基于深度强化学习在动态的多重障碍环境中的多UAV同时目标分配和路径规划
Xiaoran Kong1, Yatong Zhou1, Zhe Li2
1School of Electronic and Information Engineering, HeBei University of Technology, Tianjin, China.
Frontiers in neurorobotics
|February 6, 2024
概括
本研究介绍了多个无人机系统的深度强化学习算法,使得在动态环境中能够有效地分配目标和无碰撞路径规划.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 合作的多无人机系统需要有效的目标分配和路径规划.
- 环境动态和部分可观测性对无人机协调构成重大挑战.
研究的目的:
- 为应对多无人机在动态,部分可观测环境中的目标分配和路径规划的挑战.
- 开发一种新的算法,集成目标分配和路径规划,以加强无人机协作.
主要方法:
- 制定了多无人机目标分配和路径规划问题,作为一个部分可观测的马尔科夫决策过程 (POMDP).
- 提出了一个深度强化学习 (DRL) 算法,在双延迟的深度决定性政策梯度 (TD3) 框架中包含一个目标分配网络.
- 目标分配网络处理每个步骤的分配,而TD3优化路径规划并提供训练信号.
主要成果:
- 拟议的DRL算法实现了无人机的最佳完整目标分配.
- 证明了每个无人机在复杂的3D动态环境和多个障碍物中的无碰撞路径规划.
- 与现有方法相比,该方法在完成目标和适应性方面表现出优异的表现.
结论:
- 这种基于DRL的新算法有效地解决了多无人机系统的集成目标分配和路径规划问题.
- 该方法在合作执行任务和环境适应性方面取得了显著的改进.
- 这种方法为复杂的现实世界无人机应用提供了强大的解决方案.
相关概念视频
Multi-input and Multi-variable systems
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
In the absence of...
Vector Functions and Motion: Problem Solving
Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...
Lagrange Multipliers: Two Constraints
The method of Lagrange multipliers with two constraints is used to optimize a function subject to two independent constraints. In many applications, the objective function represents a quantity to be maximized or minimized, such as cost, area, distance, or energy. The two constraints represent requirements that the solution must satisfy, such as fixed volume, limited resources, or prescribed dimensions.For a function of three variables, each constraint forms a surface in three-dimensional space.


