Abstract
We propose a nature-inspired, intelligent collision management approach for use by multiple autonomous agents. This approach is calculated by each agent involved in possible collision through its own local view and without communication with other agents or central control. The approach uses both the current position and the velocity of other local agents to compute a future trajectory in order to both predict collision and avoid it. Our approach is capable of dealing with static obstacles and is developed in conjunction with a common kinematics metric ‘Minimal Predicted Distance (MPD)’ ensuring all agents remain free of collision while attempting to follow their goal direction, thus making the procedure well-suited for real-time applications. We build on prior work related to rectangular roundabout (‘rectabout’) and introduce the concept of hybrid rectabout for collision avoidance that takes into account heterogeneous agents, i.e. variable speed and variable size. Each agent has its own speed (and local view), and senses its surroundings and acts independently without central coordination or communication with other agents. We apply our hybrid rectabout maneuver to WowWee Rovio mobile robots and provide both analytic and empirical results to show that our fully decentralized, non-communicative and distributed approach generates collision-free motions.
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Liu, F., Narayanan, A. (2014). Intelligent Collision Avoidance between Autonomous Agents Using Adaptive Local Views. In: Dam, H.K., Pitt, J., Xu, Y., Governatori, G., Ito, T. (eds) PRIMA 2014: Principles and Practice of Multi-Agent Systems. PRIMA 2014. Lecture Notes in Computer Science(), vol 8861. Springer, Cham. https://doi.org/10.1007/978-3-319-13191-7_16
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DOI: https://doi.org/10.1007/978-3-319-13191-7_16
Publisher Name: Springer, Cham
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