Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive exhibit unique characteristic in that they are optimized without control central consciousness. Inspired by this phenomenon, we present an exploration navigation strategy for swarm robots, which exploits cooperation...

Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots ...

Foraging is a common benchmark problem in collective robotics in which a robot (the forager) explores a given environment while collecting items for further deposition at specific locations. A typical real-world application of foraging is garbage collection where robots collect garbage for further disposal in pre-defined locations. This work proposes a method to cooperatively perform the task o...

Many application domains require search and retrieval, which is also known in the robotic domain as foraging. An example domain is search and rescue where a disaster area needs to be explored and transportation of survivors to a safe area needs to be arranged. Performing these tasks by more than one robot increases performance if tasks are allocated and executed efficiently. In this paper, we s...

This work demonstrates the application of the distributed behavior-based approach [1] to generating a multi-robot controller for a group of mobile robots performing a clean-up and collection task. The paper studies a territorial approach to the task in which the robots are assigned individual territories that can be dynamically resized if one of the robots malfunctions, permitting the completio...

We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation ...

We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation ...

This work addresses the problem of coordinating a team of mobile robots such that they form a connected ad-hoc wireless network while addressing task objectives. Many tasks, such as exploration or foraging, can be performed more efficiently when robots are able to communicate with each other. All or parts of these tasks can be performed in parallel, thus multiple robots can complete the task mo...

This paper presents an optimization technique inspired from the collective foraging observed in natural insects, swarm robotics is a new approach to coordinate the behaviors of large number of relatively simple robots in decentralized manner. In such robotic systems, an individual robot have only local perception and very limited capabilities in terms of sensing, computation, and communication ...

This article presents a simple adaptation mechanism to automatically adjust the ratio of foragers to resters (division of labor) in a swarm of foraging robots and hence maximize the net energy income to the swarm. Three adaptation rules are introduced based on local sensing and communications. Individual robots use internal cues (successful food retrieval), environmental cues (collisions with t...