Safe large-scale aerial survey planning for multi-robot systems

This thesis provides multi-robot path planning methods for conducting aerial surveys over large areas. In these surveys, Unpersoned Aerial Vehicles (UAVs) collect sets of images with their downward-facing cameras such that the entire area on the ground is covered. We present two novel methods for use in planning and deploying large-scale survey operations. The first method is an offline discrete path planning tool that finds a series of GPS waypoints for each UAV to visit such that the survey coverage requirements are met. The second is an online method that guarantees collision avoidance between UAVs and other objects in their environments despite noisy relative position uncertainty. Our path planning tool is designed to make the best use of limited flight time. Unlike current survey path planning solutions based on geometric patterns, our method, Path Optimization for Population Counting with Overhead Robotic Networks (POPCORN), solves a series of Boolean satisfiability instances of increasing complexity. Each instance yields a set of feasible paths at each iteration and recovers the set of shortest paths after sufficient time. By the addition of a divide-and-conquer scheme, Split And Link Tiles (SALT), these POPCORN instances can be computed in parallel and combined in a scalable manner to produce coverage paths over very large areas-of-interest. To demonstrate this algorithm's capabilities, we implemented our planning algorithm with a team of drones to conduct multiple photographic aerial wildlife surveys of the Cape Crozier Adélie penguin rookery on Ross Island, Antarctica, one of the largest penguin colonies in the world. The colony, which contains over 300,000 nesting pairs and spans over 2 square km, was surveyed in about 3 hours. In contrast, previous human-piloted single-drone surveys of the same colony required over 2 days to complete. We also have deployed our survey system in Mono Lake, CA to survey a California gull colony as well as a 2,000 acre ranch in Marin, CA. This thesis also presents a decentralized collision avoidance method, Collision Avoidance by Reciprocal Projections (CARP) based on safe-reachability. CARP is designed to guarantee avoidance, even when an agent has noisy estimates of the positions and velocities of other agents. The agents sense each other via measurements from noisy on-board sensors to maintain their own individual estimate of all other agents without inter-agent communication. Using these estimates, each agent can solve a convex program on-board that finds a trajectory that is guaranteed to be safe. This optimization problem can be quickly solved in practice, even on embedded systems, making it practical to use for computationally-constrained platforms such as small mobile robots. Furthermore, we show that CARP is an instance in a large class of collision avoidance methods and show that all collision avoidance methods of this class are guaranteed to be collision free. We show that our method can be extended to find smooth polynomial trajectories for higher dynamic systems such as quadrotors, and we present simulations and hardware demonstrations that showcase the capability of this algorithm. Finally, by using similar tools from safe-reachability, we find effective methods for pursuit in multi-agent pursuit-evasion games under uncertainty

Keywords: Mechanical Engineering.
Advisor: Cutkosky Mark R.
Schwager Mac
Kochenderfer Mykel J.
Sadigh Dorsa