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— Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Recently, techniques have been adopted for multi-robot search from the Particle Swarm(More)
Swarm intelligence, and swarm robotics in particular, are reaching a point where leveraging the potential of communication within an artificial system promises to uncover new and varied directions for interesting research without compromising the key properties of swarm-intelligent systems such as self-organization, scalability, and robustness. However, the(More)
— We characterize and improve an existing infrared relative localization/communication module used to find range and bearing between robots in small-scale multi-robot systems. Modifications to the algorithms of the original system are suggested which offer better performance. A mathematical model which accurately describes the system is presented and allows(More)
Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using online learning techniques which allow robots to generate their own controllers online in an automated fashion. In multi-robot systems, robots operating in parallel can potentially learn at a much faster rate by sharing(More)
— Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques which allow robots to evolve their own controllers in an automated fashion. In multi-robot systems, robots learning in parallel can share information to dramatically increase the evolutionary(More)
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(More)
— Simulation is frequently used in the study of multi-agent systems. Unfortunately, in many cases, it is not necessarily clear how faithfully the details of the simulated model represent the behavior of the physical system. Often, the effects of the environment in which the system is to be placed are even neglected entirely. Taking into account the entire(More)
We present a modified version of the Particle swarm Optimization algorithm in which we adjust the virtual swarm search by incorporating inter-agent dynamics native to multi-robot search scenarios. The neighborhood structure of PSO is modified to accurately represent feasible neighborhoods in multiple robot systems with limited communication in several(More)