﻿ Planning for Search and Coverage ﻿

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Planning for Search and Coverage

The increased interest in UAVs has seen their implementation in military and civilian operations. Small inexpensive autonomous aerial vehicles are of great interest in search and coverage, surveillance, border patrol, and mapping missions. These missions require repetitive aerial maneuvers in order to locate objects/targets as soon as possible in a region or to generate a collage of a specified region. Due to the repetitive nature of the mission, small autonomous aerial vehicles are the clear choice to perform these tedious missions.

The primary challenge in implementing small autonomous aerial vehicles for a search and coverage mission is planning the path of the vehicle that will effectively cover the specified region. This requires the development of an algorithm that will always generate trajectories to maximize the spatial coverage for any specified conditions. Different approaches exist to deal with the search and coverage problem: standard search patterns and nonstandard search patterns. Standard patterns include those such as spiral and serpentine/grid (boustrophedon motion). Even though standard search patterns have proven useful to search an area, they are not ideal since they do not suit situations where multiple vehicles are cooperating to accomplish the task. On the other hand, nonstandard search patterns that have a random trajectory are easier to program in cooperative search and coverage scenario. Some algorithms used to generate the nonstandard search patterns are A∗ and traveling salesmen, which are heuristic techniques. A heuristic technique optimizes the trajectory based on the cost to reach the current state and the cost to reach the goal from the current state.

The algorithm utilizes a receding horizon control (RHC) formulation to generate the trajectory which includes a feedback to account for any disturbance that may deviate the vehicle from its predicted path. It uses set mission duration with an assumption of constant power consumption by the vehicle during the mission. The power consumption of the vehicle is not constant since it varies per the maneuver and turn rate. Secondly, the algorithm selects the path using a discrete set of turning rates that must be specified a priori. Even though the algorithm can potentially generate the most optimal trajectory, an exhaustive simulation analysis is required to obtain the appropriate set of turning rates resulting in multiple iterations. Moreover, the optimal discrete set of turning rates may only be optimal for a set of boundary conditions. This requires the user to iterate the computation of the best discrete set of turning rates for each possible specified condition.

Other forms of search techniques used are probabilistic, which use the probability of the location of an object/target in a region, requiring some prior information of the bounded region. In this form of search the user must provide the number of targets in the bounded region and the probability of the distribution of the targets/objects in the bounded region from prior survey information. This time of search method is primarily used in military application in which satellite information can provide the likelihood of the location of objects/targets. The UAV would primarily focus on sections with the greatest probability of identifying object/targets and then searches the regions with the least probability of a target/object.

A*(pronounced as "A star") In computer science is a computer algorithm that is widely used in path finding and graph traversal, the process of plotting an efficiently directed path between multiple points, called "nodes". It enjoys widespread use due to its performance and accuracy.

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