为保证各舰船之间的协同占位或位置协调,确定各舰船需要占据的位置或区域,提出舰船多智能体协同占位方案数学建模优化方法。以舰船多智能体运动数学模型为基础,分析各个舰船智能体的运动和航行状态,判断各个智能体的航行领域,并确定舰...为保证各舰船之间的协同占位或位置协调,确定各舰船需要占据的位置或区域,提出舰船多智能体协同占位方案数学建模优化方法。以舰船多智能体运动数学模型为基础,分析各个舰船智能体的运动和航行状态,判断各个智能体的航行领域,并确定舰船智能体的最近会遇距离;采用MADDPG(Multi-Agent Deep Deterministic Policy Gradient)算法结合该距离构建舰船多智能体多元组,以此获取舰船多智能体最佳的占位决策结果;在此基础上,引入同结构变换优化舰船多智能体占位编队结构,保证每个个体的精准占位以及位姿状态的一致性。测试结果表明,该方法能够有效完成各个智能体的位置决策,保证编队位姿状态的一致性,整个舰船智能体编队位置和理想位置之间的误差均低于(5,5)m。展开更多
An agent-oriented modeling approach is presented for process systems. A process system consists of two parts: agents and objects. Based on modeling objects and agents, integration of process operation systems is made ...An agent-oriented modeling approach is presented for process systems. A process system consists of two parts: agents and objects. Based on modeling objects and agents, integration of process operation systems is made in terms of three strategies.How to accomplish the three strategies is illustrated and an integrated framework is presented.展开更多
文摘为保证各舰船之间的协同占位或位置协调,确定各舰船需要占据的位置或区域,提出舰船多智能体协同占位方案数学建模优化方法。以舰船多智能体运动数学模型为基础,分析各个舰船智能体的运动和航行状态,判断各个智能体的航行领域,并确定舰船智能体的最近会遇距离;采用MADDPG(Multi-Agent Deep Deterministic Policy Gradient)算法结合该距离构建舰船多智能体多元组,以此获取舰船多智能体最佳的占位决策结果;在此基础上,引入同结构变换优化舰船多智能体占位编队结构,保证每个个体的精准占位以及位姿状态的一致性。测试结果表明,该方法能够有效完成各个智能体的位置决策,保证编队位姿状态的一致性,整个舰船智能体编队位置和理想位置之间的误差均低于(5,5)m。
文摘An agent-oriented modeling approach is presented for process systems. A process system consists of two parts: agents and objects. Based on modeling objects and agents, integration of process operation systems is made in terms of three strategies.How to accomplish the three strategies is illustrated and an integrated framework is presented.
