Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Autonomous Robots
Multi-robot learning with particle swarm optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
ROBOTRAK: a centralized real-time monitoring, control, and coordination system for robot swarms
Proceedings of the 1st international conference on Robot communication and coordination
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
From swarm intelligence to swarm robotics
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Swarm robotics: from sources of inspiration to domains of application
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Human-robot interaction in rescue robotics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
Stability analysis of social foraging swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient method for segmentation of images based on fractional calculus and natural selection
Expert Systems with Applications: An International Journal
A fuzzified systematic adjustment of the robotic Darwinian PSO
Robotics and Autonomous Systems
A collective robotic architecture in search and rescue scenarios
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Adaptive collective decision-making in limited robot swarms without communication
International Journal of Robotics Research
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This paper presents a survey on multi-robot search inspired by swarm intelligence by further classifying and discussing the theoretical advantages and disadvantages of the existing studies. Subsequently, the most attractive techniques are evaluated and compared by highlighting their most relevant features. This is motivated by the gradual growth of swarm robotics solutions in situations where conventional search cannot find a satisfactory solution. For instance, exhaustive multi-robot search techniques, such as sweeping the environment, allow for a better avoidance of local solutions but require too much time to find the optimal one. Moreover, such techniques tend to fail in finding targets within dynamic and unstructured environments. This paper presents experiments conducted to benchmark five state-of-the-art algorithms for cooperative exploration tasks. The simulated experimental results show the superiority of the previously presented Robotic Darwinian Particle Swarm Optimization (RDPSO), evidencing that sociobiological inspiration is useful to meet the challenges of robotic applications that can be described as optimization problems (e.g., search and rescue). Moreover, the RDPSO is further compared with the best performing algorithms within a population of 14 e-pucks. It is observed that the RDPSO algorithm converges to the optimal solution faster and more accurately than the other approaches without significantly increasing the computational demand, memory and communication complexity.