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Title: Bio-inspired visual motion sensing systems for mobile robots
Author: Hu, Cheng
ISNI:       0000 0004 7657 9637
Awarding Body: University of Lincoln
Current Institution: University of Lincoln
Date of Award: 2017
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Many animals, especially flying insects are experts on reacting to approaching predators. For robots, the ability to avoiding collisions is also crucial. In locusts, a visual neuron called the Lobula Giant Movement Detector (LGMD) has been identified to be responsible for evoking collision avoidance behaviours. It has been modelled for collision avoidance on large robots or vehicles whose computational power are abundant. For micro robots, however, the limited computational capabilities on-board prevent the LGMD model to be accomplished on the robot by its own. Therefore in earlier researches, those micro robots serve only as image grabbers and motion actuators, leaving majority of the model processed on a host device connected. The unavoidable communication and consequent latency have become the bottlenecks that restrains the employment of this promising collision avoidance model in multi-agent research fields such as swarm robotics. This research focuses on the embedded modelling and realization of this bio-inspired collision sensitive model ELGMD. By carefully considering the required on-board resource, a novel micro robot Colias IV is designed to meet the requirements. Featured with the sufficient computing power, various of sensing modalities including a tiny camera, the modularized design and other specialities, this robot has become an advantageous platform to perform embedded vision tasks. The bio-inspired neural model Embedded-LGMD (ELGMD) is realized on the micro robot that can run autonomously without any off-board guidance. Optimization on the structure and timing has guaranteed its computational efficiency. The performance of the ELGMD and the effectiveness on triggering the robot's collision avoidance behaviour are tested via systematic experiments. To achieve more precise interactive behaviours with other kinds of moving obstacles, a compound motion detection system is realized within the robot to detect various of motion patterns by integrating several neural models at a higher level, in which those LGMD-like neural models are accomplished by an unified ELGMD model with minimum reconfiguration. Experiments have been conducted to validate the improved ELGMD model and the compound motion detection system. Results of this research have demonstrated the design goals of all the proposed modules, including the hardware platform, the bio-inspired model and the compound motion detection system, indicating the practicability of implementing these bio-inspired visual motion sensing systems for further robotic studies.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: G400 Computer Science ; H670 Robotics and Cybernetics ; G730 Neural Computing