Repeated Impact-Based Capture of a Spinning Object by a Dual-Arm Space Robot

This paper presents detumbling and capture of space debris by a dual-arm space robot for active space debris removal missions. Space debris, such as a malfunctioning satellite or a rocket upper stage, often has uncontrolled tumbling motion. It also has uncertainties in its parameters, such as inertial characteristics or surface frictional roughness. These factors make the debris capture missions difficult to accomplish. To cope with such challenging missions, we propose a detumbling and capture control method for a dual-arm robot based on repeated impact capable of suppressing the debris motion by repeatedly utilizing an effect of a passive damping factor in the contact characteristics. In this paper, as the initial step of a study on the repeated impact-based capture method, we assume that the capture target is a rocket upper stage that can be simply modeled as a cylindrical body and mainly has angular velocity motion in its principle axis of inertia. A motion tracking control law of an end-effector of the robot arm is introduced to maintain the repeated impact. The proposed control method enables the robot to accomplish the detumbling and capture without precise estimation of the inertial characteristics and surface frictional roughness of the debris. The validity of the proposed method is presented by numerical simulations and planar microgravity experiments using an air-floating system. In particular, the experimental evaluation shows the fundamental feasibility of the proposed method, and thus, the result contributes to a practical application

Robot Composite Learning and the Nunchaku Flipping Challenge

Advanced motor skills are essential for robots to physically coexist with humans. Much research on robot dynamics and control has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this paper, we present a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation. The method tackles advanced motor skills that require dynamic time-critical maneuver, complex contact control, and handling partly soft partly rigid objects. We also introduce the "nunchaku flipping challenge", an extreme test that puts hard requirements to all these three aspects. Continued from our previous presentations, this paper introduces the latest update of the composite learning scheme and the physical success of the nunchaku flipping challenge

Simultaneous Capture and Detumble of a Resident Space Object by a Free-Flying Spacecraft-Manipulator System

The article of record as published may be found at https://doi.org/10.3389/frobt.2019.00014A maneuver to capture and detumble an orbiting space object using a chaser spacecraft equipped with a robotic manipulator is presented. In the proposed maneuver, the capture and detumble objectives are integrated into a unified set of terminal constraints. Terminal constraints on the end-effector’s position and velocity ensure a successful capture, and a terminal constraint on the chaser’s momenta ensures a post-capture chaser-target system with zero angular momentum. The manipulator motion required to achieve a smooth, impact-free grasp is gradually stopped after capture, equalizing the momenta across all bodies, rigidly connecting the two vehicles, and completing the detumble of the newly formed chaser-target system without further actuation. To guide this maneuver, an optimization-based approach that enforces the capture and detumble terminal constraints, avoids collisions, and satisfies actuation limits is used. The solution to the guidance problem is obtained by solving a collection of convex programming problems, making the proposed guidance approach suitable for onboard implementation and real-time use. This simultaneous capture and detumble maneuver is evaluated through numerical simulations and hardware-in-the-loop experiments. Videos of the numerically simulated and experimentally demonstrated maneuvers are included as Supplementary Material

