Fuzzy Logic in Collective Robotic Search

One important application of mobile robots is searching a geographical region to locate the origin of a specific sensible phenomenon. We first propose a fuzzy logic approach using a decision table. A novel fuzzy rule based was designed. And then a fuzzy search strategy is adopted by utilizing the three tier centers of mass coordination. Experimental results show that fuzzy logic algorithm is an efficient approach for the collective robots to locate the target source. In addition, noise and the position of the target affect the searching result

A Comparison of Dual Heuristic Programming (DHP) and Neural Network Based Stochastic Optimization Approach on Collective Robotic Search Problem

An important application of mobile robots is searching a region to locate the origin of a specific phenomenon. A variety of optimization algorithms can be employed to locate the target source, which has the maximum intensity of the distribution of some detected function. We propose two neural network algorithms: stochastic optimization algorithm and dual heuristic programming (DHP) to solve the collective robotic search problem. Experiments were carried out to investigate the effect of noise and the number of robots on the task performance, as well as the expenses. The experimental results showed that the performance of the dual heuristic programming (DHP) is better than the stochastic optimization method

Distributed Adaptation in Multi-Robot Search using Particle Swarm Optimization

We present an adaptive strategy for a group of robots engaged in the localization of multiple targets. The robotic search algorithm is inspired by chemotaxis behavior in bacteria, and the algorithmic parameters are updated using a distributed implementation of the Particle Swarm Optimization technique. We explore the efficacy of the adaptation, the impact of using local fitness measurements to improve global fitness, and the effect of different particle neighborhood sizes on performance. The robustness of the approach in non-static environments is tested in a time-varying scenario

Collective search by mobile robots using alpha-beta coordination

One important application of mobile robots is searching a geographical region to locate the origin of a specific sensible phenomenon. Mapping mine fields, extraterrestrial and undersea exploration, the location of chemical and biological weapons, and the location of explosive devices are just a few potential applications. Teams of robotic bloodhounds have a simple common goal; to converge on the location of the source phenomenon, confirm its intensity, and to remain aggregated around it until directed to take some other action. In cases where human intervention through teleoperation is not possible, the robot team must be deployed in a territory without supervision, requiring an autonomous decentralized coordination strategy. This paper presents the alpha beta coordination strategy, a family of collective search algorithms that are based on dynamic partitioning of the robotic team into two complementary social roles according to a sensor based status measure. Robots in the alpha role are risk takers, motivated to improve their status by exploring new regions of the search space. Robots in the beta role are motivated to improve but are conservative, and tend to remain aggregated and stationary until the alpha robots have identified better regions of the search space. Roles are determined dynamically by each member of the team based on the status of the individual robot relative to the current state of the collective. Partitioning the robot team into alpha and beta roles results in a balance between exploration and exploitation, and can yield collective energy savings and improved resistance to sensor noise and defectors. Alpha robots waste energy exploring new territory, and are more sensitive to the effects of ambient noise and to defectors reporting inflated status. Beta robots conserve energy by moving in a direct path to regions of confirmed high status