• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.389-394
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• 2014

Two evolutionary gait generation methods for Cartesian and Joint coordinates space are compared to develop a fast locomotion for quadruped robots. GA(Genetic Algorithm) based approaches seek to optimize a pre-selected set of parameters for the locus of paw and initial position in cartesian coordinates space. GP(Genetic Programming) based technique generate few joint trajectories using symbolic regression in joint coordinates space as a form of polynomials. Optimization for two proposed methods are executed using Webots simulation for the quadruped robot which is built by Bioloid. Furthermore, simulation results for two proposed methods are analysed in terms of different coordinate spaces.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.749-754
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• 2009

Two gait generation methods using GP(genetic programming) and CPG(Central Pattern Generator) are compared to develop a fast locomotion for quadruped robot. GP based technique is an effective way to generate few joint trajectories instead of the locus of paw positions and lots of stance parameters. The CPGs are neural circuits that generate oscillatory output from a input coming from the brain. Optimization for two proposed methods are executed and analysed using Webots simulation for the quadruped robot which is built by Bioloid. Furthermore, simulation results for two proposed methods are experimented in real quadruped robot and performances and motion features of GP and CPG based methods are investigated.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.843-848
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• 2007

This paper introduced an automatic generation method of falling motions for humanoid robots to minimize a damage. The proposed approach used a GA based optimization technique to find a set of joint trajectories which minimize a damage of the falling over and down. A couple of fitness functions are provided to generate various falling motions. To verify the proposed method, experiments for falling motions were executed for Sony QRIO robot in Webots simulation environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.132-137
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• 2011

The walking mobility with stability of 4 legged robots is the distinguished skills for many application areas. Planning gaits of efficient walking for quadruped robots is an important and challenging task. Especially, autonomous generation of locomotion is required to manage various robot models and environments. In this paper, we propose an adaptive locomotion control of 4 legged robot for irregular terrain using HyperNEAT. Generated locomotion is executed and analysed using ODE based Webots simulation for the 4 legged robot which is built by Bioloid.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.603-609
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• 2010

This paper introduces an objects recognition algorithm based on SURF(Speeded Up Robust Features) and GP(Genetic Programming) based gaits generation. Combining both methods, a recognition based intelligent walking for quadruped robots is proposed. The gait of quadruped robots is generated by means of symbolic regression for each joint trajectories using GP. A position and size of target object are recognized by SURF which enables high speed feature extraction, and then the distance to the object is calculated. Experiments for objects recognition and autonomous walking for quadruped robots are executed for ODE based Webots simulation and real robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.661-666
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• 2009

Modular snake-like robots, which consist of series of modules, are robust for failure and have flexible locomotions for environment. However, they are difficult to control and few efficient and various locomotions are introduced yet. In this paper, GA based phase generation and trajectory generation approaches are implemented and compared for locomotion of snake-like robots and extended for analysis for selections of partial modules. In addition, modeling and simulation environments are implemented in Webots simulator and above GA based experiments for locomotion are executed for KMC snake-like robot.

• Journal of IKEEE
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• v.25 no.2
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• pp.337-343
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• 2021

Deep reinforcement learning is an artificial intelligence algorithm that enables learners to select optimal behavior based on raw and, high-dimensional input data. A lot of research using this is being conducted to create an optimal movement path of a mobile robot in an environment in which obstacles exist. In this paper, we selected the Dueling Double DQN (D3QN) algorithm that uses the prioritized experience replay to create the moving path of mobile robot from the image of the complex surrounding environment. The virtual environment is implemented using Webots, a robot simulator, and through simulation, it is confirmed that the mobile robot grasped the position of the obstacle in real time and avoided it to reach the destination.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.280-285
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• 2009

This paper introduced an automatic generation method of falling motions for humanoid robots to minimize a damage. The proposed approach used a PGA based optimization technique to find a set of joint trajectories which minimize a damage of the falling over and down. Injection-migration PGA technique is introduced and compared with EMO and various migration topologies. To verify the proposed method, experiments for falling motions were executed for Sony QRIO robot in Webots simulation environments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.663-668
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• 2018

A combined CPG (Central Pattern Generator) based foot trajectory and GA (Genetic Algorithm) based joint compensation method is presented for adaptive humanoid walking. In order to increase an adaptability of humanoid walking for rough terrains, the experiment for rolling terrains are introduced. The CPG based foot trajectory method has been successfully applied to basic slops and variable slops, but has a limitation for the rolling terrains. The experiments are conducted in an ODE based Webots simulation environment using humanoid robot Nao to verify a stability of walking for various rolling terrains. The proposed method is compared to the previous CPG foot trajectory technique and shows better performance especially for the cascade rolling terrains.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.751-758
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• 2016

The modifiable walking pattern generation (MWPG) algorithm can handle dynamic walking commands by changing the walking period, step length, and direction independently. When an infeasible command is given, the algorithm changes the command to a feasible one. After the feasibility of the navigational command is checked, it is translated into the desired center of mass (CM) state. To achieve the desired CM state, a reference CM trajectory is generated using predefined zero moment point (ZMP) functions. Based on the proposed algorithm, various complex walking patterns were generated, including backward and sideways walking. The effectiveness of the patterns was verified in dynamic simulations using the Webots simulator.