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A collective cleanup task requires a multi-robot system to search for randomly distributed targets and remove them under a dynamic environment. In traditional methods, robots wandered in subareas (which caused too much repeat search) and interchanged all detected information with their neighbors, so global searching time and communication traffic increased. In this paper, we propose a swarm intelligence...
We present a novel scheme for coordinated search by autonomous agents that is inspired by collective forms of intelligence present in group predators. In the scheme proposed, a group of agents is tasked to find several targets over a search area. The sensing technology is imperfect so there are non-negligible probabilities for false positives and false negatives. Agents maintain two data `trails'...
This paper revisits a traditional Ant Foraging algorithm and proposes a Cluster-based Softbots algorithm to address the performance issues caused by constraints of random autonomous search featured in most swarm intelligence-based algorithms. A simple hierarchy is introduced to regulate the unfolding of dynamically changing swarm-like behaviors. Comparative experiments for Ant Foraging and the proposed...
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