Due to its flexibility and complementarity, the multiUAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue(SAR) and indoor goods delivery fields. Howe...Due to its flexibility and complementarity, the multiUAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue(SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles(UAVs)in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search(ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes.The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.展开更多
Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning frame...Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.展开更多
Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles.In this paper,we propose a novel path pl...Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles.In this paper,we propose a novel path planning algorithm-Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer,Particle swarm optimization(PSO),for fine-tuning and enhancement.In Phase 1,the start and goal trees are initialized at the starting and goal positions,respectively,and the intermediary tree is initialized at a random unexplored region of the search space.The trees were grown until one met the other and then merged and re-initialized in other unexplored regions.If the start and goal trees merge,the first solution is found and passed through a minimization process to reduce unnecessary nodes.Phase 2 begins by feeding the minimized solution from Phase 1 as the global best particle of PSO to optimize the path.After simulating two special benchmark configurations and six practice configurations with special cases,the results of the study concluded that the proposed method is capable of handling small to large,simple to complex continuous environments,whereas it was very tedious for the previous method to achieve.展开更多
多囊卵巢综合征(Polycystic ovary syndrome,PCOS)是一组生殖内分泌代谢紊乱的综合征,临床以稀发排卵、高雄激素体征、胰岛素抵抗为主要特征,其中育龄期发病率高,对女性生育力造成严重不良影响。PCOS的发生发展涉及多种信号通路,腺苷酸...多囊卵巢综合征(Polycystic ovary syndrome,PCOS)是一组生殖内分泌代谢紊乱的综合征,临床以稀发排卵、高雄激素体征、胰岛素抵抗为主要特征,其中育龄期发病率高,对女性生育力造成严重不良影响。PCOS的发生发展涉及多种信号通路,腺苷酸活化蛋白激酶(AMP-activated protein kinase,AMPK)及哺乳动物雷帕霉素靶蛋白(Mammalian target of rapamycin,mTOR)作为细胞能量感受器是其中两个关键靶点。二者在PCOS各个发病部位包括下丘脑-垂体-卵巢轴、子宫内膜、脂肪与骨骼肌中发挥重要的调节作用,通过影响细胞自噬、氧化应激、炎症、线粒体功能、葡萄糖摄取等,促进卵泡的发育和成熟,改善胰岛素抵抗。近年来,中医药因其成分多样、靶点众多等优势广泛应用于临床,研究人员已对PCOS的发病以及中药治疗及改善PCOS的机制进行了大量研究,结果提示AMPK与mTOR相关通路在其中发挥关键作用。通过总结中药干预AMPK与mTOR及其相关通路治疗PCOS的研究结果,为临床治疗及基础研究提供参考。展开更多
As low-cost and highly autonomous ocean observation platforms,underwater gliders encounter risks during their launch and recovery,especially when coordinating multi-glider deployments.This work focuses on cooperative ...As low-cost and highly autonomous ocean observation platforms,underwater gliders encounter risks during their launch and recovery,especially when coordinating multi-glider deployments.This work focuses on cooperative path planning of an underwater glider fleet with simultaneous launch and recovery to enhance the autonomy of sampling and reduce deployment risks.Specifically,the gliders collaborate to achieve sampling considering the specified routines of interest.The overall paths to be planned are divided into four rectangular parts with the same starting point,and each glider is assigned a local sampling route.A clipped-oriented line-of-sight algorithm is proposed to ensure the coverage of the desired edges.The pitch angle of the glider is selected as the optimizing parameter to coordinate the overall progress considering the susceptibility of gliders to currents and the randomness of paths produced by complex navigational strategies.Therefore,a multi-actuation deep-Q network algorithm is proposed to ensure simultaneous launch and recovery.Simulation results demonstrate the acceptable effectiveness of the proposed method.展开更多
神经病理性疼痛往往由神经系统原发性损害和功能障碍所引起,患者常表现为自发性疼痛或痛觉过敏症状。近年研究表明,AMP活化蛋白激酶(AMP-activated protein kinase,AMPK)作为关键的能量调节因子,不仅通过调节糖脂代谢参与维持机体内环...神经病理性疼痛往往由神经系统原发性损害和功能障碍所引起,患者常表现为自发性疼痛或痛觉过敏症状。近年研究表明,AMP活化蛋白激酶(AMP-activated protein kinase,AMPK)作为关键的能量调节因子,不仅通过调节糖脂代谢参与维持机体内环境稳定,也可以通过相关信号通路调节突触可塑性和神经胶质细胞功能,在一些神经系统疾病的发生与进展中发挥作用。近年来,多项针对神经病理性疼痛的动物模型实验和临床研究表明,在感觉传导通路的多个位点上,AMPK的表达及活性异常,给予AMPK激动剂治疗后,疼痛症状缓解,这提示AMPK的表达/功能异常参与了神经病理性疼痛的发生与维持,因此基于AMPK的镇痛药物研发受到广泛关注。本文就AMPK参与神经病理性疼痛相关机制的进展进行综述,希望能够为相关镇痛药物研发提供参考。展开更多
基金partly supported by Program for the National Natural Science Foundation of China (62373052, U1913203, 61903034)Youth Talent Promotion Project of China Association for Science and TechnologyBeijing Institute of Technology Research Fund Program for Young Scholars。
文摘Due to its flexibility and complementarity, the multiUAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue(SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles(UAVs)in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search(ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes.The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.
