The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gai...The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gait in a virtual environment was presented in previous research work titled “A Comparison of PPO, TD3, and SAC Reinforcement Algorithms for Quadruped Walking Gait Generation”. We demonstrated that the Soft Actor-Critic Reinforcement algorithm had the best performance generating the walking gait for a quadruped in certain instances of sensor configurations in the virtual environment. In this work, we present the performance analysis of the state-of-the-art Deep Reinforcement algorithms above for quadruped walking gait generation in a physical environment. The performance is determined in the physical environment by transfer learning augmented by real-time reinforcement learning for gait generation on a physical quadruped. The performance is analyzed on a quadruped equipped with a range of sensors such as position tracking using a stereo camera, contact sensing of each of the robot legs through force resistive sensors, and proprioceptive information of the robot body and legs using nine inertial measurement units. The performance comparison is presented using the metrics associated with the walking gait: average forward velocity (m/s), average forward velocity variance, average lateral velocity (m/s), average lateral velocity variance, and quaternion root mean square deviation. The strengths and weaknesses of each algorithm for the given task on the physical quadruped are discussed.展开更多
针对冯屯500 k V可控串补站在区内瞬时性单相接地故障过程中的火花间隙系自触发问题,笔者根据火花间隙的结构和原理,结合伊冯甲线可控串补保护动作和录波数据,分析了火花间隙的自触发原因,结果表明,火花间隙的自触发与火花间隙元件本身...针对冯屯500 k V可控串补站在区内瞬时性单相接地故障过程中的火花间隙系自触发问题,笔者根据火花间隙的结构和原理,结合伊冯甲线可控串补保护动作和录波数据,分析了火花间隙的自触发原因,结果表明,火花间隙的自触发与火花间隙元件本身和环境因素有关,伊冯甲线可控串补火花间隙自触发属于误触发。同时,为保证伊冯甲线可控串补系统安全运行,提出对投运火花间隙进行定期维检建议。展开更多
文摘The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gait in a virtual environment was presented in previous research work titled “A Comparison of PPO, TD3, and SAC Reinforcement Algorithms for Quadruped Walking Gait Generation”. We demonstrated that the Soft Actor-Critic Reinforcement algorithm had the best performance generating the walking gait for a quadruped in certain instances of sensor configurations in the virtual environment. In this work, we present the performance analysis of the state-of-the-art Deep Reinforcement algorithms above for quadruped walking gait generation in a physical environment. The performance is determined in the physical environment by transfer learning augmented by real-time reinforcement learning for gait generation on a physical quadruped. The performance is analyzed on a quadruped equipped with a range of sensors such as position tracking using a stereo camera, contact sensing of each of the robot legs through force resistive sensors, and proprioceptive information of the robot body and legs using nine inertial measurement units. The performance comparison is presented using the metrics associated with the walking gait: average forward velocity (m/s), average forward velocity variance, average lateral velocity (m/s), average lateral velocity variance, and quaternion root mean square deviation. The strengths and weaknesses of each algorithm for the given task on the physical quadruped are discussed.
文摘针对冯屯500 k V可控串补站在区内瞬时性单相接地故障过程中的火花间隙系自触发问题,笔者根据火花间隙的结构和原理,结合伊冯甲线可控串补保护动作和录波数据,分析了火花间隙的自触发原因,结果表明,火花间隙的自触发与火花间隙元件本身和环境因素有关,伊冯甲线可控串补火花间隙自触发属于误触发。同时,为保证伊冯甲线可控串补系统安全运行,提出对投运火花间隙进行定期维检建议。
