Recently there have been vast interests in introducing robotic systems such as autonomous cars and UAVs into the real world. Ensuring the safety of these systems when they are deployed is thus a highly crucial and urgent problem. Safety problems can arise from various different settings such as when there are multiple vehicles or human-operated vehicles in the environment. Different safety-critical settings often require different approaches for addressing the safety of vehicles. In this dissertation, we contribute novel methods for safety problems that arise from three different scenarios.

First, we have seen a surge of interests in deploying autonomous vehicles into the everyday lives of people. Developing accurate and generalizable algorithms for modeling and predicting human behavior thus becomes important. We present a method for generating the probabilistic forward reachable set of a human-controlled vehicle in an environment where a robot is operating in close proximity to the human-controlled vehicle.

Second, motivated by the recent advances in deploying unmanned aerial vehicles into the airspace, we tackle the problem of multi-vehicle safety. We first contribute a planning and control strategy for guaranteeing safety of multiple vehicles while vehicles complete their objectives. We also present an initialization strategy based on machine learning to enhance the safety of multi-vehicle systems when they adopt least-restrictive safety-aware algorithms.

Finally, machine learning has emerged as a promising tool to enable robots to accomplish challenging tasks under uncertainty in the dynamics of the robots or the environment. However, the safety of the robot while it’s learning online is often not taken into account, which could lead to unsafe behavior of the robot. We present an online learning framework that enables a robot to learn about its dynamics, accomplish a task, and update its safe set simultaneously online.




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