Overtaking in Autonomous Racing with Online Refinement of Opponent Behavior Prediction using Gaussian Process

Department of Mechanical EngineeringThis paper addresses an overtaking strategy in autonomous head-to-head racing, by virtue of a learningbased prediction to the opponent vehicle???s behavior. The existing prediction approaches either rely on prior model or off-line learning for opponent behavior, whose accuracy diminishes when the opponent in real racing exhibits different driving style. Motivated by this concern, we proposes an online learningbased prediction algorithm that can adapt to the opponents??? different driving style and refine the prediction during the race. Resorting to Gaussian Process (GP) regressor as the baseline learning model, we leverage several techniques to reduce the data size and computation cost of GP, making the algorithm suitable for online learning and prediction refinement in real time. The effectiveness of the proposed algorithm is demonstrated with different simulation scenarios and compared with the other algorithms in terms of prediction accuracy, computation efficiency, and success rate of overtaking maneuver.ope

Robust Control of Nonlinear Systems with applications to Aerial Manipulation and Self Driving Cars

This work considers the problem of planning and control of robots in an environment with obstacles and external disturbances. The safety of robots is harder to achieve when planning in such uncertain environments. We describe a robust control scheme that combines three key components: system identification, uncertainty propagation, and trajectory optimization. Using this control scheme we tackle three problems. First, we develop a Nonlinear Model Predictive Controller (NMPC) for articulated rigid bodies and apply it to an aerial manipulation system to grasp object mid-air. Next, we tackle the problem of obstacle avoidance under unknown external disturbances. We propose two approaches, the first approach using adaptive NMPC with open- loop uncertainty propagation and the second approach using Tube NMPC. After that, we introduce dynamic models which use Artificial Neural Networks (ANN) and combine them with NMPC to control a ground vehicle and an aerial manipulation system. Finally, we introduce a software framework for integrating the above algorithms to perform complex tasks. The software framework provides users with the ability to design systems that are robust to control and hardware failures where preventive action is taken before-hand. The framework also allows for safe testing of control and task logic in simulation before evaluating on the real robot. The software framework is applied to an aerial manipulation system to perform a package sorting task, and extensive experiments demonstrate the ability of the system to recover from failures. In addition to robust control, we present two related control problems. The first problem pertains to designing an obstacle avoidance controller for an underactuated system that is Lyapunov stable. We extend a standard gyroscopic obstacle avoidance controller to be applicable to an underactuated system. The second problem addresses the navigation of an Unmanned Ground Vehicle (UGV) on an unstructured terrain. We propose using NMPC combined with a high fidelity physics engine to generate a reference trajectory that is dynamically feasible and accounts for unsafe areas in the terrain