As the capabilities of robots and their control systems improve, applications involving the use of large robot swarms in semi-structured environments become increasingly viable. Such applications include the progressive automation of warehouses, factories, mine sites and hospitals. Despite differences in context and application, these problems all require accurate localization and timely coordination of large fleets of robots.
In outdoor applications, satellite-based localization (e.g. GPS) is a core technology driving the development of autonomous vehicles and facilitating the progressive robotization of industries such as agriculture, mining, inspection and freight. Satellite-based localization enables such applications by providing robots with the ability to quickly and independently measure their absolute position. In indoor environments, satellite-based localization is unavailable, making absolute positioning in such environments challenging.
The first contribution of this thesis is the development of a scalable, “indoor GPS”-like system using ultra-wideband radio technology. The topology of this system is similar to that of GPS: fixed-position radio modules installed in the environment regularly transmit radio signals, fulfilling a similar role to that of GPS satellites; while mobile robots move within the coverage area and localize themselves based on the received signals. Much like GPS, the system therefore scales to support an unlimited number of robots. Theoretical developments presented in this thesis are supported by experimental results, including a demonstration of the system’s functionality, in which multiple nano-quadcopters are flown simultaneously within a space.
Generating collision-free trajectories for large swarms of robots operating in close proximity is a similarly challenging problem, since robot trajectories are coupled through collision avoidance constraints, making the problem computationally expensive and time consuming to solve using classical optimization techniques. The second contribution of this thesis is a method to quickly generate such trajectories by leveraging the parallel-computation architecture of modern graphics processing units. The effectiveness and scalability of this method is demonstrated in two simulation-based case studies: a benchmark problem requiring a swarm of 200 quadcopters to traverse a maze; and an example in which a fleet of 100 robots with bicycle dynamics must change their formation. In both cases, the method easily handles nonlinear dynamics and constraints, and generates feasible, collision-free trajectories for the entire swarm in a matter of seconds.
The developments and contributions presented in this thesis provide a pathway towards the application of these technologies to the localization and coordination of large robot swarms in indoor environments.