Task-switching for self-sufficient robots

Task-switching enables a system to make its own decisions about which task to perform. It is therefore a key ability for any truly autonomous system. Common task-switching methods range from computationally expensive planning methods to often suboptimal, minimalistic, heuristics. This thesis takes a bio-inspired approach, motivated by the fact that animals successfully make task-switching decisions on a daily basis. The field of behavioural ecology provides a vast literature on animal task-switching. Generally these works are descriptive models of animal behaviour, either modelling to fit the data from observed animal behaviour, or theoretically optimal models of how animals ought to behave. But what is needed in robotics are methods that generate behaviour based on the information available (due to sensing) to the robot. Furthermore these methods have to take the physical limitations (velocity, acceleration, storage capacity etc.) of the robot into account. This thesis takes inspiration from descriptive behavioural ecology models and proposes a situated and embodied task-switching method suitable for mobile robots. To evaluate the quality of the decisions an objective function is needed. Reproductive success is commonly used in Biology, here economical success is used. We illustrate the applicability of the proposed methods on Toda's Fungus Eater robot. The decisions this robot faces are (1) when to work and when to refuel and (2) where to work or refuel respectively. Both decision types are essential to any autonomous, mobile robot. The proposed task-switching methods are based on Optimal Foraging Theory, in particular on rate-maximization and the Marginal-Value Theorem.