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Enhancement of Ant Colony Optimization in Multi-Robot Source Seeking Coordination

机译:多机器人寻源协调中蚁群优化的增强

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This research presents dynamic approaches for swarm robotics system and subsequently achieve enhanced strategies to enhance equilibrium and optimize power usage. Method apply in progress of the project can be divided into hardware platform, control and optimization, and lastly measurement and analysis method. In hardware platform, the speed of rotation of the wheel is controlled for various movement such as direct motion and rotation in place. Optimization method is focused on ant colony optimization. The corrected equation for robot localization control provides more precise mathematical model for manipulating the robot motion. This research compared ACO, dynamic ACO and Dijkstra algorithm in simulated static condition. The result shows that Standard ACO outperforms others algorithm in static condition while Improved algorithm is best used in dynamic conditions.
机译:这项研究提出了用于群体机器人系统的动态方法,并随后实现了增强策略以增强平衡并优化功耗。项目进行中的方法可以分为硬件平台,控制和优化,最后是测量和分析方法。在硬件平台中,控制车轮的旋转速度以进行各种运动,例如直接运动和就地旋转。优化方法侧重于蚁群优化。用于机器人定位控制的校正方程式为操纵机器人运动提供了更精确的数学模型。该研究在模拟静态条件下比较了ACO,动态ACO和Dijkstra算法。结果表明,标准ACO在静态条件下的性能优于其他算法,而改进型算法在动态条件下的性能最好。

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