Paper Info
Title
Evolution, Robustness And Generality Of A Team Of Simple Agents With Asymmetric Morphology In Predator-Prey Pursuit Problem
Abstract
One of the most desired features of autonomous robotic systems is their ability to accomplish complex tasks with a minimum amount of sensory information. Often, however, the limited amount of information (simplicity of sensors) should be compensated by more precise and complex control. An optimal tradeoff between the simplicity of sensors and control would result in robots featuring better robustness, higher throughput of production and lower production costs, reduced energy consumption, and the potential to be implemented at very small scales. In our work we focus on a society of very simple robots (modeled as agents in a multi-agent system) that feature an extreme simplicity of both sensors and control. The agents have a single line-of-sight sensor, two wheels in a differential drive configuration as effectors, and a controller that does not involve any computing, but rathera direct mapping of the currently perceived environmental state into a pair of velocities of the two wheels. Also, we applied genetic algorithms to evolve a mapping that results in effective behavior of the team of predator agents, towards the goal of capturing the prey in the predator-prey pursuit problem (PPPP), and demonstrated that the simple agents featuring the canonical (straightforward) sensory morphology could hardly solve the PPPP. To enhance the performance of the evolved system of predator agents, we propose an asymmetric morphology featuring an angular offset of the sensor, relative to the longitudinal axis. The experimental results show that this change brings a considerable improvement of both the efficiency of evolution and the effectiveness of the evolved capturing behavior of agents. Finally, we verified that some of the best-evolved behaviors of predators with sensor offset of 20 degrees are both (i) general in that they successfully resolve most of the additionally introduced, unforeseen initial situations, and (ii) robust to perception noise in that they show a limited degradation of the number of successfully solved initial situations.
Year
DOI
Venue
2019
10.3390/info10020072
INFORMATION
Keywords
Field
DocType
simple agents, micro-robots, asymmetric morphology, predator-prey problem, genetic algorithms
Differential (mechanical device),Control theory,Control theory,Computer science,Robustness (computer science),Artificial intelligence,Throughput,Robot,Energy consumption,Genetic algorithm,Machine learning,Offset (computer science)
Journal
Volume
Issue
ISSN
10
2
2078-2489
Citations 
PageRank 
References 
0
0.34
3
Authors
4
Name
Order
Citations
PageRank
Milen Georgiev101.35
Ivan Tanev227846.51
Katsunori Shimohara3327106.53
Thomas S. Ray412328.09