基于蚁群算法的仿真机器鱼动作决策策略

Action Decision Strategy of Simulation Robot Fish Based on Ant Colony Algorithm

针对仿真机器鱼非对抗赛和对抗赛情况,为使求解结果在既不依赖初始路线的选择,也不需要外界的特定干预的情况下,实现鱼快速、准确的调整,分别提出2种基于蚁群算法的动作决策策略。基于蚁群算法的分支界限法,判断机器鱼关键物理量所在分支,自主确定当前时刻的鱼的速度和角速度档位的最优组合;而基于蚁群算法的动态规划法,在每个周期内,根据机器鱼反馈回来的动态变量及时进行自主调整。以上2种方法经2D仿真平台验证结果表明:机器鱼可根据该策略调整路径,实现速度和方向的组合优化,以最短的时间和距离找到目标点。这说明基于蚁群算法的2种动作决策策略具有很强的适应能力,满足仿真机器鱼对于动作决策的要求。

Aiming at the non-match and match situations of simulation robot fish, in order to solve the result is neither dependent on the choice of the initial line does not need outside intervention-specific circumstances, to achieve the fish fast, accurate adjustment, this paper proposed two kinds of ant colony algorithms action decision strategy. Ant colony algorithm based on branch bound method judges the key physical machine where the fish branch, self-determined speed and angular velocity in the moment and optimal combined speed of the fish with angular velocity of the fish; In each cycle, the ant colony algorithm based on dynamic programming make self adjustment according to the dynamics of the robot fish immediate feedback. Examples of the above two methods used by the 2D simulation platform validation results showed, robot fish can be adjusted based on the policy path, to achieve optimal combination of speed and direction, the shortest time and distance to find the target point. This shows that based on ant colony algorithm＇s two kinds of action decision strategy has a strong ability to adapt effectively to meet the simulation of robot fish for action decision-making.