首页> 外文会议>Adaptive Dynamic Programming and Reinforcement Learning, 2009. ADPRL '09 >Adaptive Critic Designs-based autonomous unmanned vehicles navigation: Application to robotic farm vehicles
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Adaptive Critic Designs-based autonomous unmanned vehicles navigation: Application to robotic farm vehicles

机译:基于自适应批判设计的自动无人驾驶导航:应用于机器人农用车

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This paper addresses the problem of generating autonomously an optimal control action sequence for robotic autonomous unmanned vehicles based on adaptive critic designs (ACDs) for their use in autonomous agriculture vehicles, in the context of precision agriculture. The main objective is to design autonomously an optimal controller that steers the center of the vehicle through a number of waypoints in a particular order using a minimum amount of time and energy consumption. The last aspect is very important for the endurance performance in autonomous unmanned vehicles. In general, the steering of unmanned robotic vehicles depends on the interactions between the vehicle and its supporting medium. Planning for the future encounters with the waypoints should be part of the current control decision, since the vehicles position and orientation as it moves through one gate greatly alter the case of navigation through successive points. The proposed ACD-based intelligent controller learns to guide the vehicle through a set of points autonomously. The simulation results show the performance of the proposed approach for a simple case of mobile robotics.
机译:本文讨论了在精确农业的背景下,基于自适应批评家设计(ACD)为机器人自动无人驾驶汽车自动生成最佳控制动作序列的问题,以将其用于自动驾驶农业汽车。主要目标是自主设计一种最佳控制器,该控制器使用最少的时间和能量消耗以特定顺序通过多个路标操纵车辆中心。最后一个方面对于无人驾驶自动驾驶汽车的耐久性能非常重要。通常,无人机器人车辆的转向取决于车辆与其支撑介质之间的相互作用。规划未来与路点的相遇应该是当前控制决策的一部分,因为车辆在通过一个闸门时的位置和方向会大大改变通过连续点的导航情况。拟议中的基于ACD的智能控制器学习自动引导车辆通过一组点。仿真结果表明了该方法在移动机器人简单情况下的性能。

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