Development of a bio-inspired vision system for mobile micro-robots

In this paper, we present a new bio-inspired vision system for mobile micro-robots. The processing method takes inspiration from vision of locusts in detecting the fast approaching objects. Research suggested that locusts use wide field visual neuron called the lobula giant movement detector to respond to imminent collisions. We employed the locusts' vision mechanism to motion control of a mobile robot. The selected image processing method is implemented on a developed extension module using a low-cost and fast ARM processor. The vision module is placed on top of a micro-robot to control its trajectory and to avoid obstacles. The observed results from several performed experiments demonstrated that the developed extension module and the inspired vision system are feasible to employ as a vision module for obstacle avoidance and motion control

Modelling LGMD2 visual neuron system

Two Lobula Giant Movement Detectors (LGMDs) have been identified in the lobula region of the locust visual system: LGMD1 and LGMD2. LGMD1 had been successfully used in robot navigation to avoid impending collision. LGMD2 also responds to looming stimuli in depth, and shares most the same properties with LGMD1; however, LGMD2 has its specific collision selective responds when dealing with different visual stimulus. Therefore, in this paper, we propose a novel way to model LGMD2, in order to emulate its predicted bio-functions, moreover, to solve some defects of previous LGMD1 computational models. The mechanism of ON and OFF cells, as well as bioinspired nonlinear functions, are introduced in our model, to achieve LGMD2’s collision selectivity. Our model has been tested by a miniature mobile robot in real time. The results suggested this model has an ideal performance in both software and hardware for collision recognition

Investigation of Multi-Robots Food Foraging Efficiency with an Artificial Pheromone System

In nature, the pheromone released by social insects is crucial for communication, which has become a rich inspiration source of swarm robotics. By utilising the virtual pheromone in physical swarm robot system, we can coordinate individuals and simulate behaviours of social insects. This thesis aims to investigate two influences, i.e., the leader and the wind effects on multi-robots’ food foraging efficiency in an artificial pheromone system, wherein the pheromone is represented by light spots or trails on a TV screen. To investigate the leader effect, we remotely controlled a robot agent as a leader to guide other wandering agents to reach a food source with persistent virtual pheromone and then aggregate around it; the released pheromone by the leader could be sensed by other mates so that triggering following behaviour. We compare the aggregation efficiency with the scenarios without a leader robot agent. After that, we simulated wind effects on the virtual pheromone affecting its evaporation and diffusion. The experimental results demonstrate that without interacting with the leader, the aggregation efficiency is highly depending on start positions of follower agents within each experiment. The potential of using the leader interaction with the other robots can improve the swarm efficiency under the same experimental setting. Moreover, the experimenting results of wind effects on the artificial pheromone system and the food foraging simulation demonstrate the wind has the power to influence the food foraging efficiency, which cannot be ignored. This research indicates that the leader and the wind effects are important factors affecting the pheromone-based swarm efficiency