Biologically inspired approaches to robotics: what can we learn from insects?
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Modelling and Identification in Robotics
Modelling and Identification in Robotics
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics
Proceedings of the Third European Conference on Advances in Artificial Life
Running Across the Reality Gap: Octopod Locomotion Evolved in a Minimal Simulation
Proceedings of the First European Workshop on Evolutionary Robotics
Automatic Generation of Control Programs for Walking Robots Using Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Adaptive Parameterization of Evolutionary Algorithms and Chaotic Populations
Advances in Computational Intelligence and Learning: Methods and Applications
Fault-Tolerant Gait Planning for a Hexapod Robot Walking over Rough Terrain
Journal of Intelligent and Robotic Systems
Modified genetic algorithm strategy for structural identification
Computers and Structures
Learning to coordinate behaviors
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Autonomous evolution of dynamic gaits with two quadruped robots
IEEE Transactions on Robotics
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The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.