J, Zhao H, Li B, Gong X.
europepmc +1 more source
Dataset for autonomous agriculture using robots to inspect corn and beet. [PDF]
Data BriefSánchez de la Fuente S +5 more
europepmc +1 more source
Evolutionary Neurocontrollers for Autonomous Mobile Robots [PDF]
Neural Networks, 1998 In this article we describe a methodology for evolving neurocontrollers of autonomous mobile robots without human intervention. The presentation, which spans from technological and methodological issues to several experimental results on evolution of physical mobile robots, covers both previous and recent work in the attempt to provide a unified ...
Francesco Mondada
exaly +4 more sources
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Autonomous Mobile Robots: Refining the Computational Landscape
2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2021Within distributed computing, the study of distributed systems of identical mobile computational entities, called robots, operating in a Euclidean space is rather extensive. When a robot is activated, it executes a Look-Compute-Move cycle: it takes a snapshot of the environment (Look); with this input, it computes its destination (Compute); and then it
Kevin Buchin +5 more
openaire +3 more sources
IFAC Proceedings Volumes, 1988
Abstract Integrating autonomous mobile robots in a CIM-environment is a challenging problem concerning the design of the technical systems and the related control structure. To guarantee the flexibility of the entire system, a hierarchically structured modular concept is needed which automatically adapts to the particular task.
E. Freund, P. Kaever
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Abstract Integrating autonomous mobile robots in a CIM-environment is a challenging problem concerning the design of the technical systems and the related control structure. To guarantee the flexibility of the entire system, a hierarchically structured modular concept is needed which automatically adapts to the particular task.
E. Freund, P. Kaever
openaire +1 more source