Sampling-based Motion Planning for Active Multirotor System Identification

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.Comment: Published at ICRA 2017. Video available at https://www.youtube.com/watch?v=xtqrWbgep5

An Experimental Study on Airborne Landmine Detection Using a Circular Synthetic Aperture Radar

Many countries in the world are contaminated with landmines. Several thousand casualties occur every year. Although there are certain types of mines that can be detected from a safe stand-off position with tools, humanitarian demining is still mostly done by hand. As a new approach, an unmanned aerial system (UAS) equipped with a ground penetrating synthetic aperture radar (GPSAR) was developed, which is used to detect landmines, cluster munition, grenades, and improvised explosive devices (IEDs). The measurement system consists of a multicopter, a total station, an inertial measurement unit (IMU), and a frequency-modulated continuous-wave (FMCW) radar operating from 1 GHz to 4 GHz. The highly accurate localization of the measurement system and the full flexibility of the UAS are used to generate 3D-repeat-pass circular SAR images of buried antipersonnel landmines. In order to demonstrate the functionality of the system, 15 different dummy landmines were buried in a sandbox. The measurement results show the high potential of circular SAR for the detection of minimum metal mines. 11 out of 15 different test objects could be detected unambiguously with cm-level accuracy by examining depth profiles showing the amplitude of the targets response over the processing depth.Comment: 7 pages, 9 figure

State Estimation and Mission Planning for Precision-critical Aerial Field Robotics

Rotary-wing micro aerial vehicles (MAVs) are disrupting geomatics, logistics, and maintenance industries. Being airborne and easy to deploy they are a great tool to investigate large areas, inaccessible structures, and dangerous environments. Commercial platforms exist that automatically generate digital maps of cities, count warehouse inventory or perform non-destructive testing on industrial assets. Many more applications are envisioned but to turn those visions into reality, MAVs will have to fly closer to structures, physically engage with the environment, and integrate novel sensor modalities while providing ever more autonomy to make these advanced functionalities available to non-expert users. This thesis presents three precision-critical applications that contribute to the state of the art of industrial aerial robotics. The first contribution is a multi-agent aerial robotic system to search, pick-up and relocate metallic objects. To interact with small, partly moving objects, the aerial robot requires precise navigation and detection capabilities as well as a compliant grasping process. Our system combines (i) GNSS empowered visual-inertial state estimation with (ii) collision avoiding, model predictive position control and (iii) geometric computer vision into a precise autonomous transportation system. The system was deployed in various environments, including successful participation in the Mohamed Bin Zayed International Robotics Challenge 2017. It shows that basic autonomous aerial physical interaction in outdoor environments is possible given well-defined task and environment constraints such as known target properties and workspace. The second contribution is an MAV with ground-penetrating synthetic aperture radar (GPSAR) for humanitarian landmine detection. Airborne GPSAR is highly dependent on the flight path and the precision with which the radar antenna positions are determined. In this work we present a navigation framework that allows generating arbitrary circular and stripmap GPSAR missions controlled at low altitude above ground level. A self-calibrating, factor graph-based localization framework combines dual receiver RTK GNSS with inertial measurements to estimate the position of the radar antennas during flight. A custom hardware triggering mechanism ensures temporal correlation of the navigation sensors reaching sub-μs accuracy with respect to GNSS time. The system is self-contained and enables autonomous optical and radar surveys. In various experiments we show the advantages of the custom system design, including uniform radar sampling, self-calibration, and localization batch optimization. Finally, we validate mapping of buried objects to demonstrate the system's suitability for humanitarian landmine detection. The final contribution is an automatic coverage path planner to enable aerial surveys with nadir imaging sensors in obstructed environments. The algorithm is based on exact cellular decomposition, which splits an admissible polygon flight area into simple polygons that can be covered efficiently with consecutive back-and-forth motions. Our algorithm improves the connection of the individual polygon coverage patterns by considering multiple starting points for each polygon. Formulating the problem as an equality generalized traveling salesman problem and basing it on a strong computational geometry foundation created an implementation that plans optimal coverage missions within seconds. The open-source software is popular in the robotics community and has been a valuable mission planning tool for our GPSAR missions. Overall this thesis focuses on high-precision aerial robotic state estimation and MAV mission planning under spatial and motion constraints. All contributions have been tested in self-contained, real-world applications. The underlying software is available open-source to help bring forth a new generation of industrial aerial robots that operate autonomously in the vicinity of obstacles

