Coverage path planning methods focusing on energy efficient and cooperative strategies for unmanned aerial vehicles

The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research

Minimizing Turns in Single and Multi Robot Coverage Path Planning

Spurred by declining costs of robotics, automation is becoming a prevalent area of interest for many industries. In some cases, it even makes economic sense to use a team of robots to achieve a goal faster. In this thesis we study sweep coverage path planning, in which a robot or a team of robots must cover all points in a workspace with its footprint. In many coverage applications, including cleaning and monitoring, it is beneficial to use coverage paths with minimal robot turns. In the first part of the thesis, we address this for a single robot by providing an efficient method to compute the minimum altitude of a non-convex polygonal region, which captures the number of parallel line segments, and thus turns, needed to cover the region. Then, given a non-convex polygon, we provide a method to cut the polygon into two pieces that minimizes the sum of their altitudes. Given an initial convex decomposition of a workspace, we apply this method to iteratively re-optimize and delete cuts of the decomposition. Finally, we compute a coverage path of the workspace by placing parallel line segments in each region, and then computing a tour of the segments of minimum cost. We present simulation results on several workspaces with obstacles, which demonstrate improvements in both the number of turns in the final coverage path and runtime. In the second part of the thesis, we extend the concepts developed for a single robot coverage to a multi robot case. We provide a metric χ that approximates the cost of a coverage path, which accounts for the cost of turns. Given a polygon, we provide a method for cutting a polygon into two that would minimize the maximum cost χ between the two polygons. Provided with an initial n-cell decomposition, we apply this method in the iterative manner to re-optimize cuts in order to minimize the maximum cost χ over all cells in the decomposition. For each cell in the re-optimized n-cell decomposition, a single robot coverage path is computed using the minimum altitude decomposition. We present the simulation results that demonstrate improvements in the maximum cost as well as the range of costs over all robots in the team

SEARCHING HETEROGENEOUS DOCUMENT IMAGE COLLECTIONS

A decrease in data storage costs and widespread use of scanning devices has led to massive quantities of scanned digital documents in corporations, organizations, and governments around the world. Automatically processing these large heterogeneous collections can be difficult due to considerable variation in resolution, quality, font, layout, noise, and content. In order to make this data available to a wide audience, methods for efficient retrieval and analysis from large collections of document images remain an open and important area of research. In this proposal, we present research in three areas that augment the current state of the art in the retrieval and analysis of large heterogeneous document image collections. First, we explore an efficient approach to document image retrieval, which allows users to perform retrieval against large image collections in a query-by-example manner. Our approach is compared to text retrieval of OCR on a collection of 7 million document images collected from lawsuits against tobacco companies. Next, we present research in document verification and change detection, where one may want to quickly determine if two document images contain any differences (document verification) and if so, to determine precisely what and where changes have occurred (change detection). A motivating example is legal contracts, where scanned images are often e-mailed back and forth and small changes can have severe ramifications. Finally, approaches useful for exploiting the biometric properties of handwriting in order to perform writer identification and retrieval in document images are examined