UAV motion planning towards visibility and uncertainty

Abstract

Efficient and effective trajectory planning is a fundamental aspect of Unmanned Aerial Vehicles (UAVs). The basic requirement of motion planning is the real-time generation of energy-efficient, feasible and collision-free trajectories. Beyond these fundamental prerequisites, there exist advanced demands that are crucial for successfully executing real-world tasks. This thesis addresses two such complex aspects: the requirement of visibility and the challenge of uncertainty.

Visibility stands as a common criterion for a plethora of UAV tasks. In tasks such as tracking or inspection, successfully completing these tasks relies on the comprehensive observation of predefined positions. The absence of such observation deems these tasks unsuccessful or failed. Despite its critical importance, current methodologies in motion planning literature fall short of providing guaranteed visibility. Typically, visibility is handled as a peripheral utility, subject to optimization alongside a handcrafted visibility cost and other factors, such as smoothness, leading to a lack of assured observation.

Another challenge in motion planning arises from the presence of uncertainty, which can be categorized as either intrinsic or extrinsic. Extrinsic uncertainties stem from factors external to the UAV, such as dynamic obstacles and disturbances in the environment. On the other hand, intrinsic uncertainties originate from within the system itself, often due to inaccurate robot localization and errors in modeling. Previous approaches that have addressed dynamic obstacles or external disturbances in isolation are insufficient to handle the complexities of all the uncertainty and lead to conservative trajectories. This thesis proposes a reliable motion planning framework for UAVs, integrating various uncertainties into a chance constraint that characterizes the uncertainty in a probabilistic manner. The chance constraint provides a probabilistic safety certificate by calculating the collision probability between the robot’s Gaussian-distributed forward reachable set and states of obstacles. To reduce the conservatism of the planned trajectory, a tight upper bound of the collision probability is proposed and evaluated both exactly and approximately. The approximated solution is used to generate motion primitives as a reference trajectory, while the exact solution is leveraged to iteratively optimize the trajectory for better results. Our method is thoroughly tested in simulation and real-world experiments, verifying its reliability and effectiveness in uncertain environments.