Collective Robotic Search with Obstacle Avoidance Using Swarm Intelligence
Department
Electrical and Computer Engineering
Major
Electrical Engineering
Research Advisor
Venayagamoorthy, Ganesh K.
Advisor's Department
Electrical and Computer Engineering
Funding Source
UMR Opportunities for Undergraduate Research Experience (OURE) Program
Abstract
Dispensable, inexpensive robots are desirable for use in dangerous tasks, such as land mine detection, to remove the need for human involvement. A team of intelligent mobile robots can be used to locate a specified target in a high risk environment when the principles of Swarm Intelligence are employed. Swarm Intelligence is based on the collective interactions of animals.
A type of Swarm Intelligence, Particle Swarm Optimization (PSO) is demonstrated in this paper to be a useful technique in target search applications such as Collective Robotic Search (CRS). An algorithm for obstacle avoidance is presented in this paper with the PSO approach applied to navigate robots in collective search applications. Obstacles represented by basic geometric shapes to simulate perilous ground terrain are introduced to the search area. Results are presented to show that PSO algorithm based CRS is able to locate targets while avoiding hazardous pathways.
Biography
Lisa Smith is a junior undergraduate student at the University of Missouri--Rolla, majoring in Electrical Engineering.
Research Category
Engineering
Presentation Type
Oral Presentation
Document Type
Presentation
Award
Engineering oral presentation, Third place
Presentation Date
12 Apr 2006, 9:00 am
Collective Robotic Search with Obstacle Avoidance Using Swarm Intelligence
Dispensable, inexpensive robots are desirable for use in dangerous tasks, such as land mine detection, to remove the need for human involvement. A team of intelligent mobile robots can be used to locate a specified target in a high risk environment when the principles of Swarm Intelligence are employed. Swarm Intelligence is based on the collective interactions of animals.
A type of Swarm Intelligence, Particle Swarm Optimization (PSO) is demonstrated in this paper to be a useful technique in target search applications such as Collective Robotic Search (CRS). An algorithm for obstacle avoidance is presented in this paper with the PSO approach applied to navigate robots in collective search applications. Obstacles represented by basic geometric shapes to simulate perilous ground terrain are introduced to the search area. Results are presented to show that PSO algorithm based CRS is able to locate targets while avoiding hazardous pathways.
