Locally-optimal multi-robot navigation under delaying disturbances using homotopy constraints

We study the problem of reliable motion coordination strategies for teams of mobile robots when any of the robots can be temporarily stopped by an exogenous disturbance at any time. We assume that an arbitrary multi-robot planner initially provides coordinated trajectories computed without considering such disturbances. We are interested in designing a control strategy that handles delaying disturbance such that collisions and deadlocks are provably avoided, and the travel time is minimized. The problem is analyzed in a coordination space framework, in which each dimension represents the position of a single robot along its planned trajectory. We demonstrate that to avoid deadlocks, the trajectory of the system in the coordination space must be homotopic to the trajectory corresponding to the planned solution. We propose a controller that abides this homotopy constraint while minimizing the travel time. Besides being provably deadlock-free, our experiments show that travel time is significantly smaller with our method than than with a reactive method

Multi Vehicle Trajectory Planning On Road Networks

When multiple autonomous vehicles work in a shared space, such as in a surface mine or warehouse, they often travel along specified paths through a static road network. Although these vehicles’ actions and performance are coupled, their motion is often planned myopically or omits cooperation beyond avoiding collisions reactively. More desirable solutions could be achieved by coordinating and planning actions ahead of time. To make multi-vehicle systems more productive and efficient, the thesis introduces planning methods that can optimise for travel time, energy consumption, and trajectory smoothness. Vehicle motion is coordinated by using motion models that combine all trajectories, and avoid collisions. Mathematical programming is then used to find optimised solutions. The proposed methods are shown to significantly reduce solution costs compared to an approach based on common driving practices. As the number of vehicles and interactions between them increases, the number of solutions grows exponentially, making finding a solution computationally challenging. A major aim here was to find high quality solutions within practical computation times. To achieve this, techniques were developed that exploit the structure of the problems. This includes a heuristic algorithm that scales better with problem size, and is combined with the mathematical programming techniques to reduce their complexity. These were found to significantly reduce computation times, trading off marginal solution quality