Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot

Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities. Since the tasks are mostly unknown in advance, the robot has to adapt to a wide variety of terrains and workspaces during a mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and an anthropomorphic upper body to carry out complex tasks in environments too dangerous for humans. Due to its high number of degrees of freedom, controlling the robot with direct teleoperation approaches is challenging and exhausting. Supervised autonomy approaches are promising to increase quality and speed of control while keeping the flexibility to solve unknown tasks. We developed a set of operator assistance functionalities with different levels of autonomy to control the robot for challenging locomotion and manipulation tasks. The integrated system was evaluated in disaster response scenarios and showed promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 201

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

A mosaic of eyes

Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties

Design of a perception system for the Formula Student Driverless competition: from vehicle sensorization to SLAM

openFormula Student Driverless is an international racing competition held among universities, where the vehicles must complete a set of trials without any human intervention. Together with RaceUP, the Formula Student team of the University of Padova, this thesis represents the beginning of the project to build an autonomous prototype to compete in the Driverless Cup in the 2024 season. Three important aspects of an autonomous system design will be tackled: vehicle sensorization, perception, and simultaneous localization and mapping (SLAM), with the main focus on the development of the last one. The proposed approach for the back-end is based on the optimization of a factor graph, holding information about car poses and landmarks positions, by exploiting spatial and kinematic constraints between its vertices. The full back-end pipeline has been tested thoroughly, step by step, allowing to obtain satisfactory results on the different virtual tracks used for testing. Using both modern and classical techniques, we can process information produced by the stereo camera and the LIDAR, to be able to localize the colored cones delimiting the track. The estimation of cones positions serves then as input for other important modules of the car, such as the control part and the SLAM pipeline. Finally, a complete dataset has been acquired by properly sensorizing RaceUP's last year's car: having real data represents a helpful resource to make experiments and validate the system, even without the availability of the actual vehicle prototype.Formula Student Driverless is an international racing competition held among universities, where the vehicles must complete a set of trials without any human intervention. Together with RaceUP, the Formula Student team of the University of Padova, this thesis represents the beginning of the project to build an autonomous prototype to compete in the Driverless Cup in the 2024 season. Three important aspects of an autonomous system design will be tackled: vehicle sensorization, perception, and simultaneous localization and mapping (SLAM), with the main focus on the development of the last one. The proposed approach for the back-end is based on the optimization of a factor graph, holding information about car poses and landmarks positions, by exploiting spatial and kinematic constraints between its vertices. The full back-end pipeline has been tested thoroughly, step by step, allowing to obtain satisfactory results on the different virtual tracks used for testing. Using both modern and classical techniques, we can process information produced by the stereo camera and the LIDAR, to be able to localize the colored cones delimiting the track. The estimation of cones positions serves then as input for other important modules of the car, such as the control part and the SLAM pipeline. Finally, a complete dataset has been acquired by properly sensorizing RaceUP's last year's car: having real data represents a helpful resource to make experiments and validate the system, even without the availability of the actual vehicle prototype

To Collide or Not To Collide -- Exploiting Passive Deformable Quadrotors for Contact-Rich Tasks

With an increase in aerial vehicle applications, passive deformable quadrotors are getting significant attention in the research community due to their potential to perform physical interaction tasks. Such quadrotors are capable of undergoing collisions, both planned and unplanned, which are harnessed to induce deformation and retain stability by dissipating collision energies. In this article, we utilize one such passive deforming quadrotor, XPLORER, to complete various contact-rich tasks by exploiting its compliant chassis via various impact-aware planning and control algorithms. At the core of these algorithms is a novel external wrench estimation technique developed specifically for the unique multi-linked structure of XPLORER's chassis. The external wrench information is then employed for designing interaction controllers to obtain three additional flight modes: static-wrench application, disturbance rejection and yielding to the disturbance. These modes are then incorporated into a novel online exploration scheme to enable navigation in unknown flight spaces with only tactile feedback and generate a map of the environment without requiring additional sensors. Experiments show the efficacy of this scheme to generate maps of the previously unexplored flight space with an accuracy of 96.72%. Finally, we develop a novel collision-aware trajectory planner (CATAAN) to generate minimum time maneuvers for waypoint tracking by integrating collision-induced state jumps for both elastic and inelastic cases. We experimentally validate that minimum time trajectories can be obtained with CATAAN leading to a 40.38% reduction of settling time accompanied by improved tracking performance of a root mean squared error in position within 0.5cm as compared to 3cm of conventional methods

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic Systems (JINT), for the provided open-source software see http://github.com/ctu-mr