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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. Techniques have been adopted for multi-robot search from the particle swarm optimization(More)
We present an on-board robotic module which can determine relative positions among miniature robots. The module uses high-frequency modulated infrared emissions to enable nearby robots to determine the range, bearing, and message of the sender with a rapid update rate. A CSMA protocol is employed for scalable operation. We describe a technique for(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)
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)
Swarm intelligence, and swarm robotics in particular, are reaching a point where leveraging the potential of communication within an artificial system promises to uncover newand varied directions for interesting research without compromising the key properties of swarmintelligent 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)
We explore using particle swarm optimization on problems with noisy performance evaluation, focusing on unsupervised robotic learning. We adapt a technique of overcoming noise used in genetic algorithms for use with particle swarm optimization, and evaluate the performance of both the original algorithm and the noise-resistant method for several numerical(More)