Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR

This paper addressed the challenge of exploring large, unknown, and unstructured industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system is that all the algorithms relied on the multi-resolution of the octomap for the world representation. We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements of the capability of the open-source system to run online and on-board the UAV in real-time. Our approach is compared to different reference heuristics under this simulation environment showing better performance in regards to the amount of explored space. With the proposed approach, the UAV is able to explore 93% of the search space under 30 min, generating a path without repetition that adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411

Vision Based Collaborative Localization and Path Planning for Micro Aerial Vehicles

Autonomous micro aerial vehicles (MAV) have gained immense popularity in both the commercial and research worlds over the last few years. Due to their small size and agility, MAVs are considered to have great potential for civil and industrial tasks such as photography, search and rescue, exploration, inspection and surveillance. Autonomy on MAVs usually involves solving the major problems of localization and path planning. While GPS is a popular choice for localization for many MAV platforms today, it suffers from issues such as inaccurate estimation around large structures, and complete unavailability in remote areas/indoor scenarios. From the alternative sensing mechanisms, cameras arise as an attractive choice to be an onboard sensor due to the richness of information captured, along with small size and inexpensiveness. Another consideration that comes into picture for micro aerial vehicles is the fact that these small platforms suffer from inability to fly for long amounts of time or carry heavy payload, scenarios that can be solved by allocating a group, or a swarm of MAVs to perform a task than just one. Collaboration between multiple vehicles allows for better accuracy of estimation, task distribution and mission efficiency. Combining these rationales, this dissertation presents collaborative vision based localization and path planning frameworks. Although these were created as two separate steps, the ideal application would contain both of them as a loosely coupled localization and planning algorithm. A forward-facing monocular camera onboard each MAV is considered as the sole sensor for computing pose estimates. With this minimal setup, this dissertation first investigates methods to perform feature-based localization, with the possibility of fusing two types of localization data: one that is computed onboard each MAV, and the other that comes from relative measurements between the vehicles. Feature based methods were preferred over direct methods for vision because of the relative ease with which tangible data packets can be transferred between vehicles, and because feature data allows for minimal data transfer compared to large images. Inspired by techniques from multiple view geometry and structure from motion, this localization algorithm presents a decentralized full 6-degree of freedom pose estimation method complete with a consistent fusion methodology to obtain robust estimates only at discrete instants, thus not requiring constant communication between vehicles. This method was validated on image data obtained from high fidelity simulations as well as real life MAV tests. These vision based collaborative constraints were also applied to the problem of path planning with a focus on performing uncertainty-aware planning, where the algorithm is responsible for generating not only a valid, collision-free path, but also making sure that this path allows for successful localization throughout. As joint multi-robot planning can be a computationally intractable problem, planning was divided into two steps from a vision-aware perspective. As the first step for improving localization performance is having access to a better map of features, a next-best-multi-view algorithm was developed which can compute the best viewpoints for multiple vehicles that can improve an existing sparse reconstruction. This algorithm contains a cost function containing vision-based heuristics that determines the quality of expected images from any set of viewpoints; which is minimized through an efficient evolutionary strategy known as Covariance Matrix Adaption (CMA-ES) that can handle very high dimensional sample spaces. In the second step, a sampling based planner called Vision-Aware RRT* (VA-RRT*) was developed which includes similar vision heuristics in an information gain based framework in order to drive individual vehicles towards areas that can benefit feature tracking and thus localization. Both steps of the planning framework were tested and validated using results from simulation

Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles

In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory optimization-based local planners have been shown to perform well in these cases, prior work either does not address how to deal with local minima in the optimization problem, or solves it by using an optimistic global planner. We present a conservative trajectory optimization-based local planner, coupled with a local exploration strategy that selects intermediate goals. We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner, and also outperforms doing a single exploration step when the local planner is stuck. The method is validated through experiments in a variety of highly cluttered environments including a dense forest. These experiments show the complete system running in real time fully onboard an MAV, mapping and replanning at 4 Hz.Comment: Accepted to ICRA 2018 and RA-L 201

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Vision and Learning for Deliberative Monocular Cluttered Flight

Cameras provide a rich source of information while being passive, cheap and lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a number of contributions: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with a quadrotor built from off-the-shelf parts. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar as well if available

PAMPC: Perception-Aware Model Predictive Control for Quadrotors

We present the first perception-aware model predictive control framework for quadrotors that unifies control and planning with respect to action and perception objectives. Our framework leverages numerical optimization to compute trajectories that satisfy the system dynamics and require control inputs within the limits of the platform. Simultaneously, it optimizes perception objectives for robust and reliable sens- ing by maximizing the visibility of a point of interest and minimizing its velocity in the image plane. Considering both perception and action objectives for motion planning and control is challenging due to the possible conflicts arising from their respective requirements. For example, for a quadrotor to track a reference trajectory, it needs to rotate to align its thrust with the direction of the desired acceleration. However, the perception objective might require to minimize such rotation to maximize the visibility of a point of interest. A model-based optimization framework, able to consider both perception and action objectives and couple them through the system dynamics, is therefore necessary. Our perception-aware model predictive control framework works in a receding-horizon fashion by iteratively solving a non-linear optimization problem. It is capable of running in real-time, fully onboard our lightweight, small-scale quadrotor using a low-power ARM computer, to- gether with a visual-inertial odometry pipeline. We validate our approach in experiments demonstrating (I) the contradiction between perception and action objectives, and (II) improved behavior in extremely challenging lighting conditions