Kostas Alexis

Receding horizon “next-best-view” planner for 3D exploration

This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” scheme: In an online computed random tree it finds the best branch, the quality of which is determined by the amount of unmapped space that can be explored. Only the first edge of this branch is executed at every planning step, while repetition of this procedure leads to complete exploration results. The proposed planner is capable of running online, onboard a robot with limited resources. Its high performance is evaluated in detailed simulation studies as well as in a challenging real world experiment using a rotorcraft micro aerial vehicle. Analysis on the computational complexity of the algorithm is provided and its good scaling properties enable the handling of large scale and complex problem setups.

Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots

This paper presents a new algorithm for three-dimensional coverage path planning for autonomous structural inspection operations using aerial robots. The proposed approach is capable of computing short inspection paths via an alternating two-step optimization algorithm according to which at every iteration it attempts to find a new and improved set of viewpoints that together provide full coverage with decreased path cost. The algorithm supports the integration of multiple sensors with different fields of view, the limitations of which are respected. Both fixed-wing as well as rotorcraft aerial robot configurations are supported and their motion constraints are respected at all optimization steps, while the algorithm operates on both mesh- and occupancy map-based representations of the environment. To thoroughly evaluate this new path planning strategy, a set of large-scale simulation scenarios was considered, followed by multiple real-life experimental test-cases using both vehicle configurations.

Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using Robot Operating System

In this chapter, strategies for Model Predictive Control (MPC) design and implementation for Unmaned Aerial Vehicles (UAVs) are discussed. This chapter is divided into two main sections. In the first section, modelling, controller design and implementation of MPC for multi-rotor systems is presented. In the second section, we show modelling and controller design techniques for fixed-wing UAVs. System identification techniques are used to derive an estimate of the system model, while state of the art solvers are employed to solve the optimization problem online. By the end of this chapter, the reader should be able to implement an MPC to achieve trajectory tracking for both multi-rotor systems and fixed-wing UAVs.

Full Attitude Control of a VTOL tailsitter UAV

This paper addresses the challenges of the design, development and control of a new convertible VTOL tailsitter unmanned aerial vehicle that combines the advantages of both fixed wing and rotary wing systems. Wind tunnel measurements are used to get an understanding of the control allocation and to model the static forces and moments acting on the system. Based on the derived model, a novel controller that operates in SO(3) and handles the dynamics of the vehicle at any attitude configuration, including the rotorcraft and fixed-wing regimes as well as their transitions, is presented. This unified controller allows the autonomous transition of the system without discontinuities of switching, as well as its overall high performance flight control. The capabilities and flying qualities of the platform and the controller are demonstrated and evaluated by means of extensive experimental studies.

Receding horizon path planning for 3D exploration and surface inspection

Within this paper a new path planning algorithm for autonomous robotic exploration and inspection is presented. The proposed method plans online in a receding horizon fashion by sampling possible future configurations in a geometric random tree. The choice of the objective function enables the planning for either the exploration of unknown volume or inspection of a given surface manifold in both known and unknown volume. Application to rotorcraft Micro Aerial Vehicles is presented, although planning for other types of robotic platforms is possible, even in the absence of a boundary value solver and subject to nonholonomic constraints. Furthermore, the method allows the integration of a wide variety of sensor models. The presented analysis of computational complexity and thorough simulations-based evaluation indicate good scaling properties with respect to the scenario complexity. Feasibility and practical applicability are demonstrated in real-life experimental test cases with full on-board computation.

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.

An incremental sampling-based approach to inspection planning: the rapidly exploring random tree of trees

A new algorithm, called rapidly exploring random tree of trees (RRTOT) is proposed, that aims to address the challenge of planning for autonomous structural inspection. Given a representation of a structure, a visibility model of an onboard sensor, an initial robot configuration and constraints, RRTOT computes inspection paths that provide full coverage. Sampling based techniques and a meta-tree structure consisting of multiple RRT* trees are employed to find admissible paths with decreasing cost. Using this approach, RRTOT does not suffer from the limitations of strategies that separate the inspection path planning problem into that of finding the minimum set of observation points and only afterwards compute the best possible path among them. Analysis is provided on the capability of RRTOT to find admissible solutions that, in the limit case, approach the optimal one. The algorithm is evaluated in both simulation and experimental studies. An unmanned rotorcraft equipped with a vision sensor was utilized as the experimental platform and validation of the achieved inspection properties was performed using 3D reconstruction techniques.

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 Robotic Aerial Tracking, Avoidance, and Seeking of a Mobile Human Subject

This paper presents a methodology to achieve Robotic Aerial Tracking of a mobile – human – subject within a previously-unmapped environment, potentially cluttered with unknown structures. The proposed system initially employs a high-end Unmanned Aerial Vehicle, capable of fully-autonomous estimation and flight control. This platform also carries a high-level Perception and Navigation Unit, which performs the tasks of 3D-visual perception, subject detection, segmentation, and tracking, which allows the aerial system to follow the human subject as they perform free unscripted motion, in the perceptual – and equally importantly – in the mobile sense. To this purpose, a navigation synthesis which relies on an attractive/repulsive forces-based approach and collision-free path planning algorithms is integrated into the scheme. Employing an incrementally-built map model which accounts for the ground subject’s and the aerial vehicle’s motion constraints, the Robotic Aerial Tracker system is capable of achieving continuous tracking and reacquisition of the mobile target.