scholarly journals Gait Generation and Walk Speed Control for Multi-Legged Robot with Wave CPG Model

2004 ◽  
Vol 22 (2) ◽  
pp. 230-238
Author(s):  
Shinkichi Inagaki ◽  
Hideo Yuasa ◽  
Takanori Suzuki ◽  
Tamio Arai
Keyword(s):  
Speed Control ◽  
Legged Robot ◽  
Walk Speed ◽  
Gait Generation

Wave CPG model for autonomous decentralized multi-legged robot: Gait generation and walking speed control

2006 ◽  
Vol 54 (2) ◽  
pp. 118-126 ◽  
Author(s):  
Shinkichi Inagaki ◽  
Hideo Yuasa ◽  
Takanori Suzuki ◽  
Tamio Arai
Keyword(s):  
Walking Speed ◽  
Speed Control ◽  
Legged Robot ◽  
Gait Generation

Control of a legged robot

2015 ◽  
Vol 24 (1-2) ◽  
pp. 53-57
Author(s):  
Maxime Sadre
Keyword(s):  
Reference Model ◽  
Stabilization Energy ◽  
Main Task ◽  
Legged Robot ◽  
Energy Shaping ◽  
Gait Generation ◽  
System A ◽  
Nonlinear Energy

AbstractThis paper deals with the control of hopping and running systems that interact intermittently with the environment. The control, based on a nonlinear energy reference model, has the main task of conferring to the system, a periodic stable behavior. This approach may be used for gait generation, nominal stance stabilization, energy shaping, and optimization.

scholarly journals Reinforcement Learning for Agents with Many Sensors and Actuators Acting in Categorizable Environments

2005 ◽  
Vol 23 ◽  
pp. 79-122 ◽  
Author(s):  
J. M. Porta ◽  
E. Celaya
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Robots ◽  
Learning Algorithms ◽  
Learning Systems ◽  
Learning Approaches ◽  
Legged Robot ◽  
Sensors And Actuators ◽  
Gait Generation ◽  
Learning Time ◽  
Motor Commands

In this paper, we confront the problem of applying reinforcement learning to agents that perceive the environment through many sensors and that can perform parallel actions using many actuators as is the case in complex autonomous robots. We argue that reinforcement learning can only be successfully applied to this case if strong assumptions are made on the characteristics of the environment in which the learning is performed, so that the relevant sensor readings and motor commands can be readily identified. The introduction of such assumptions leads to strongly-biased learning systems that can eventually lose the generality of traditional reinforcement-learning algorithms. In this line, we observe that, in realistic situations, the reward received by the robot depends only on a reduced subset of all the executed actions and that only a reduced subset of the sensor inputs (possibly different in each situation and for each action) are relevant to predict the reward. We formalize this property in the so called 'categorizability assumption' and we present an algorithm that takes advantage of the categorizability of the environment, allowing a decrease in the learning time with respect to existing reinforcement-learning algorithms. Results of the application of the algorithm to a couple of simulated realistic-robotic problems (landmark-based navigation and the six-legged robot gait generation) are reported to validate our approach and to compare it to existing flat and generalization-based reinforcement-learning approaches.

Gait Generation for Legged Robot by Encoder-Decoder Model

2021 ◽  
Vol 2021 (0) ◽  
pp. 1P1-I07
Author(s):  
Yusaku SEMIYA ◽  
Kyo KUTSUZAWA ◽  
Dai OWAKI ◽  
Mitsuhiro HAYASHIBE
Keyword(s):  
Legged Robot ◽  
Gait Generation

3504 A Study on Gait Generation for Two-Legged Robot during Walking Motion Transition by Optimization Methods

2015 ◽  
Vol 2015.25 (0) ◽  
pp. _3504-1_-_3504-6_
Author(s):  
Kenshiro KATAI ◽  
Takaya YAMAGUCHI ◽  
Garuda FUJII ◽  
Masayuki NAKAMURA
Keyword(s):  
Optimization Methods ◽  
Legged Robot ◽  
Gait Generation ◽  
Walking Motion

Optimal turning gait of a six-legged robot using a GA-fuzzy approach

2000 ◽  
Vol 14 (3) ◽  
pp. 207-219 ◽  
Author(s):  
DILIP KUMAR PRATIHAR ◽  
KALYANMOY DEB ◽  
AMITABHA GHOSH
Keyword(s):  
Stability Margin ◽  
Static Stability ◽  
Legged Robot ◽  
Fuzzy Approach ◽  
Traditional Methods ◽  
Gait Generation ◽  
Minimum Number ◽  
Stable Gait ◽  
Multilegged Robot ◽  
The Stability

This paper describes a new method for generating the turning-gait of a six-legged robot using a combined genetic algorithm (GA)-Fuzzy approach. The main drawback of the traditional methods of gait generation is their high computational load. Thus, there is still a need for the development of a computationally tractable algorithm that can be implemented online to generate stable gait of a multilegged robot. In the proposed genetic-fuzzy system, the fuzzy logic controllers (FLCs) are used to generate the stable gait of a hexapod and a GA is used to improve the performance of the FLCs. The effectiveness of the proposed algorithm is tested on a number of turning-gait generation problems of a hexapod that involve translation as well as rotation of the vehicle. The hexapod will have to take a sharp circular turn (either clockwise or counter-clockwise) with minimum number of ground legs having the maximum average kinematic margin. Moreover, the stability margin should lie within a certain range to ensure static stability of the vehicle. Each leg of a six-legged robot is controlled by a separate FLC and the performance of the controllers is improved by using a GA. It is to be noted that the actual optimization is done off-line and the hexapod can use these optimized FLCs to navigate in real-world scenarios. As an FLC is computationally less expensive, the proposed algorithm will be faster compared with the traditional methods of gait-generation, which include both graphical as well as analytical methods. The GA-tuned FLCs are found to perform better than the author-defined FLCs.

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