Gait Control Generation for Physically Based Simulated Robots Using Genetic Algorithms (original) (raw)

Applying genetic algorithms to control gait of physically based simulated robots

… , 2006. CEC 2006. IEEE Congress on, 2006

This paper describes our studies in the legged robots research area and the development of the LegGen System, that is used to automatically create and control stable gaits for legged robots into a physically based simulation environment. The parameters used to control the robot are optimized using genetic algorithms (GA). Comparisons between different robot models and fitness functions were accomplished, indicating how to compose a better multi-criterion fitness function to be used in the gait control of legged robots. The best gait control solution and the best robot model were selected in order to help us to build a real robot. The results also showed that it is possible to generate stable gaits using GA in an efficient manner.

Applying Genetic Algorithms to Control Gait of Simulated Robots

2007

This paper describes the LegGen simulator, used to automatically create and control stable gaits for legged robots into a physically based simulation environment. In our approach, the gait is defined using two different methods: a finite state machine based on robot's leg joint angles sequences; and a recurrent neural network. The parameters for both methods are optimized using genetic algorithms. The model validation was performed by several experiments realized with a robot simulated using the ODE physical simulation engine. The results showed that it is possible to generate stable gaits using genetic algorithms in an efficient manner, using these two different methods.

Evolving Gait Control of Physically Based Simulated Robots

This paper describes the LegGen System, used to automatically create and control stable gaits for legged robots into a physically based simulation environment. In our approach, the gait is defined using three different methods: a finite state machine based on robot's leg joint angles sequences; a locus based gait defining the endpoint (paw) trajectory and using inverse kinematics to determine joint angles; and through a half ellipse cyclic function used to define each endpoint trajectory. The parameters used to control the robot through these methods are optimized using Genetic Algorithms. The model validation was performed by several experiments realized with different configurations of four and six legged robots, simulated using the ODE physical simulation engine. A comparison between these different robot configurations and control methods was realized, and the best solution was selected in order to help us to build a physical legged robot. The results also showed that it is possible to generate stable gaits using Genetic Algorithms in a efficient manner, using these three different methods.

Legged robot gait locus generation based on genetic algorithms

Proceedings of the 2006 international symposium on Practical cognitive agents and robots - PCAR '06, 2006

Achieving an effective gait locus for legged robots is a challenging task. It is often done manually in a laborious way due to the lack of research in automatic gait locus planning. Bearing this problem in mind, this article presents a gait locus planning method using inverse kinematics while incorporating genetic algorithms. Using quadruped robots as a platform for evaluation, this method is shown to generate a good gait locus for legged robots.

Using genetic algorithms to establish efficient walking gaits for an eight-legged robot

International Journal of Systems Science, 2001

In the design and development of a legged robot, many factors need to be considered. As a consequence, creating a legged robot that can eYciently and autonomousl y negotiate a wide range of terrains is a challenging task. Many researchers working in the area of legged robotics have traditionally looked towards the natural world for inspiration and solutions, reasoning that these evolutionary solutions are appropriate and eVective because they have passed the hard tests for survival over time and generations. This paper reports the use of genetically inspired learning strategies, commonly referred to as genetic algorithms, as an evolutionary design tool for improving the design and performance of an algorithm for controlling the leg stepping sequences of a walking robot. The paper presents a speci® c case of ® nding optimal walking gaits for an eightlegged robot called Robug IV and simulated results are provided.

Morphology and Gait Control Evolution of Legged Robots

2008

This paper describes our research and experiments with autonomous robots, in which were used genetic algorithms to evolve stable gaits of simulated legged robots in a physically based simulation environment. In our approach, the gait is defined using a finite state machine based on the joint angles of the robot legs, and the parameters are optimized using genetic algorithms. The proposed model also allows the evolution of the robot body morphology. The model validation was performed by several experiments and a valid statistical analysis, and the results show that it is possible to generate fast and stable gaits using genetic algorithms in an efficient manner.

GENETIC ALGORITHMS FOR GAIT SYNTHESIS IN A HEXAPOD ROBOT

World Scientific Series in Robotics and Intelligent Systems, 1994

This paper describes the staged evolution of a complex motor pattern generator (CPG) for the control of the leg movements of a six-legged walking robot. The CPG is composed of a network of neurons. In contrast to the main stream work in neural networks, the interconnection weights are altered by a Genetic Algorithm (GA), rather than a learning algorithm. Staged evolution is used to improve the convergence rate of the algorithm, thus obtaining rapid evolution of behavior toward a goal set. First, an oscillator for the individual leg movements is evolved. Then, a network of these oscillators is evolved to coordinate the movements of the different legs. In this way, the designer specifies "islands of fitness" on the way to the final goal, rather than using a single fitness function or determining the explicit solution to the control problem. By introducing a staged set of manageable challenges, the algorithm's performance is improved.

A Review of Gait Optimization Based on Evolutionary Computation

Applied Computational Intelligence and Soft Computing, 2010

Gait generation is very important as it directly affects the quality of locomotion of legged robots. As this is an optimization problem with constraints, it readily lends itself to Evolutionary Computation methods and solutions. This paper reviews the techniques used in evolution-based gait optimization, including why Evolutionary Computation techniques should be used, how fitness functions should be composed, and the selection of genetic operators and control parameters. This paper also addresses further possible improvements in the efficiency and quality of evolutionary gait optimization, some problems that have not yet been resolved and the perspectives for related future research.

Genetic algorithm for walking robots motion optimization

2011

The paper presents a method to determine the optimal solution regarding the movement done by the leg of a hexapod walking robot with three degrees of freedom, with higher positioning precision taking into account the importance of three factors (Precision, Movement and Friction). To find the coefficients that establish the order of these factors we use an Analytical Hierarchy Process. With these coefficients we build the fitness function for a genetic algorithm that is slightly modified (instead of two chromosomes crossed, we crossed three) in order to obtain a diversity in the exchanged information. The obtained results prove that, applying the genetic algorithm in correlation with the analytic hierarchy method of processing, we can optimize the precision in the robot movement with a relative small increase of the consumed energy.

On-line stable gait generation of a two-legged robot using a genetic-fuzzy system

Robotics and Autonomous Systems, 2005

Gait generation for legged vehicles has since long been considered as an area of keen interest by the researchers. Soft computing is an emerging technique, whose utility is more stressed, when the problems are ill-defined, difficult to model and exhibit large scale solution spaces. Gait generation for legged vehicles is a complex task. Therefore, soft computing can be applied to solve it. In this work, gait generation problem of a two-legged robot is modeled using a fuzzy logic controller (FLC), whose rule base is optimized offline, using a genetic algorithm (GA). Two different GA-based approaches (to improve the performance of FLC) are developed and their performances are compared to that of manually constructed FLC. Once optimized, the FLCs will be able to generate dynamically stable gait of the biped. As the CPU-time of the algorithm is found to be only 0.002 s in a P-III PC, the algorithm is suitable for on-line (real-time) implementations.