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Bio-Inspired Obstacle Avoidance: from Animals to Intelligent Agents

  • A considerable amount of research in the field of modern robotics deals with mobile agents and their autonomous operation in unstructured, dynamic, and unpredictable environments. Designing robust controllers that map sensory input to action in order to avoid obstacles remains a challenging task. Several biological concepts are amenable to autonomous navigation and reactive obstacle avoidance. We present an overview of most noteworthy, elaborated, and interesting biologically-inspired approaches for solving the obstacle avoidance problem. We categorize these approaches into three groups: nature inspired optimization, reinforcement learning, and biorobotics. We emphasize the advantages and highlight potential drawbacks of each approach. We also identify the benefits of using biological principles in artificial intelligence in various research areas.

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Author:Ruben Nuredini
Document Type:Preprint
Year of Completion:2016
Release Date:2017/03/13
Tag:autonomous navigation; biologically-inspired learning; obstacle avoidance
Pagenumber:7 Seiten
Preprint; bei Journal of computers, ISSN 1796-203X zur Veröffentlichung im Sommer 2017 angenommen
Access Right:Frei zugänglich
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung