Small uncrewed aerial systems (sUASs) are useful tools for 3D reconstruction due to their speed, ease of use, and ability to access high-utility viewpoints. Today, most aerial survey approaches generate a preplanned coverage pattern assuming a planar target region. However, this is inefficient since it results in superfluous overlap and suboptimal viewing angles and does not utilize the entire flight envelope. In this work, we propose active path planning for photogrammetric reconstruction. Our main contribution is a view utility function based on Fisher information approximating the offline reconstruction uncertainty. The metric enables online path planning to make in-flight decisions to collect geometrically informative image data in complex terrain. We evaluate our approach in a photorealistic simulation. A viewpoint selection study shows that our metric leads to faster and more precise reconstruction than state-of-the-art active planning metrics and adapts to different camera resolutions. Comparing our online planning approach to an ordinary fixed-wing aerial survey yields 3.2 × faster coverage of 16 ha undulated terrain without sacrificing precision.

This paper presents a novel method for using Riemannian Motion Policies on volumetric maps, shown in the example of obstacle avoidance for Micro Aerial Vehicles (MAVs), Today, most robotic obstacle avoidance algorithms rely on sampling or optimization-based planners with volumetric maps. However, they are computationally expensive and often have inflexible monolithic architectures. Riemannian Motion Policies are a modular, parallelizable, and efficient navigation alternative but are challenging to use with the widely used voxel-based environment representations. We propose using GPU raycasting and tens of thousands of concurrent policies to provide direct obstacle avoidance using Riemannian Motion Policies in voxelized maps without needing map smoothing or pre-processing. Additionally, we present how the same method can directly plan on LiDAR scans without any intermediate map. We show how this reactive approach compares favorably to traditional planning methods and can evaluate up to 200 million rays per second. We demonstrate the planner successfully on a real MAV for static and dynamic obstacles. The presented planner is made available as an open-source package11https://github.com/ethz-asl/reactive_avoidance.

Micro aerial vehicles (MAVs) hold the potential for performing autonomous and contactless land surveys for the detection of landmines and explosive remnants of war (ERW). Metal detectors are the standard detection tool but must be operated close to and parallel to the terrain. A successful combination of MAVs with metal detectors has not been presented yet, as it requires advanced flight capabilities. To this end, we present an autonomous system to survey challenging undulated terrain using a metal detector mounted on a 5 degrees of freedom (DOF) MAV. Based on an online estimate of the terrain, our receding-horizon planner efficiently covers the area, aligning the detector to the surface while considering the kinematic and visibility constraints of the platform. As the survey requires resilient and accurate localization in diverse terrain, we also propose a factor graph-based online fusion of GNSS, IMU, and LiDAR measurements. We validate the robustness of the solution to individual sensor degeneracy by flying under the canopy of trees and over featureless fields. A simulated ablation study shows that the proposed planner reduces coverage duration and improves trajectory smoothness. Real-world flight experiments showcase autonomous mapping of buried metallic objects in undulated and obstructed terrain.

Drilling, grinding, and setting anchors on vertical walls are fundamental processes in everyday construction work. Manually doing these works is error-prone, potentially dangerous, and elaborate at height. Today, heavy mobile ground robots can perform automatic power tool work. However, aerial vehicles could be deployed in untraversable environments and reach inaccessible places. Existing drone designs do not provide the large forces, payload, and high precision required for using power tools. This work presents the first aerial robot design to perform versatile manipulation tasks on vertical concrete walls with continuous forces of up to 150 N. The platform combines a quadrotor with active suction cups for perching on walls and a lightweight, tiltable linear tool table. This combination minimizes weight using the propulsion system for flying, surface alignment, and feed during manipulation and allows precise positioning of the power tool. We evaluate our design in a concrete drilling application - a challenging construction process that requires high forces, accuracy, and precision. In 30 trials, our design can accurately pinpoint a target position despite perching imprecision. Nine visually guided drilling experiments demonstrate a drilling precision of 6 mm without further automation. Aside from drilling, we also demonstrate the versatility of the design by setting an anchor into concrete.

The ability to safely navigate through complex and cluttered environments is required for a wide range of robotics applications. This paper introduces a framework to compute safe global paths in maps represented as collections of 3D Signed Distance Function (SDF) submaps. Such maps are able to maintain global consistency in spite of odometry drift. However, computationally efficient global path planning in this context remains a challenging problem. We present a planning approach based on pre-computed local graphs, computed in each submap, that are linked to form a global path at planning time. To ensure globally safe paths, planning algorithms make frequent queries to the submap collection, which grows over time as the agent collects observational data. We present an efficient algorithm for performing these queries, through the use of a spatial hash table. We analyze the performance of our proposal extensively in simulation and real-world environments, and compare our approach to state- of-the-art planning approaches designed for monolithic maps, extended to submap-based maps. We show the efficacy of our method at adapting to global map deformations, while significantly reducing the planning time to an average of ~1.2 seconds, a reduction by 90 % compared to classical monolithic approaches.

Many scenarios require a robot to be able to explore its 3D environment online without human supervision. This is especially relevant for inspection tasks and search and rescue missions. To solve this high-dimensional path planning problem, sampling-based exploration algorithms have proven successful. However, these do not necessarily scale well to larger environments or spaces with narrow openings. This paper presents a 3D exploration planner based on the principles of Next-Best Views (NBVs). In this approach, a Micro-Aerial Vehicle (MAV)equipped with a limited field-of-view depth sensor randomly samples its configuration space to find promising future viewpoints. In order to obtain high sampling efficiency, our planner maintains and uses a history of visited places, and locally optimizes the robot's orientation with respect to unobserved space. We evaluate our method in several simulated scenarios, and compare it against a state-of-the-art exploration algorithm. The experiments show substantial improvements in exploration time (2 ⨯ faster), computation time, and path length, and advantages in handling difficult situations such as escaping dead-ends (up to 20 ⨯ faster). Finally, we validate the on-line capability of our algorithm on a computational constrained real world MAV.

This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These motions are often hard to find, especially for inexperienced users. Therefore, we consider system model identification in an active setting, where the vehicle autonomously decides what actions to take in order to quickly identify the model. Our algorithm approximates the belief dynamics of the system around a candidate trajectory using an extended Kalman filter (EKF). It uses sampling-based motion planning to explore the space of possible beliefs and find a maximally informative trajectory within a user-defined budget. We validate our method in simulation and on a real system showing the feasibility and repeatability of the proposed approach. Our planner creates trajectories which reduce model parameter convergence time and uncertainty by a factor of four.