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| 1 | CPG Control for Biped Hopping Robot in Unpredictable Environment显示文摘 | Tingting Wang Wei Guo Mantian Li Fusheng Zha Lining Sun | 2012 | Journal of Bionic Engineering2012，9，1： | 17 |
| 2 | A Parallel Actuated Pantograph Leg for High-speed Locomotion显示文摘高速度的跑是在结构的设计和控制上要求严格的限制的 legged 机器人的地里的最重要的话题之一。为了解决高加速,高精力消费,高步频率和地面的问题,在高速度的运动期间影响,这份报纸论述一条平行激活的缩放仪腿与一近似 decoupled 配置。在轻重量,高负担能力,高机械的效率和结构的稳定性的明白表示的腿特征。在控制点和脚之间的力量和位置的类似特征被分析。使动态表演担心,被腿惯性的全面评估小心地优化并且集中在最大的脚轨道以内的关键设计参数, K 1 和 K 2, 。合并装载转换摆钟(SLIP ) 模型和活跃力量也是的虚拟春天的控制策略建议测试设计。没有机械弹簧,策略能实现高度灵活的阻抗,它实质地简化设计并且在高速度的跑期间满足可变僵硬要求。结构的合理性和控制法律的有效性被模拟和实验验证。 | Wei Guo Changrong Cai Mantian Li Fusheng Zha Pengfei Wang Kenan Wang | 2017 | Journal of Bionic Engineering2017，14，2： | 2 |
| 3 | Force-controlled Compensation Scheme for P-Q Valve-controlled Asymmetric Cylinder used on Hydraulic Quadruped Robots显示文摘Under the requirement of the force controller of hydraulic quadruped robots,the goal of this work is to accurately track the force commands at the level of the hydraulic drive unit.The main contribution focuses on the development of a force-controlled compensation scheme,which is specifically aimed at the key issues affecting the hydraulic quadrupedal locomotion.With this idea,based on a P-Q valve-controlled asymmetric cylinder,we first establish a mathematical model for the hydraulic drive unit force control system.With the desired force commands,a force feed-forward algorithm is presented to improve the dynamic performance of the system.Meanwhile,we propose a disturbance compensation algorithm to reduce the influence induced by external disturbances due to foot-ground impacts.Afterwards,combining with a variable gain PI controller,a series of experiments are implemented on a force control performance test platform to verify the proposed scheme.The results demonstrate that the force-controlled compensation scheme has the ability to notably improve the force tracking accuracy,reduce the response time and redundant force. | Yapeng Shi Mantian Li Fusheng Zha Lining Sun Wei Guo Cong Ma Zhibin Li | 2020 | Journal of Bionic Engineering2020，17，6： | 1 |
| 4 | Design and analysis of a whole-body controller for a velocity controlled robot mobile manipulator显示文摘Mobile manipulators,which are intrinsically redundant when the manipulator and mobile base are moving together,are known for their capabilities to carry out multiple tasks at the same time.This paper presents a whole-body control framework,inspired by legged bio-robots,for a velocity controlled non-holonomic mobile manipulator based on task priority.Control primitives,such as manipulability optimization,trajectory tracking of the end-effector and mobile base,and collision avoidance,are considered in the framework and arranged at different priorities.Lower priority tasks are projected into the null space of control tasks with higher priorities.As a result,lower level tasks are completed without affecting the performance of higher priority tasks.Several experiments are implemented to verify the effectiveness of the proposed controller.The proposed method is proved to be an effective way to solve the whole-body control problem of velocity controlled mobile manipulators. | Mantian LI Zeguo YANG Fusheng ZHA Xin WANG Pengfei WANG Ping LI Qinyuan REN Fei CHEN | 2020 | Science China(Information Sciences)2020，63，7： | 1 |
| 5 | Squeezing More Past Knowledge for Online Class-Incremental Continual Learning显示文摘Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios. | Da Yu Mingyi Zhang Mantian Li Fusheng Zha Junge Zhang Lining Sun Kaiqi Huang | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，3： | 1 |
| 6 | Robot Navigation Strategy in Complex Environment Based on Episode Cognition显示文摘The hippocampal formation of the brain contains a series of nerve cells related to environmental cognition and navigation.These cells can integrate their moment information and external perceptual information and acquire episodic cognitive memory.Through episodic cognition and memory,organisms can achieve autonomous navigation in complex environments.This paper mainly studies the strategy of robot episode navigation in complex environments.After exploring the environment,the robot obtains subjective environmental cognition and forms a cognition map.The grid cells information contained in the cognitive map can obtain the direction and distance of the target through vector calculation,which can get a shortcut through the inexperienced area.The synaptic connection of place cells in the cognitive map can be used as the topological relationship between episode nodes.When the target-oriented vector navigation encounters obstacles,the obstacles can be realized by setting closer sub-targets.Based on the known obstacle information obtained from boundary cells in the cognitive map,topological paths can be divided into multi-segment vector navigation to avoid encountering obstacles.This paper combines vector and topological navigation to achieve goal-oriented and robust navigation capability in a complex environment. | Jinsheng Yuan Wei Guo Zhiyuan Hou Fusheng Zha Mantian Li Lining Sun Pengfei Wang | 2023 | Journal of Bionic Engineering2023，20，1： | 0 |
| 7 | Reinforcement Learning Navigation for Robots Based on Hippocampus Episode Cognition显示文摘Artificial intelligence is currently achieving impressive success in all fields.However,autonomous navigation remains a major challenge for AI.Reinforcement learning is used for target navigation to simulate the interaction between the brain and the environment at the behavioral level,but the Artificial Neural Network trained by reinforcement learning cannot match the autonomous mobility of humans and animals.The hippocampus–striatum circuits are considered as key circuits for target navigation planning and decision-making.This paper aims to construct a bionic navigation model of reinforcement learning corresponding to the nervous system to improve the autonomous navigation performance of the robot.The ventral striatum is considered to be the behavioral evaluation region,and the hippocampal–striatum circuit constitutes the position–reward association.In this paper,a set of episode cognition and reinforcement learning system simulating the mechanism of hippocampus and ventral striatum is constructed,which is used to provide target guidance for the robot to perform autonomous tasks.Compared with traditional methods,this system reflects the high efficiency of learning and better Environmental Adaptability.Our research is an exploration of the intersection and fusion of artificial intelligence and neuroscience,which is conducive to the development of artificial intelligence and the understanding of the nervous system. | Jinsheng Yuan Wei Guo Zhiyuan Hou Fusheng Zha Mantian Li Pengfei Wang Lining Sun | 2024 | Journal of Bionic Engineering2024，21，1： | 0 |
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