Feedback-based real-time scheduling in autonomous vehicle systems

The use of feedback control techniques has been gaining importance in the context of scheduling in real-time systems as a means to provide predictable performance in the face of uncertain workload. We propose a novel feedback-based scheduling approach for task scheduling in real-time systems. We focus on a system with a mobile node, where the mobility characteristics affect task parameters. The objective is to achieve low miss ratio and high CPU utilization. This objective is achieved by feeding back system performances and adapting the node' mobility parameters. We study the new approach in a selective herbicide spraying problem in the agricultural production wherein the speed of an autonomous vehicle (and hence task parameters) is adapted based on weed distribution in the given farm field. Simulations and analysis show that our approach can achieve low miss ratio and high CPU utilization.