¹Department for Software Engineering, Heilbronn University of Applied Sciences, Heilbronn, Germany.
²Department for Contemporary Sciences and Technologies, South-East European University, Tetovo, Macedonia.
³Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia.
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Abstract—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.

Index Terms—Autonomous navigation, obstacle avoidance, biologically-inspired learning.

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Cite: Ruben Nuredini, Bekim Fetaji, Ivan Chorbev, "Bio-inspired Obstacle Avoidance: From Animals to Intelligent Agents," Journal of Computers vol. 13, no. 2, pp. 146-153, 2018.