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UBC Theses and Dissertations

A biology-inspired attention model with application in robot navigation Du, Yu

Abstract

At present, robots are applied to specific situations and needs, so special methods are adopted for determining and controlling their movements. This thesis investigates the attention and navigation control of a mobile robot for carrying out dynamically challenging tasks involving humans. Since the robot environment can be complex, dynamic, and unpredictable, the required capacities are defined as those where the robot is required to pay attention to the needed object, plan the best path to the goal location and avoid obstacles. This thesis makes significant contributions in the development of an integrated model of top-down and bottom-up visual attention with self-awareness, and a biology-inspired method of robot planning and obstacle avoidance for a mobile robot. Specifically, an integrated model of top-down and bottom-up visual attention with self-awareness for robot is developed for selecting the highest saliency area in robot view. For mimicking the human attention processes, a robot self-awareness model with a fuzzy decision making system is developed and utilized, which is an important improvement over the existing robot attention models. Inspired by a mammal’s spatial awareness and navigation capabilities, a path planning method based on biological recognition is proposed for navigation tasks of a mobile robot in an unstructured and dynamic environment. An episodic cognitive map that encapsulates the information of scene perception, state neurons and pose perception is built to realize the environment cognition of the robot. An algorithm of the event sequence planning is presented for real-time navigation using the minimum distance between events. The method can choose the optimal planning path based on the tasks. A visual navigation algorithm that has the scale invariant feature transform (SIFT) feature is presented. By using the SIFT feature information, the horizontal coordinate of the matched feature pairs is considered to achieve the purpose of visual navigation. An approach of obstacle avoidance for visual navigation is presented based on a risk function and feasible paths. It can choose an optimal path for obstacle avoidance and then return to path planning through the interaction with the surrounding environment. The developed methodologies are evaluated using both simulation and experimentation.

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Attribution-NonCommercial-NoDerivatives 4.0 International

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