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

Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm

Aggregation is one of the most fundamental behaviors and has been studied in swarm robotic researches for more than two decades. Studies in biology have revealed that the environment is a preeminent factor, especially in cue-based aggregation. This can be defined as aggregation at a particular location which is a heat or a light source acting as a cue indicating an optimal zone. In swarm robotics, studies on cue-based aggregation mainly focused on different methods of aggregation and different parameters such as population size. Although of utmost importance, environmental effects on aggregation performance have not been studied systematically. In this paper, we study the effects of different environmental factors: size, texture and number of cues in a static setting, and moving cues in a dynamic setting using real robots. We used the aggregation time and size of the aggregate as the two metrics with which to measure aggregation performance. We performed real robot experiments with different population sizes and evaluated the performance of aggregation using the defined metrics. We also proposed a probabilistic aggregation model and predicted the aggregation performance accurately in most of the settings. The results of the experiments show that environmental conditions affect the aggregation performance considerably and have to be studied in depth

Biologically Inspired Connected Advanced Driver Assistance Systems

Advanced Driver Assistance Systems (ADAS) have become commonplace in the automotive industry over the last few decades. Even with the advent of ADAS, however, there are still a significant number of accidents and fatalities. ADAS has in some instances been shown to significantly reduce the number and severity of accidents. Manufacturers are working to avoid ADAS plateauing for effectiveness, which has led the industry to pursue various avenues of investment to ascend the next mountain of challenges – vehicle autonomy, smart mobility, connectivity, and electrification – for reducing accidents and injuries. A number of studies pertaining to ADAS scrutinize a specific ADAS technology for its effectiveness at mitigating accidents and reducing injury severity. A few studies take holistic accounts of ADAS. There are a number of directions ADAS could be further progressed. Industry manufacturers are improving existing ADAS technologies through multiple avenues of technology advancement. A number of ADAS systems have already been improved from passive, alert or warning, systems to active systems which provide early warning and if no action is taken will control the vehicle to avoid a collision or reduce the impact of the collision. Studies about the individual ADAS technologies have found significant improvement for reduction in collisions, but when evaluating the actual vehicles driving the performance of ADAS has been fairly constant since 2015. At the same time, industry is looking at networking vehicle ADAS with fixed infrastructure or with other vehicles’ ADAS. The present literature surrounding connected ADAS be it with fixed systems or other vehicles with ADAS focuses on the why and the how information is passed between vehicles. The ultimate goal of ADAS and connected ADAS is the development of autonomous vehicles. Biologically inspired systems provide an intriguing avenue for examination by applying self-organization found in biological communities to connecting ADAS among vehicles and fixed systems. Biological systems developed over millions of years to become highly organized and efficient. Biological inspiration has been used with much success in several engineering and science disciplines to optimize processes and designs. Applying movement patterns found in nature to automotive transportation is a rational progression. This work strategizes how to further the effectiveness of ADAS through the connection of ADAS with supporting assets both fixed systems and other vehicles with ADAS based on biological inspiration. The connection priorities will be refined by the relative positioning of the assets interacting with a particular vehicle’s ADAS. Then based on the relative positioning data distribution among systems will be stratified based on level of relevance. This will reduce the processing time for incorporating the external data into the ADAS actions. This dissertation contributes to the present understanding of ADAS effectiveness in real-world situations and set forth a method for how to optimally connect local ADAS vehicles following from biological inspiration. Also, there will be a better understanding of how ADAS reduces accidents and injury severity. The method for how to structure an ADAS network will provide a framework for auto-manufacturers for the development of their proprietary networked ADAS. This method will lead to a new horizon for reducing accidents and injury severity through the design of connecting ADAS equipped vehicles.Ph.D