Autonomous robotics in noisy and delayed environments

本研究旨在开发能够在所有环境中开发和部署自主机器人的方法和流程，其中机器人可以促进安全和生产力，特别是在延时环境中。时间延迟来自几个不同的来源，如传感器、计算、执行器、系统不同部分之间的通信和交互，特别是机器人与机器人（或agent与agent）的交互。我们主要对应用于多机器人环境的在线实时学习感兴趣，在这种环境中，机器人集体中出现个人和群体行为，并且时间延迟可能导致个体或集体的不同甚至不稳定的行为。*** ***对于本应用程序的目的，个体行为是由单个机器人在感知环境处于给定状态时如何选择行动（根据策略或策略）来定义的。机器人感知环境状态的方式本质上是不精确的，有限的，嘈杂的，潜在的时间延迟的。此外，其他机器人的存在会改变环境，以至于与另一个机器人的关系可能会影响在任何给定时间做出的决定。群体行为是指当面对相互冲突的角色和任务时，集体作为一个群体可能采取的行动。群体行为与个体行为之间的联系目前尚不完全清楚。****本研究打算应用基于强化学习技术（及其衍生技术）的学习算法，以生产能够在线实时的机器人，这些机器人可以应用于实际应用，如简易爆炸装置（IED）的定位和破坏、目标识别、矿井和隧道导航、空中监视、水下测绘、拥挤环境中的导航等。*** ***环境的表示是通过使用学习算法以及基于模型预测控制（MPC）的分散控制来完成的，特别是在考虑代理之间的相互作用时（当代理具有共同的目标或目标时是合作的，或者当代理在本质上没有其他目标的明确表示时是竞争的）。学习将应用于两个不同的层面：(i)单个任务的执行；(2)代理之间的相互作用。*** ***所开发的算法将在符合机器人操作系统（ROS）的真实机器人平台上进行测试。这些算法需要在有噪声和延迟的传感器读数下实时运行。此外，在控制算法的处理随机延迟是可能的。该平台由地面和空中车辆组成，实验设施已经在加拿大皇家军事学院（RMCC）可用

This research aims to develop the methods and processes that enable the development and deployment of autonomous robots in all environments wherein robots can contribute to safety and productivity, especially in time-delayed environments. The time delay comes from several different sources as sensors, computation, actuators, communication and interactions among different parts of the system, especially robot to robot (or agent to agent) interaction. We are primarily interested in the case of on-line real-time learning applied to multi-robot environments in which emergence of individual and group behaviours in collectives of robots arise and in which time-delays could lead to different, or even unstable, behaviours, individually or collectively.*** ***For the purposes of this application, individual behaviour is defined by how a single robot chooses to act (in terms of policies or strategies) given that it perceives the environment to be at a given state. The way the robot perceives the state of the environment is inherently imprecise, limited, noisy and potentially time-delayed. Also, the presence of other robots change the environment in such a way that the relationship with another single robot may affect the decision taken at any given time. Group behaviour is how the collective may act as a group when faced to conflicting roles and tasks. Group behaviours are linked to individual behaviours in a manner that is not presently completely known.****This research intends to apply learning algorithms based on reinforcement learning techniques (and its derivatives) in order to produce on-line real-time capable robots that can be applied to actual applications such as the location and disruption of improvised explosive devices (IED), object identification, mine and tunnel navigation, aerial surveillance, underwater mapping, navigation in crowded environments and so on.*** ***The representation of the environment is to be done by using learning algorithms as well as decentralized control based on Model Predictive Control (MPC), especially when considering the interactions among agents (either cooperative when the agents have a common goal or objective or competitive when the agents do not have an explicit representation of the goal of the others in nature). The learning will be applied to two different layers: (i) the individual execution of a task; and (ii) the interaction among agents.*** ***The algorithms developed will be tested in real robotic platforms that are compliant with the Robot Operating System (ROS). These algorithms are needed to run in real-time with the noisy and delayed sensor readings. Furthermore, stochastic delays in the processing of the control algorithms are possible. The platforms are composed of ground and aerial vehicles and the experimental facility is already available at the Royal Military College of Canada (RMCC).**