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该项目将深度Q-Learning实施为自动化领域研究最多的任务之一,即避障。环境和无人机本身在Unity3D中模拟,而DQL算法是用Python编写的。无人机的动作被限制为只能左转、右转和直行。这意味着它的运动仅限于2D水平面,与汽车非常相似。然而,无人机在旋转时也会滚动,就像普通飞机需要滚动才能转弯一样。因此,环境仍然是 3D 的。 代理的目标是在障碍物森林中飞行一段时间。为了防止代理绕圈,引入了目标点供代理飞到。这个目标点不是让代理到达的,而只是为了引导无人机到一个特定的方向。 代理每接近目标点,就会获得+1的奖励。但是,如果它在任何时间步长中飞离目标点更远,它将获得-1的奖励。如果代理坠毁,无论其飞行方向如何,它都将获得 -1 奖励。如此简单的奖励的原因是
无人机收藏视频 三轴飞行器,四轴飞行器,六轴飞行器和八轴飞行器无人机在Unity中构建。包括一个最小的控制器,用于稳定四轴飞行器,由 Unity 机器学习代理制成。
阿木实验室,自主无人机开源软件。
自动驾驶的仿真相关
GA遗传算法&动态可视化的,解决旅行商问题,python
TSP算法全复现:遗传(GA)、粒子群(PSO)、模拟退火(SA)、禁忌搜索(ST)、蚁群算法(ACO)、自自组织神经网络(SOM)
毕业设计的代码部分,实现了UE4和airsim环境下无人机自主导航和目标跟踪的强化学习算法。
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