Automatic Generation of Control Programs for Walking Robots Using Genetic Programming

Busch, Jens; Ziegler, Jens; Aue, Christian; Ross, Andree; Sawitzki, Daniel; Banzhaf, Wolfgang

doi:10.1007/3-540-45984-7_25

Cited by 32 publications

(15 citation statements)

References 6 publications

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“…Busch et al (2002) observed some gaits that cannot found their counterparts in the nature; however, these gaits were slow and not useful for a real robot. This problem is mostly related to a complex state space, in which the search for a solution may be sub-optimal.…”

Section: Related Workmentioning

confidence: 99%

A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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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.

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Section: Related Workmentioning

confidence: 99%

A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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“…GP is an emerging field in evolutionary computing and machine learning and has already been applied to many tasks, including image analysis [13], object detection [16], regression problems [8] and even control programs for walking robots [3]. GP has been very successful in solving or performing these tasks and "now routinely delivers high-return human-competitive machine intelligence" [9].…”

Section: Introductionmentioning

confidence: 99%

Algebraic simplification of GP programs during evolution

Wong

Zhang

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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Program bloat is a fundamental problem in the field of Genetic Programming (GP). Exponential growth of redundant and functionally useless sections of programs can quickly overcome a GP system, exhausting system resources and causing premature termination of the system before an acceptable solution can be found. Simplification is an attempt to remove such redundancies from programs. This paper looks at the effects of applying an algebraic simplification algorithm to programs during the GP evolution. The GP system with the simplification is examined and compared to a standard GP system on four regression and classification problems of varying difficulty. The results suggest that the GP system employing a simplification component can achieve superior efficiency and effectiveness to the standard system on these problems.

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“…In recent years, the development of the study tend to adopt knowledge evolution method, through evolutionary algorithm to automatically generate the appropriate application logic [1] [2][3] [4]. In this paper, using genetic programming (GP) evolution control algorithm for robot path planning.…”

Section: Introductionmentioning

confidence: 99%

Method of robot route control with genetic programming

Peng¹,

YuHan²

2015

Advances in Computer Science Research

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Abstract. This paper studies the robot path planning method,which is suitable for the complex environment. First of all, to abstract modeling of robot's work environment, set up the hexagonal lattice model of environment, every hexagonal has a cost value; This model meets the requirement of describing the different environment; through knowledge evolutionary algorithm based on genetic programming (GP) to get the optimal route choice; at last, through example show that the proposed method is effective.

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Automatic Generation of Control Programs for Walking Robots Using Genetic Programming

Cited by 32 publications

References 6 publications

A biologically inspired approach to feasible gait learning for a hexapod robot

A biologically inspired approach to feasible gait learning for a hexapod robot

Algebraic simplification of GP programs during evolution

Method of robot route control with genetic programming

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