Autonomous navigation is a complex challenge that involves the interpretation and analysis of information about the scenario to facilitate the cognitive processes of a robot to perform free trajectories in dynamic env...Autonomous navigation is a complex challenge that involves the interpretation and analysis of information about the scenario to facilitate the cognitive processes of a robot to perform free trajectories in dynamic environments. To solve this, the paper introduces a Case-Based Reasoning methodology to endow robots with an efficient decision structure aiming of selecting the best maneuver to avoid collisions. In particular, Manhattan Distance was implemented to perform the retrieval process in CBR method. Four scenarios were depicted to run a set of experiments in order to validate the functionality of the implemented work. Finally, conclusions emphasize the advantages of CBR methodology to perform autonomous navigation in unknown and uncertain environments.展开更多
文摘Autonomous navigation is a complex challenge that involves the interpretation and analysis of information about the scenario to facilitate the cognitive processes of a robot to perform free trajectories in dynamic environments. To solve this, the paper introduces a Case-Based Reasoning methodology to endow robots with an efficient decision structure aiming of selecting the best maneuver to avoid collisions. In particular, Manhattan Distance was implemented to perform the retrieval process in CBR method. Four scenarios were depicted to run a set of experiments in order to validate the functionality of the implemented work. Finally, conclusions emphasize the advantages of CBR methodology to perform autonomous navigation in unknown and uncertain environments.