基金the financial support of the National Key Research and Development Program of China(2020AAA0108100)the Shanghai Municipal Science and Technology Major Project(2021SHZDZX0100)the Shanghai Gaofeng and Gaoyuan Project for University Academic Program Development for funding。
文摘Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.
基金funded by International University,VNU-HCM under Grant Number T2021-02-IEM.
文摘Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles.In this paper,we propose a novel path planning algorithm-Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer,Particle swarm optimization(PSO),for fine-tuning and enhancement.In Phase 1,the start and goal trees are initialized at the starting and goal positions,respectively,and the intermediary tree is initialized at a random unexplored region of the search space.The trees were grown until one met the other and then merged and re-initialized in other unexplored regions.If the start and goal trees merge,the first solution is found and passed through a minimization process to reduce unnecessary nodes.Phase 2 begins by feeding the minimized solution from Phase 1 as the global best particle of PSO to optimize the path.After simulating two special benchmark configurations and six practice configurations with special cases,the results of the study concluded that the proposed method is capable of handling small to large,simple to complex continuous environments,whereas it was very tedious for the previous method to achieve.
文摘多囊卵巢综合征(Polycystic ovary syndrome,PCOS)是一组生殖内分泌代谢紊乱的综合征,临床以稀发排卵、高雄激素体征、胰岛素抵抗为主要特征,其中育龄期发病率高,对女性生育力造成严重不良影响。PCOS的发生发展涉及多种信号通路,腺苷酸活化蛋白激酶(AMP-activated protein kinase,AMPK)及哺乳动物雷帕霉素靶蛋白(Mammalian target of rapamycin,mTOR)作为细胞能量感受器是其中两个关键靶点。二者在PCOS各个发病部位包括下丘脑-垂体-卵巢轴、子宫内膜、脂肪与骨骼肌中发挥重要的调节作用,通过影响细胞自噬、氧化应激、炎症、线粒体功能、葡萄糖摄取等,促进卵泡的发育和成熟,改善胰岛素抵抗。近年来,中医药因其成分多样、靶点众多等优势广泛应用于临床,研究人员已对PCOS的发病以及中药治疗及改善PCOS的机制进行了大量研究,结果提示AMPK与mTOR相关通路在其中发挥关键作用。通过总结中药干预AMPK与mTOR及其相关通路治疗PCOS的研究结果,为临床治疗及基础研究提供参考。
基金supported by the National Natural Science Foundation of China(No.51909252)the Fundamental Research Funds for the Central Universities(No.202061004)This work is also partly supported by the China Scholar Council.
文摘As low-cost and highly autonomous ocean observation platforms,underwater gliders encounter risks during their launch and recovery,especially when coordinating multi-glider deployments.This work focuses on cooperative path planning of an underwater glider fleet with simultaneous launch and recovery to enhance the autonomy of sampling and reduce deployment risks.Specifically,the gliders collaborate to achieve sampling considering the specified routines of interest.The overall paths to be planned are divided into four rectangular parts with the same starting point,and each glider is assigned a local sampling route.A clipped-oriented line-of-sight algorithm is proposed to ensure the coverage of the desired edges.The pitch angle of the glider is selected as the optimizing parameter to coordinate the overall progress considering the susceptibility of gliders to currents and the randomness of paths produced by complex navigational strategies.Therefore,a multi-actuation deep-Q network algorithm is proposed to ensure simultaneous launch and recovery.Simulation results demonstrate the acceptable effectiveness of the proposed method.
文摘神经病理性疼痛往往由神经系统原发性损害和功能障碍所引起,患者常表现为自发性疼痛或痛觉过敏症状。近年研究表明,AMP活化蛋白激酶(AMP-activated protein kinase,AMPK)作为关键的能量调节因子,不仅通过调节糖脂代谢参与维持机体内环境稳定,也可以通过相关信号通路调节突触可塑性和神经胶质细胞功能,在一些神经系统疾病的发生与进展中发挥作用。近年来,多项针对神经病理性疼痛的动物模型实验和临床研究表明,在感觉传导通路的多个位点上,AMPK的表达及活性异常,给予AMPK激动剂治疗后,疼痛症状缓解,这提示AMPK的表达/功能异常参与了神经病理性疼痛的发生与维持,因此基于AMPK的镇痛药物研发受到广泛关注。本文就AMPK参与神经病理性疼痛相关机制的进展进行综述,希望能够为相关镇痛药物研发提供参考。