Christos Papachristos

Distributed infrastructure inspection path planning for aerial robotics subject to time constraints

Within this paper, the problem of 3D inspection path planning for distributed infrastructure using aerial robots that are subject to time constraints is addressed. The proposed algorithm handles varying spatial properties of the infrastructure facilities, accounts for their different importance and exploration function and computes an overall inspection path of high inspection reward while respecting the robot endurance or mission time constraints, as well as the vehicle dynamics and sensor limitations. To achieve its goal, it employs an iterative, 3-step optimization strategy within which it first randomly samples a set of possible structures to visit, subsequently solves the derived traveling salesman problem and computes the travel costs, while finally it randomly assigns inspection times to each structure, and evaluates the total inspection reward. For the derivation of the inspection paths per each independent facility, it interfaces a path planner dedicated to the 3D coverage of single structures. The resulting algorithm properties, computational performance and path quality are evaluated using simulation studies as well as an experimental test-case employing a multirotor micro aerial vehicle.

Uncertainty-aware receding horizon exploration and mapping using aerial robots

This paper presents a novel path planning algorithm for autonomous, uncertainty-aware exploration and mapping of unknown environments using aerial robots. The proposed planner follows a two-step, receding horizon, belief space-based approach. At first, in an online computed tree the algorithm finds the branch that optimizes the amount of space expected to be explored. The first viewpoint configuration of this branch is selected, but the path towards it is decided through a second planning step. Within that, a new tree is sampled, admissible branches arriving at the reference viewpoint are found and the robot belief about its state and the tracked landmarks of the environment is propagated. The branch that minimizes the expected localization and mapping uncertainty is selected, the corresponding path is executed by the robot and the whole process is iteratively repeated. The proposed planner is capable of running online onboard a small aerial robot and its performance is evaluated using experimental studies in a challenging environment.

Augmented reality-enhanced structural inspection using aerial robots

This paper investigates the arising potential when automated path planning for aerial robotic structural inspection is combined with an Augmented Reality interface that provides live feed of stereo views fused with real-time 3D reconstruction data of the environment, while allowing seamless on-the-fly adaptation of the next robot viewpoints using intuitive head motions. The proposed solution aims to address the problem of accurate inspection and mapping of structures and environments for which a prior model exists but is not accurate, potentially outdated, or does not encode important features and semantics such as human-readable indications and other texture information. To approach the problem, the robot computes an optimized inspection path given any prior knowledge of the environment, while the human operator utilizes the live camera views and the real-time derived 3D map data to locally adjust the reference trajectory of the robot, such that it visits an updated set of viewpoints which provides the desired coverage of the real environment and sufficient focus on certain features and details. An autonomous aerial robot capable of navigation and mapping in GPS-denied environments is employed and combined with the Augmented Reality interface to experimentally demonstrate the potential of the approach in structural inspection applications.

Autonomous detection and classification of change using aerial robots

This work proposes a strategy for autonomous change detection and classification using aerial robots. The aerial robots are initially armed with the capacity to autonomously explore unknown environments. Subsequently the online-derived inspection path is repeated in the next missions in order to detect change. For aerial robotic missions that were conducted in different spatio-temporal conditions, the pose-annotated camera data are first compared for similarity in order to identify the correspondence map among the different image sets. Then, efficient feature matching techniques relying on binary descriptors are used to estimate the geometric transformations among the corresponding images from different mission runs, and finally image subtraction and filtering are performed, enabling robust change detection results. To further decrease the computational load, the known poses of the images from different runs are used to create local subsets within which similar images are expected to be found. Once change detection is accomplished, a small set of the images that present the maximum levels of change are used to classify the change by searching to recognize a list of known objects through a bag-of-features approach. The proposed algorithm is initially verified using handheld-smartphone collected data, and eventually evaluated in experiments using an autonomous aerial robot.

Change Detection and Object Recognition Using Aerial Robots

This work proposes a strategy for autonomous change detection and classification using aerial robots. For aerial robotic missions that were conducted in different spatio–temporal conditions, the pose–annotated camera data are first compared for similarity in order to identify the correspondence map among the different image sets. Then efficient feature matching techniques relying on binary descriptors are used to estimate the geometric transformations among the corresponding images, and subsequently perform image subtraction and filtering to robustly detect change. To further decrease the computational load, the known poses of the images are used to create local subsets within which similar images are expected to be found. Once change detection is accomplished, a small set of the images that present the maximum levels of change are used to classify the change by searching to recognize a list of known objects through a bag–of–features approach. The proposed algorithm is evaluated using both handheld–smartphone collected data, as well as experiments using an aerial robot.

Autonomous Exploration and Inspection Path Planning for Aerial Robots Using the Robot Operating System

This use case chapter presents a set of algorithms for the problems of autonomous exploration, terrain monitoring and optimized inspection path planning using aerial robots. The autonomous exploration algorithms described employ a receding horizon structure to iteratively derive the action that the robot should take to optimally explore its environment when no prior map is available, with the extension to localization uncertainty–aware planning. Terrain monitoring is tackled by a finite–horizon informative planning algorithm that further respects time budget limitations. For the problem of optimized inspection with a model of the environment known a priori, an offline path planning algorithm is proposed. All methods proposed are characterized by computational efficiency and have been tested thoroughly via multiple experiments. The Robot Operating System corresponds to the common middleware for the outlined family of methods. By the end of this chapter, the reader should be able to use the open–source contributions of the algorithms presented, implement them from scratch, or modify them to further fit the needs of a particular autonomous exploration, terrain monitoring, or structural inspection mission using aerial robots. Four different open–source ROS packages (compatible with ROS Indigo, Jade and Kinetic) are released, while the repository https://github.com/unr-arl/informative-planning stands as a single point of reference for all of them.