Chaos: making a new science
Particle swarm-based olfactory guided search
Autonomous Robots
Snap and Spread: A Self-deployment Algorithm for Mobile Sensor Networks
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
A geometric approach to deploying robot swarms
Annals of Mathematics and Artificial Intelligence
Distributed Constraint Reasoning Applied to Multi-robot Exploration
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Making networked robots connectivity-aware
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Ad-hoc wireless network coverage with networked robots that cannot localize
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Bio-inspired algorithms for autonomous deployment and localization of sensor nodes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment
International Journal of Robotics Research
From swarm intelligence to swarm robotics
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Deployment of mobile robots with energy and timing constraints
IEEE Transactions on Robotics
Hi-index | 0.00 |
In most real multi-robot applications, such as search-and-rescue, cooperative robots have to move to complete their tasks while maintaining communication among themselves without the aid of a communication infrastructure. However, initially deploying and ensuring a mobile ad-hoc network in real and complex environments is an arduous task since the strength of the connection between two nodes (i.e., robots) can change rapidly in time or even disappear. An extension of the Particle Swarm Optimization to multi-robot applications has been previously proposed and denoted as Robotic Darwinian PSO (RDPSO). This paper contributes with a further extension of the RDPSO, thus integrating two research aspects: (i) an autonomous, realistic and fault-tolerant initial deployment strategy denoted as Extended Spiral of Theodorus (EST); and (ii) a fault-tolerant distributed search to prevent communication network splits. The exploring agents, denoted as scouts, are autonomously deployed using supporting agents, denoted as rangers. Experimental results with 15 physical scouts and 3 physical rangers show that the algorithm converges to the optimal solution faster and more accurately using the EST approach over the random deployment strategy. Also, a more fault-tolerant strategy clearly influences the time needed to converge to the final solution, but is less susceptible to robot failures.