A multirobot platform based on autonomous surface and underwater vehicles with bio-inspired neurocontrollers for long-term oil spills monitoring

This paper describes the BUSCAMOS-Oil monitoring system, which is a robotic platform consisting of an autonomous surface vessel combined with an underwater vehicle. The system has been designed for the long-term monitoring of oil spills, including the search for the spill, and transmitting information on its location, extent, direction and speed. Both vehicles are controlled by two different types of bio-inspired neural networks: a Self-Organization Direction Mapping Network for trajectory generation and a Neural Network for Avoidance Behaviour for avoiding obstacles. The systems’ resilient capabilities are provided by bio-inspired algorithms implemented in a modular software architecture and controlled by redundant devices to give the necessary robustness to operate in the difficult conditions typically found in long-term oil-spill operations. The efficacy of the vehicles’ adaptive navigation system and long-term mission capabilities are shown in the experimental results.This work was partially supported by the BUSCAMOS Project (ref. 1003211003700) under the program DN8644 COINCIDENTE of the Spanish Defense Ministry, the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia-19895/GERM/15)”, and the Spanish Government’s cDrone (ref. TIN2013-45920-R) and ViSelTR (ref. TIN2012-39279) projects

Emergence of leadership in a robotic fish group under diverging individual personality traits

Variations of individual’s personality traits have been identified before as one of the possible mechanisms for the emergence of leadership in an interactive collective, which may lead to benefits for the group as a whole. Complementing the large number of existing literatures on using simulation models to study leadership, we use biomimetic robotic fish to gain insight into how the fish’s behaviours evolve under the influence of the physical hydrodynamics. In particular, we focus in this paper on understanding how robotic fish’s personality traits affect the emergence of an effective leading fish in repeated robotic foraging tasks when the robotic fish’s strategies, to push or not to push the obstacle in its foraging path, are updated over time following an evolutionary game set-up. We further show that the robotic fish’s personality traits diverge when the group carries out difficult foraging tasks in our experiments, and self-organization takes place to help the group to adapt to the level of difficulties of the tasks without inter-individual communication