Should I Stay or Should I Go: A Learning Approach for Drone-based Sensing Applications

TL;DR

This paper introduces a learning-based decision strategy for drones to optimize waiting times for sensor data processing, improving efficiency in sensing tasks by up to 25.8% across various scenarios.

Contribution

It presents a novel learning approach enabling drones to decide dynamically whether to wait for computation results, outperforming static policies.

Findings

Outperforms static policies by up to 25.8% in efficiency.

Effective in scenarios with stable and varying probabilities of requiring further action.

Demonstrates significant improvements in drone sensing operations.

Abstract

Multicopter drones are becoming a key platform in several application domains, enabling precise on-the-spot sensing and/or actuation. We focus on the case where the drone must process the sensor data in order to decide, depending on the outcome, whether it needs to perform some additional action, e.g., more accurate sensing or some form of actuation. On the one hand, waiting for the computation to complete may waste time, if it turns out that no further action is needed. On the other hand, if the drone starts moving toward the next point of interest before the computation ends, it may need to return back to the previous point, if some action needs to be taken. In this paper, we propose a learning approach that enables the drone to take informed decisions about whether to wait for the result of the computation (or not), based on past experience gathered from previous missions. Through an extensive evaluation, we show that the proposed approach, when properly configured, outperforms several static policies, up to 25.8%, over a wide variety of different scenarios where the probability of some action being required at a given point of interest remains stable as well as for scenarios where this probability varies in time.