Evolving Error Tolerance in Biologically-Inspired iAnt Robots

@inproceedings{Hecker2013EvolvingET,
  title={Evolving Error Tolerance in Biologically-Inspired iAnt Robots},
  author={Joshua P. Hecker and Karl Stolleis and Bjorn Swenson and Kenneth Letendre and Melanie E. Moses},
  booktitle={ECAL},
  year={2013}
}
Evolutionary algorithms can adapt the behavior of individuals to maximize the fitness of cooperative multi-agent teams. We use a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants, then transfer the evolved behaviors into physical iAnt robots. We introduce positional and resource detection error models into our simulation to characterize the empirically-measured sensor error in our physical robots. Physical and simulated robots that live in a… 
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References

SHOWING 1-10 OF 37 REFERENCES
An evolutionary approach for robust adaptation of robot behavior to sensor error
TLDR
This work uses a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants, emulating the sensor error characterized by the physical iAnt robot platform.
Evolving mobile robots in simulated and real environments
TLDR
By evolving neural controllers for a Khepera robot in computer simulations and then transferring the agents obtained to the real environment, it is shown that an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot.
Embodied Evolution: Distributing an evolutionary algorithm in a population of robots
TLDR
Embodied Evolution is introduced as a new methodology for evolutionary robotics that uses a population of physical robots that autonomously reproduce with one another while situated in their task environment and designs a fully decentralized, asynchronous evolutionary algorithm.
Evolution of neural controllers for competitive game playing with teams of mobile robots
TLDR
This research emphasized the development of methods to automate the production of behavioral robot controllers that do not require a human designer to define specific intermediate behaviors for a complex robot task.
Formica ex Machina: Ant Swarm Foraging from Physical to Virtual and Back Again
TLDR
This work demonstrates the feasibility of programming large robot teams for collective tasks such as retrieval of dispersed resources, mapping, and environmental monitoring and lays a foundation for evolving collective search algorithms in silico and then implementing those algorithms in machina in robust and scalable robotic swarms.
Artificial Life and Real Robots
TLDR
A new abstraction for behavior-based robot programming which is specially tailored to be used with genetic programming techniques is introduced, which will be necessary to automatically evolve programs that are one to two orders of magnitude more complex than those previously reported in any domain.
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics
TLDR
It has been demonstrated that it is possible to develop successful robot controllers in simulation that generate almost identical behaviours in reality, at least for a particular class of robot-environment interaction dynamics.
ARGoS: a Pluggable, Multi-Physics Engine Simulator for Heterogeneous Swarm Robotics
We present a novel robot simulator called ARGoS. The main focus of ARGoS is the real-time simulation of massive heterogeneous swarms of robots. In contrast to existing robot simulators, which obtain
How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics
A methodology for evolving the control systems of autonomous robots has not yet been well established. In this paper we will show different examples of applications of evolutionary robotics to real
ALLIANCE: an architecture for fault tolerant multirobot cooperation
  • L. Parker
  • Engineering, Computer Science
    IEEE Trans. Robotics Autom.
  • 1998
TLDR
This software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robotteam that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention.
...
1
2
3
4
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