Dynamics and control of robotic systems for on-orbit objects manipulation

Multi-body systems (MSs) are assemblies composed of multiple bodies (either rigid or structurally flexible) connected among each other by means of mechanical joints. In many engineering fields (such as aerospace, aeronautics, robotics, machinery, military weapons and bio-mechanics) a large number of systems (e.g. space robots, aircraft, terrestrial vehicles, industrial machinery, launching systems) can be included in this category. The dynamic characteristics and performance of such complex systems need to be accurately and rapidly analyzed and predicted. Taking this engineering background into consideration, a new branch of study, named as Multi-body Systems Dynamics (MSD), emerged in the 1960s and has become an important research and development area in modern mechanics; it mainly addresses the theoretical modeling, numerical analysis, design optimization and control for complex MSs. The research on dynamics modeling and numerical solving techniques for rigid multi-body systems has relatively matured and perfected through the developments over the past half century. However, for many engineering problems, the rigid multi-body system model cannot meet the requirements in terms of precision. It is then necessary to consider the coupling between the large rigid motions of the MS components and their elastic displacements; thus the study of the dynamics of flexible MSs has gained increasing relevance. The flexible MSD involves many theories and methods, such as continuum mechanics, computational mechanics and nonlinear dynamics, thus implying a higher requirement on the theoretical basis. Robotic on-orbit operations for servicing, repairing or de-orbiting existing satellites are among space mission concepts expected to have a relevant role in a close future. In particular, many studies have been focused on removing significant debris objects from their orbit. While mission designs involving tethers, nets, harpoons or glues are among options studied and analyzed by the scientific and industrial community, the debris removal by means of robotic manipulators seems to be the solution with the longest space experience. In fact, robotic manipulators are now a well-established technology in space applications as they are routinely used for handling and assembling large space modules and for reducing human extravehicular activities on the International Space Station. The operations are generally performed in a tele-operated approach, where the slow motion of the robotic manipulator is controlled by specialized operators on board of the space station or at the ground control center. Grasped objects are usually cooperative, meaning they are capable to re-orient themselves or have appropriate mechanisms for engagement with the end-effectors of the manipulator (i.e. its terminal parts). On the other hand, debris removal missions would target objects which are often non-controlled and lacking specific hooking points. Moreover, there would be a distinctive advantage in terms of cost and reliability to conduct this type of mission profile in a fully autonomous manner, as issues like obstacle avoidance could be more easily managed locally than from a far away control center. Space Manipulator Systems (SMSs) are satellites made of a base platform equipped with one or more robotic arms. A SMS is a floating system because its base is not fixed to the ground like in terrestrial manipulators; therefore, the motion of the robotic arms affects the attitude and position of the base platform and vice versa. This reciprocal influence is denoted as "dynamic coupling" and makes the dynamics modeling and motion planning of a space robot much more complicated than those of fixed-base manipulators. Indeed, SMSs are complex systems whose dynamics modeling requires appropriate theoretical and mathematical tools. The growing importance SMSs are acquiring is due to their operational ductility as they are able to perform complicated tasks such as repairing, refueling, re-orbiting spacecraft, assembling articulated space structures and cleaning up the increasing amount of space debris. SMSs have also been employed in several rendezvous and docking missions. They have also been the object of many studies which verified the possibility to extend the operational life of commercial and scientific satellites by using an automated servicing spacecraft dedicated to repair, refuel and/or manage their failures (e.g. DARPA's Orbital Express and JAXA's ETS VII). Furthermore, Active Debris Removal (ADR) via robotic systems is one of the main concerns governments and space agencies have been facing in the last years. As a result, the grasping and post-grasping operations on non-cooperative objects are still open research areas facing many technical challenges: the target object identification by means of passive or active optical techniques, the estimation of its kinematic state, the design of dexterous robotic manipulators and end-effectors, the multi-body dynamics analysis, the selection of approaching and grasping maneuvers and the post-grasping mission planning are the main open research challenges in this field. The missions involving the use of SMSs are usually characterized by the following typical phases: 1. Orbital approach; 2. Rendez-vous; 3. Robotic arm(s) deployment; 4. Pre-grasping; 5. Grasping and post-grasping operations. This thesis project will focus on the last three. The manuscript is structured as follows: Chapter 1 presents the derivation of a multi-body system dynamics equations further developing them to reach their Kane's formulation; Chapter 2 investigates two different approaches (Particle Swarm Optimization and Machine Learning) dealing with a space manipulator deployment maneuver; Chapter 3 addresses the design of a combined Impedance+PD controller capable of accomplishing the pre-grasping phase goals and Chapter 4 is dedicated to the dynamic modeling of the closed-loop kinematic chain formed by the manipulator and the grasped target object and to the synthesis of a Jacobian Transpose+PD controller for a post-grasping docking maneuver. Finally, the concluding remarks summarize the overall thesis contribution

Robotic Manipulation and Capture in Space: A Survey

Space exploration and exploitation depend on the development of on-orbit robotic capabilities for tasks such as servicing of satellites, removing of orbital debris, or construction and maintenance of orbital assets. Manipulation and capture of objects on-orbit are key enablers for these capabilities. This survey addresses fundamental aspects of manipulation and capture, such as the dynamics of space manipulator systems (SMS), i.e., satellites equipped with manipulators, the contact dynamics between manipulator grippers/payloads and targets, and the methods for identifying properties of SMSs and their targets. Also, it presents recent work of sensing pose and system states, of motion planning for capturing a target, and of feedback control methods for SMS during motion or interaction tasks. Finally, the paper reviews major ground testing testbeds for capture operations, and several notable missions and technologies developed for capture of targets on-orbit

