Authors:
Tiago Rodrigues
;
Miguel Duarte
;
Sancho Oliveira
and
Anders Lyhne Christensen
Affiliation:
Instituto de Telecomunicações, Instituto Universitário de Lisboa (ISCTE-IUL) and BioMachines Lab, Portugal
Keyword(s):
Swarm Robotics, Evolutionary Robotics, Situated Communication, Local Collective Sensing
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Cooperation and Coordination
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Reactive AI
;
Robot and Multi-Robot Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The constituent robots in swarm robotics systems are typically equipped with relatively simple, onboard sensors
of limited quality and range. When robots have the capacity to communicate with one another, communication
has so far been exclusively used for coordination. In this paper, we present a novel approach in which
local, situated communication is leveraged to overcome the sensory limitations of the individual robots. In
our approach, robots share sensory inputs with neighboring robots, thereby effectively extending each other’s
sensory capabilities. We evaluate our approach in a series of experiments in which we evolve controllers for
robots to capture mobile preys. We compare the performance of (i) swarms that use our approach, (ii) swarms
in which robots use only their limited onboard sensors, and (iii) swarms in which robots are equipped with
ideal sensors that provide extended sensory capabilities without the need for communication. Our results show
that swarms in which loc
al communication is used to extend the sensory capabilities of the individual robots
outperform swarms in which only onboard sensors are used. Our results also show that in certain experimental
configurations, the performance of swarms using our approach is close to the performance of swarms with
ideal sensors.
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