Autonomous robotics in noisy and delayed environments

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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).