Data-driven learning for robot physical intelligence

The physical intelligence, which emphasizes physical capabilities such as dexterous manipulation and dynamic mobility, is essential for robots to physically coexist with humans. Much research on robot physical intelligence has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this dissertation, a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation is proposed. This method tackles advanced motor skills that require dynamic time-critical maneuver, complex contact control, and handling partly soft partly rigid objects. Besides, the power of crowdsourcing is brought to tackle case-specific engineering problem in the robot physical intelligence. Crowdsourcing has demonstrated great potential in recent development of artificial intelligence. Constant learning from a large group of human mentors breaks the limit of learning from one or a few mentors in individual cases, and has achieved success in image recognition, translation, and many other cyber applications. A robot learning scheme that allows a robot to synthesize new physical skills using knowledge acquired from crowdsourced human mentors is proposed. The work is expected to provide a long-term and big-scale measure to produce advanced robot physical intelligence

Simultaneous Capture and Detumble of a Resident Space Object by a Free-Flying Spacecraft-Manipulator System

A maneuver to capture and detumble an orbiting space object using a chaser spacecraft equipped with a robotic manipulator is presented. In the proposed maneuver, the capture and detumble objectives are integrated into a unified set of terminal constraints. Terminal constraints on the end-effector's position and velocity ensure a successful capture, and a terminal constraint on the chaser's momenta ensures a post-capture chaser-target system with zero angular momentum. The manipulator motion required to achieve a smooth, impact-free grasp is gradually stopped after capture, equalizing the momenta across all bodies, rigidly connecting the two vehicles, and completing the detumble of the newly formed chaser-target system without further actuation. To guide this maneuver, an optimization-based approach that enforces the capture and detumble terminal constraints, avoids collisions, and satisfies actuation limits is used. The solution to the guidance problem is obtained by solving a collection of convex programming problems, making the proposed guidance approach suitable for onboard implementation and real-time use. This simultaneous capture and detumble maneuver is evaluated through numerical simulations and hardware-in-the-loop experiments. Videos of the numerically simulated and experimentally demonstrated maneuvers are included as Supplementary Material

The dynamics of tethers and space-webs

The thesis 'The dynamics of tethers and space-webs' investigates the motion of the Motorized Momentum Exchange Tether (MMET) on an inclined orbit, and while deploying and retracting symmetric payloads. The MMET system is used as a basis for examining the stability of space-webs using a triangular structure of tethers while rotating. The motion of small robots is introduced as they move on the space-web, and their motions are found to influence the behaviour of the structure. Several methods of limiting the destabilising influences of the robots are considered, and are found to stabilise the web in most circumstances. A structured method for describing the rotations of a tether system is outlined, and different rotational systems are compared. This lays the foundation for the further chapters examining MMET dynamics on an inclined orbit and while deploying and recovering the payloads. Lagrange's equations are generated for the three cases, and are solved using standard numerical integration techniques. To emphasise the practical uses of the MMET system, several missions are analysed by using the system as a re-usable launcher for micro-satellite payloads

Robotic Ball Catching with an Eye-in-Hand Single-Camera System

In this paper, a unified control framework is proposed to realize a robotic ball catching task with only a moving single-camera (eye-in-hand) system able to catch flying, rolling, and bouncing balls in the same formalism. The thrown ball is visually tracked through a circle detection algorithm. Once the ball is recognized, the camera is forced to follow a baseline in the space so as to acquire an initial dataset of visual measurements. A first estimate of the catching point is initially provided through a linear algorithm. Then, additional visual measurements are acquired to constantly refine the current estimate by exploiting a nonlinear optimization algorithm and a more accurate ballistic model. A classic partitioned visual servoing approach is employed to control the translational and rotational components of the camera differently. Experimental results performed on an industrial robotic system prove the effectiveness of the presented solution. A motion-capture system is employed to validate the proposed estimation process via ground truth