A Local Search Approach to Observation Planning with Multiple UAVs

AAAI Conferences

Observation planning for Unmanned Aerial Vehicles (UAVs) is a challenging task as it requires planning trajectories over a large continuous space and with motion models that can not be directly encoded into current planners. Furthermore, realistic problems often require complex objective functions that complicate problem decomposition. In this paper, we propose a local search approach to plan the trajectories of a fleet of UAVs on an observation mission. The strength of the approach lies in its loose coupling with domain specific requirements such as the UAV model or the objective function that are both used as black boxes. Furthermore, the Variable Neighborhood Search (VNS) procedure considered facilitates the adaptation of the algorithm to specific requirements through the addition of new neighborhoods. We demonstrate the feasibility and convenience of the method on a large joint observation task in which a fleet of fixed-wing UAVs maps wildfires over areas of a hundred square kilometers. The approach allows generating plans over tens of minutes for a handful of UAVs in matter of seconds, even when considering very short primitive maneuvers.

Autonomous Search and Tracking via Temporal Planning

AAAI Conferences

Search And Tracking (SAT) is the problem of searching for a mobile target and tracking it after it is found. As this problem has important applications in search-and-rescue and surveillance operations, recently there has been increasing interest in equipping unmanned aerial vehicles (UAVs) with autonomous SAT capabilities. State-of-the-art approaches to SAT rely on estimating the probability density function of the target's state and solving the search control problem in a greedy fashion over a short planning horizon (typically, a one-step lookahead). These techniques suffer high computational cost, making them unsuitable for complex problems. In this paper, we propose a novel approach to SAT, which allows us to handle big geographical areas, complex target motion models and long-term operations. Our solution is to track the target reactively while it is in view and to plan a recovery strategy that relocates the target every time it is lost, using a high-performing automated planning tool. The planning problem consists of deciding where to search and which search patterns to use in order to maximise the likelihood of recovering the target. We show experimental results demonstrating the potential of our approach.

Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle

AAAI Conferences

In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Trees. Additionally, we incorporate dynamic reconfigurability into the framework by integrating the motion planners with the control kernel of the UAV in a novel manner with little modification to the original algorithms. The framework has been verified through simulation and in actual flight. Empirical results show that these techniques used with such a framework offer a surprisingly efficient method for dynamically reconfiguring a motion plan based on unforeseen contingencies which may arise during the execution of a plan. The framework is generic and can be used for additional platforms.

Optimal strategies for the control of autonomous vehicles in data assimilation

arXiv.org Machine Learning

We propose a method to compute optimal control paths for autonomous vehicles deployed for the purpose of inferring a velocity field. In addition to being advected by the flow, the vehicles are able to effect a fixed relative speed with arbitrary control over direction. It is this direction that is used as the basis for the locally optimal control algorithm presented here, with objective formed from the variance trace of the expected posterior distribution. We present results for linear flows near hyperbolic fixed points.