Unmanned Vehicle Navigation Using Swarm Intelligence

Unmanned vehicles are used to explore physical areas where humans are unable to go due to different constraints. There have been various algorithms that have been used to perform this task. This paper explores swarm intelligence for searching a given problem space for a particular target(s). The work in this paper has two parts. In the first part, a set of randomized unmanned vehicles are deployed to locate a single target. In the second part, the randomized unmanned vehicles are deployed to locate various targets and are then converged at one of targets of a particular interest. Each of the targets carries transmits some information which draws the attention of the randomized unmanned vehicles to the target of interest. The Particle Swarm Optimization (PSO) has been applied for solving this problem. Results have shown that the PSO algorithm converges the unmanned vehicles to the target of particular interest successfully and quickly

Navigation of Mobile Sensors Using PSO and Embedded PSO in a Fuzzy Logic Controller

This paper presents novel structures for optimization and communication of a swarm of mobile sensors or robots for maximizing local and global tasks such as firefighting, landmine detection, radioactivity detection, etc. The navigation of the sensors is carried out using two strategies. The first strategy is based on particle swarm optimization (PSO) and the second strategy is based on a swarm of fuzzy logic based controllers. In addition, the membership functions and the rules of the fuzzy logic controller (FLC) are optimized using the PSO algorithm. Navigation of mobile sensors is considered in this paper to locate desirable target sources in a given sensing area. Both approaches presented do not depend on the number of target sources. Results are provided for target locations based on a PSO, a swarm of fuzzy logic controllers and a swarm of optimized fuzzy logic controllers

Improving the Performance of Particle Swarm Optimization Using Adaptive Critics Designs

Swarm intelligence algorithms are based on natural behaviors. Particle swarm optimization (PSO) is a stochastic search and optimization tool. Changes in the PSO parameters, namely the inertia weight and the cognitive and social acceleration constants, affect the performance of the search process. This paper presents a novel method to dynamically change the values of these parameters during the search. Adaptive critic design (ACD) has been applied for dynamically changing the values of the PSO parameters

Optimal PSO for Collective Robotic Search Applications

Unmanned vehicles/mobile robots are of particular interest in target tracing applications since there are many areas where a human cannot explore. Different means of control have been investigated for unmanned vehicles with various algorithms like genetic algorithms, evolutionary computations, neural networks etc. This work presents the application of particle swarm optimization (PSO) for collective robotic search. The performance of the PSO algorithm depends on various parameters called quality factors and these parameters are determined using a secondary PSO. Results are presented to show that the performance of PSO algorithm and search is improved for a single and multiple target searches

Computational intelligence techniques for collective robotic search

Over the years various applications involving robots have been developed. Target tracing has been one such application and numerous methods have been explored for its implementation. Collective Robotic Search (CRS) is a target tracing application where a team of small, inexpensive and dispensable robots are used to carry out the task. Different Computational Intelligence (CI) techniques have been explored for the navigation of the robots in this thesis --Abstract, page iii

Collective Robotic Search Using Hybrid Techniques: Fuzzy Logic and Swarm Intelligence Inspired by Nature

This paper presents two new strategies for navigation of a swarm of robots for target/mission focused applications including landmine detection and firefighting. The first method presents an embedded fuzzy logic approach in the particle swarm optimization (PSO) algorithm robots and the second method presents a swarm of fuzzy logic controllers, one on each robot. The framework of both strategies has been inspired by natural swarms such as the school of fish or the flock of birds. In addition to the target search using the above methods, a hierarchy for the coordination of a swarm of robots has been proposed. The robustness of both strategies is evaluated for failures or loss in swarm members. Results are presented with both strategies and comparisons of their performance are carried out against a greedy search algorithm