Tilt-rotor aerial robots are more dynamic and versatile than fixed-rotor platforms, since the thrust vector and body orientation are decoupled. However, the coordination of servos and propellers (the allocation problem) is not trivial, especially accounting for overactuation and actuator dynamics. We incrementally build and present three novel allocation methods for tilt-rotor aerial robots, comparing them to state-of-the-art methods on a real system performing dynamic maneuvers. We extend the state-of-the-art geometric allocation into a differential allocation, which uses the platform's redundancy and does not suffer from singularities. We expand it by incorporating actuator dynamics and propeller power dynamics. These allow us to model dynamic propeller acceleration limits, bringing two main advantages: balancing propeller speed without the need for nullspace goals and allowing the platform to selectively turn off propellers during flight, opening the door to new manipulation possibilities. We also use actuator dynamics and limits to normalize the allocation problem, making it easier to tune and allowing it to track 70% faster trajectories than a geometric allocation.

Recently, the utilization of aerial manipulators for performing pushing tasks in non-destructive testing (NDT) applications has seen significant growth. Such operations entail physical interactions between the aerial robotic system and the environment. End-effectors with multiple contact points are often used for placing NDT sensors in contact with a surface to be inspected. Aligning the NDT sensor and the work surface while preserving contact, requires that all available contact points at the end-effector tip are in contact with the work surface. With a standard full-pose controller, attitude errors often occur due to perturbations caused by modeling uncertainties, sensor noise, and environmental uncertainties. Even small attitude errors can cause a loss of contact points between the end-effector tip and the work surface. To preserve full alignment amidst these uncertainties, we propose a control strategy which selectively deactivates angular motion control and enables direct force control in specific directions. In particular, we derive two essential conditions to be met, such that the robot can passively align with flat work surfaces achieving full alignment through the rotation along non-actively controlled axes. Additionally, these conditions serve as hardware design and control guidelines for effectively integrating the proposed control method for practical usage. Real world experiments are conducted to validate both the control design and the guidelines.

Fast and reliable obstacle avoidance is an important task for mobile robots. In this work, we propose an efficient reactive system that provides high-quality obstacle avoidance while running at hundreds of hertz with minimal resource usage. Our approach combines wavemap, a hierarchical volumetric map representation, with a novel hierarchical and parallelizable obstacle avoidance algorithm formulated through Riemannian Motion Policies (RMP). Leveraging multi-resolution obstacle avoidance policies, the proposed navigation system facilitates precise, low-latency (36ms), and extremely efficient obstacle avoidance with a very large perceptive radius (30m). We perform extensive statistical evaluations on indoor and outdoor maps, verifying that the proposed system compares favorably to fixed-resolution RMP variants and CHOMP. Finally, the RMP formulation allows the seamless fusion of obstacle avoidance with additional objectives, such as goal-seeking, to obtain a fully-fledged navigation system that is versatile and robust. We deploy the system on a Micro Aerial Vehicle and show how it navigates through an indoor obstacle course. Our complete implementation, called waverider, is made available as open source1.

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.

In this work we address the challenging problem of manipulating a flexible link, like a rope, with an aerial robot. Inspired by spraying tasks in construction and maintenance scenarios, we consider the case in which an autonomous end-effector (e.g., a spray nozzle moved by a robot or a human operator) is connected to a fixed point by a rope (e.g., a hose). To avoid collisions between the rope and the environment while the end-effector moves, we propose the use of an aerial robot as a flying companion to properly manipulate the rope away from collisions. The aerial robot is attached to the rope between the end-effector and the fixed point. Assuming no direct control of the end-effector (e.g., when operated by a human), we design a reactive and fast motion planner for the aerial robot. Grounding on the theory of Forced Geometric Fabrics, we design a motion planner that generates trajectories to drive the aerial robot to follow the end-effector, while manipulating the rope to avoid collisions in cluttered environments. To include the complex behavior of the flexible link, we propose a rope model that estimates its real-time state under forces and position-based interactions, as well as collisions with obstacle surfaces. Finally, we evaluate the system behavior and the motion planner performance in simulations, as well as in real-world experiments on an original spray painting application.

In this paper, we propose a resource-efficient approach to provide an autonomous UAV with an on-board perception method to detect safe, hazard-free landing sites during flights over complex 3D terrain. We aggregate 3D measurements acquired from a sequence of monocular images by a Structure-from-Motion approach into a local, robot-centric, multi-resolution elevation map of the overflown terrain, which fuses depth measurements according to their lateral surface resolution (pixel-footprint) in a probabilistic framework based on the concept of dynamic Level of Detail. Map aggregation only requires depth maps and the associated poses, which are obtained from an on-board Visual Odometry algorithm. An efficient landing site detection method then exploits the features of the underlying multi-resolution map to detect safe landing sites based on slope, roughness, and quality of the reconstructed terrain surface. The evaluation of the performance of the mapping and landing site detection modules are analyzed independently and jointly in simulated and real-world experiments in order to establish the efficacy of the proposed approach.

This article presents and validates active interaction force control and planning for fully actuated and omnidirectional aerial manipulation platforms, with the goal of aerial contact inspection in unstructured environments. We present a variable axis-selective impedance control which integrates direct force control for intentional interaction, using feedback from an on-board force sensor. The control approach aims to reject disturbances in free flight, while handling unintentional interaction and actively controlling desired interaction forces. A fully actuated and omnidirectional tilt-rotor aerial system is used to show capabilities of the control and planning methods. Experiments demonstrate disturbance rejection, push-and-slide interaction, and force-controlled interaction in different flight orientations. The system is validated as a tool for nondestructive testing of concrete infrastructure, and statistical results of interaction control performance are presented and discussed.

This work presents a method to control omnidirectional micro aerial vehicles (OMAVs) for the tracking of 6-DoF trajectories in free space. A rigid body model based approach is applied in a receding horizon fashion to generate optimal wrench commands that can be constrained to meet limits given by the mechanical design and actuators of the platform. Allocation of optimal actuator commands is performed in a separate step. A disturbance observer estimates forces and torques that may arise from unmodeled dynamics or external disturbances and fuses them into the optimization to achieve offset-free tracking. Experiments on a fully overactuated MAV show the tracking performance and compare it against a classical PD-based controller.