Multi-Sensor Fusion for Aerial Vehicles

Low latency and robust state estimation is one of the key components to stable rotary-wing control. In this lecture style presentation, we summarize the different level of state estimation from rate control to position control and practical considerations when designing the system architecture and selecting sensors. Furthermore, we give recommendations on practical sensor fusion, where probabilistic sensor fusion frameworks have to be approximated given real-world sensor noise

Design and Evaluation of a Mixed Reality-based Human-Robot Interface for Teleoperation of Omnidirectional Aerial Vehicles

Omnidirectional aerial vehicles are an attractive solution for visual inspection tasks that require observations from different views. However, the decisional autonomy of modern robots is limited. Therefore, human input is often necessary to safely explore complex industrial environments. Existing teleoperation tools rely on on-board camera views or 3D renderings of the environment to improve situational awareness. Mixed-Reality (MR) offers an exciting alternative, allowing the user to perceive and control the robot's motion in the physical world. Furthermore, since MR technology is not limited by the hardware constraints of standard teleoperation interfaces, like haptic devices or joysticks, it allows us to explore new reference generation and user feedback methodologies. In this work, we investigate the potential of MR in teleoperating 6DoF aerial robots by designing a holographic user interface (see Fig. 1) to control their translational velocity and orientation. A user study with 13 participants is performed to assess the proposed approach. The evaluation confirms the effectiveness and intuitiveness of our methodology, independent of prior user experience with aerial vehicles or MR. However, prior familiarity with MR improves task completion time. The results also highlight limitation to line-of-sight operation at distances where relevant details in the physical environment can still be visually distinguished

History-aware Autonomous Exploration in Confined Environments using MAVs

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

Design and Evaluation of a Mixed Reality-based Human-Robot Interface for Teleoperation of Omnidirectional Aerial Vehicles

International audienceOmnidirectional aerial vehicles are an attractive solution for visual inspection tasks that require observations from different views. However, the decisional autonomy of modern robots is limited. Therefore, human input is often necessary to safely explore complex industrial environments. Existing teleoperation tools rely on on-board camera views or 3D renderings of the environment to improve situational awareness. Mixed-Reality (MR) offers an exciting alternative, allowing the user to perceive and control the robot's motion in the physical world. Furthermore, since MR technology is not limited by the hardware constraints of standard teleoperation interfaces, like haptic devices or joysticks, it allows us to explore new reference generation and user feedback methodologies. In this work, we investigate the potential of MR in teleoperating 6DoF aerial robots by designing a holographic user interface (see Fig. 1) to control their translational velocity and orientation. A user study with 13 participants is performed to assess the proposed approach. The evaluation confirms the effectiveness and intuitiveness of our methodology, independent of prior user experience with aerial vehicles or MR. However, prior familiarity with MR improves task completion time. The results also highlight limitation to line-of-sight operation at distances where relevant details in the physical environment can still be visually distinguished

Position acquisition for a multicopter-based synthetic aperture radar

Humanitarian demining is still mainly carried out by hand. The most trusted and widely used technical tool is probably the metal detector. However, these sensors are hand-held devices which are operated closely to the surface. To make the process of mine clearance safer, a ground penetrating synthetic aperture radar (GPSAR) was developed that can be operated on an autonomous flying unmanned aerial system (UAS). A key challenge of this system approach is the accurate position acquisition of the UAS. This paper compares a real time kinematic global navigation satellite system (RTK GNSS) and a total station with respect to UAS-based synthetic aperture radar (SAR) image processing. First, the systems and the associated signal processing chain will be briefly presented, then the trajectories and the processed SAR image will be compared

A Decentralized Multi-Agent Unmanned Aerial System to Search, Pick Up, and Relocate Objects

We present a fully integrated autonomous multi- robot aerial system for finding and collecting moving and static objects with unknown locations. This task addresses multiple relevant problems in search and rescue (SAR) robotics such as multi-agent aerial exploration, object detection and tracking, and aerial gripping. Usually, the community tackles these problems individually but the integration into a working system generates extra complexity which is rarely addressed. We show that this task can be solved reliably using only simple components. Our decentralized system uses accurate global state estimation, reactive collision avoidance, and sweep planning for multi-agent exploration. Objects are detected, tracked, and picked up using blob detection, inverse 3D-projection, Kalman filtering, visual-servoing, and a magnetic gripper. We evaluate the individual components of our system on the real platform. The full system has been deployed successfully in various public demonstrations, field tests, and the Mohamed Bin Zayed International Robotics Challenge 2017 (MBZIRC). Among the contestants we showed reliable performances and reached second place out of 17 in the individual challenge.Comment: Published at the 15th IEEE International Symposium on Safety, Security, and Rescue Robotics 2017 (SSRR 2017) in Shanghai, Chin