This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we emplo...This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.展开更多
低轨遥感星座任务规划是一个复杂的多目标优化问题,目前基于深度强化学习的卫星任务规划研究存在试验数据星座规模小、优化目标单一、任务重复安排或模型适应性差等问题.针对上述问题,提出CON_DQN(Contract network and Deep Q Network...低轨遥感星座任务规划是一个复杂的多目标优化问题,目前基于深度强化学习的卫星任务规划研究存在试验数据星座规模小、优化目标单一、任务重复安排或模型适应性差等问题.针对上述问题,提出CON_DQN(Contract network and Deep Q Network)算法,采用主从星在轨分布式协商机制,从星基于规划决策,主星基于深度强化学习算法决策,从任务优先级、资源代价和负载均衡等方面进行多目标优化,实现面向即时响应的卫星在轨分布式协商智能任务规划.针对用户需求高频动态到达重点观测区域的场景,进行百星级星座不同规模任务集的仿真实验,结果表明本文所提算法的响应速度较快且能达到较高的任务收益.展开更多
A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal...A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid(MG)and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations(REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution(DE) and particle swarm optimization(PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planningmodel gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.展开更多
基金supported by the National Natural Science Foundation of China(the Key Project,52131201Science Fund for Creative Research Groups,52221005)+1 种基金the China Scholarship Councilthe Joint Laboratory for Internet of Vehicles,Ministry of Education–China MOBILE Communications Corporation。
文摘This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.
文摘低轨遥感星座任务规划是一个复杂的多目标优化问题,目前基于深度强化学习的卫星任务规划研究存在试验数据星座规模小、优化目标单一、任务重复安排或模型适应性差等问题.针对上述问题,提出CON_DQN(Contract network and Deep Q Network)算法,采用主从星在轨分布式协商机制,从星基于规划决策,主星基于深度强化学习算法决策,从任务优先级、资源代价和负载均衡等方面进行多目标优化,实现面向即时响应的卫星在轨分布式协商智能任务规划.针对用户需求高频动态到达重点观测区域的场景,进行百星级星座不同规模任务集的仿真实验,结果表明本文所提算法的响应速度较快且能达到较高的任务收益.
基金supported by Application Technology Research and Engineering Demonstration Program of National Energy Administration in China (No. NY20150301)
文摘A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system(ESS) in active distribution systems(ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid(MG)and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations(REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution(DE) and particle swarm optimization(PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planningmodel gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.
