Exploded view of the UniPilot module, with hardware, sensing, and compute components highlighted, alongside an image of the aerial platform that integrates the module in a process tank environment. Abstract-- This paper presents UniPilot, a compact hardware-software autonomy payload that can be integrated across diverse robot embodiments to enable autonomous operation in GPS-denied environments. The system integrates a multi-modal sensing suite including LiDAR, radar, vision, and inertial sensing for robust operation in conditions where uni-modal approaches may fail. UniPilot runs a complete autonomy software comprising multi-modal perception, exploration and inspection path planning, and learning-based navigation policies. The payload provides robust localization, mapping, planning, and safety and control capabilities in a single unit that can be deployed across a wide range of platforms. A large number of experiments are conducted across diverse environments and on a variety of robot platforms to validate the mapping, planning, and safe navigation capabilities enabled by the payload. I. INTRODUCTION With the progressing rise of autonomy, it becomes increasingly more important to be able to test a wide variety of complex methods and systems on easily re-creatable platforms. Especially of interest is to investigate and demonstrate a method's generality across different robot embodiments. Furthermore, the demand for autonomy in different robot actualization and in varied environments necessitates flexibility at the core of this design philosophy.

In this work, we present an autonomous inspection framework for remote sensing tasks in active open-pit mines. Specifically, the contributions are focused towards developing a methodology where an initial approximate operator-defined inspection plan is exploited by an online view-planner to predict an inspection path that can adapt to changes in the current mine-face morphology caused by route mining activities. The proposed inspection framework leverages instantaneous 3D LiDAR and localization measurements coupled with modelled sensor footprint for view-planning satisfying desired viewing and photogrammetric conditions. The efficacy of the proposed framework has been demonstrated through simulation in Feiring-Bruk open-pit mine environment and hardware-based outdoor experimental trials. The video showcasing the performance of the proposed work can be found here: https://youtu.be/uWWbDfoBvFc

This letter investigates the problem of controlling an aerial manipulator, composed of an omnidirectional tilting drone equipped with a five-degrees-of-freedom robotic arm. The robot has to interact with the environment to inspect structures and perform non-destructive measurements. A parallel force-impedance control technique is developed to establish contact with the designed surface with a desired force profile. During the interaction, a pushing phase is required to create a vacuum between the surface and the echometer sensor mounted at the end-effector, to measure the thickness of the interaction surface. Repetitive measures are performed to show the repeatability of the algorithm.

In this work, we present a novel actuation strategy for a suspended aerial platform. By utilizing an underactuation approach, we demonstrate the successful oscillation damping of the proposed platform, modeled as a spherical double pendulum. A state estimator is designed in order to obtain the deflection angles of the platform, which uses only onboard IMU measurements. The state estimator is an extended Kalman filter (EKF) with intermittent measurements obtained at different frequencies. An optimal state feedback controller and a PD+ controller are designed in order to dampen the oscillations of the platform in the joint space and task space respectively. The proposed underactuated platform is found to be more energy-efficient than an omnidirectional platform and requires fewer actuators. The effectiveness of our proposed system is validated using both simulations and experimental studies.

This paper presents an aerial platform capable of performing physically interactive tasks in unstructured environments with human-like dexterity under human supervision. This aerial platform consists of a humanoid torso attached to a hexacopter. A two-degree-of-freedom head and two five-degree-of-freedom arms equipped with softhands provide the requisite dexterity to allow human operators to carry out various tasks. A robust tendon-driven structure is purposefully designed for the arms, considerably reducing the impact of arm inertia on the floating base in motion. In addition, tendons provide flexibility to the joints, which enhances the robustness of the arm preventing damage in interaction with the environment. To increase the payload of the aerial system and the battery life, we use the concept of Suspended Aerial Manipulation, i.e., the flying humanoid can be connected with a tether to a structure, e.g., a larger airborne carrier or a supporting crane. Importantly, to maximize portability and applicability, we adopt a modular approach exploiting commercial components for the aerial base hardware and autopilot, while developing an outer stabilizing control loop to maintain the attitude, compensating for the tether force and for the humanoid head and arm motions. The humanoid can be controlled by a remote operator, thus effectively realizing a Suspended Aerial Manipulation Avatar. The proposed system is validated through experiments in indoor scenarios reproducing post-disaster tasks.

Aerial mapping systems are important for many surveying applications (e.g., industrial inspection or agricultural monitoring). Semi-autonomous mapping with GPS-guided aerial platforms that fly preplanned missions is already widely available but fully autonomous systems can significantly improve efficiency. Autonomously mapping complex 3D structures requires a system that performs online mapping and mission planning. This paper presents Osprey, an autonomous aerial mapping system with state-of-the-art multi-session mapping capabilities. It enables a non-expert operator to specify a bounded target area that the aerial platform can then map autonomously, over multiple flights if necessary. Field experiments with Osprey demonstrate that this system can achieve greater map coverage of large industrial sites than manual surveys with a pilot-flown aerial platform or a terrestrial laser scanner (TLS). Three sites, with a total ground coverage of $7085$ m$^2$ and a maximum height of $27$ m, were mapped in separate missions using $112$ minutes of autonomous flight time. True colour maps were created from images captured by Osprey using pointcloud and NeRF reconstruction methods. These maps provide useful data for structural inspection tasks.

In recent years, the robotics community has witnessed the development of several software stacks for ground and articulated robots, such as Navigation2 and MoveIt. However, the same level of collaboration and standardization is yet to be achieved in the field of aerial robotics, where each research group has developed their own frameworks. This work presents Aerostack2, a framework for the development of autonomous aerial robotics systems that aims to address the lack of standardization and fragmentation of efforts in the field. Built on ROS 2 middleware and featuring an efficient modular software architecture and multi-robot orientation, Aerostack2 is a versatile and platform-independent environment that covers a wide range of robot capabilities for autonomous operation. Its major contributions include providing a logical level for specifying missions, reusing components and sub-systems for aerial robotics, and enabling the development of complete control architectures. All major contributions have been tested in simulation and real flights with multiple heterogeneous swarms. Aerostack2 is open source and community oriented, democratizing the access to its technology by autonomous drone systems developers.

DJI has released a set of goggles that lets users take flight with their $999 Mavic Pro drone. Providing a first-person-view (FPV), the googles showcase a live streamed video of what the drone's onboard cameras capture while it pilots through the air. DJI Goggles combine a pair of large ultra-high-quality screens, long-range and low-latency wireless connectivity and direct control of photo and video capture – in order to provide'drone pilots a seamless bird's eye view of the world in full HD'. DJI has released a set of goggles that lets users take flight with their Mavic Pro drone. Providing a first-person-view (FPV), the googles showcase a live streamed video of what the drone's onboard cameras capture (right) while it pilots through the air Providing a first-person-view (FPV), the googles showcase a live streamed video of what the drone's onboard cameras capture while it pilots through the air.