On the Dynamics and Control of Free-floating Space Manipulator Systems in the Presence of Angular Momentum

Nanos, Kostas; Papadopoulos, Evangelos

doi:10.3389/frobt.2017.00026

Cited by 34 publications

(21 citation statements)

References 23 publications

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“…The kinematic and dynamic formulation of free-floating spacecrafts have been studied previously Umetani and Yoshida, 1989 ; Wilde et al, 2018 ; Nanos and Papadopoulos, 2017 . Here we give a abridged version of the same for completeness.…”

Section: Dynamic Formulationmentioning

confidence: 99%

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

et al. 2021

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This paper adds on to the on-going efforts to provide more autonomy to space robots and introduces the concept of programming by demonstration or imitation learning for trajectory planning of manipulators on free-floating spacecraft. A redundant 7-DoF robotic arm is mounted on small spacecraft dedicated for debris removal, on-orbit servicing and assembly, autonomous and rendezvous docking. The motion of robot (or manipulator) arm induces reaction forces on the spacecraft and hence its attitude changes prompting the Attitude Determination and Control System (ADCS) to take large corrective action. The method introduced here is capable of finding the trajectory that minimizes the attitudinal changes thereby reducing the load on ADCS. One of the critical elements in spacecraft trajectory planning and control is the power consumption. The approach introduced in this work carry out trajectory learning offline by collecting data from demonstrations and encoding it as a probabilistic distribution of trajectories. The learned trajectory distribution can be used for planning in previously unseen situations by conditioning the probabilistic distribution. Hence almost no power is required for computations after deployment. Sampling from a conditioned distribution provides several possible trajectories from the same start to goal state. To determine the trajectory that minimizes attitudinal changes, a cost term is defined and the trajectory which minimizes this cost is considered the optimal one.

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Section: Dynamic Formulationmentioning

confidence: 99%

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

et al. 2021

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“…As mentioned before, conventional probabilistic robotic arm planning methods cannot control the free-floating platform as the dynamics of the free-floating base is coupled with the movement of the robotic arm. To date, very limited number of research has investigated the coupled dynamics and kinematics problem (Nanos and Papadopoulos, 2017 ) and the planning methods are expensive and difficult to be used for the free-floating spacecraft attitude optimization with high-DOF robotic arms. In addition, the monocular cameras cluster system, which has 360° coverage, has no attitude pointing margin as needed for the conventional Attitude Determination and Control System (ADCS) sensors.…”

Section: Testbed Setups and The Demonstration Missionmentioning

confidence: 99%

Intelligent Spacecraft Visual GNC Architecture With the State-Of-the-Art AI Components for On-Orbit Manipulation

et al. 2021

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Conventional spacecraft Guidance, Navigation, and Control (GNC) architectures have been designed to receive and execute commands from ground control with minimal automation and autonomy onboard spacecraft. In contrast, Artificial Intelligence (AI)-based systems can allow real-time decision-making by considering system information that is difficult to model and incorporate in the conventional decision-making process involving ground control or human operators. With growing interests in on-orbit services with manipulation, the conventional GNC faces numerous challenges in adapting to a wide range of possible scenarios, such as removing unknown debris, potentially addressed using emerging AI-enabled robotic technologies. However, a complete paradigm shift may need years' efforts. As an intermediate solution, we introduce a novel visual GNC system with two state-of-the-art AI modules to replace the corresponding functions in the conventional GNC system for on-orbit manipulation. The AI components are as follows: (i) A Deep Learning (DL)-based pose estimation algorithm that can estimate a target's pose from two-dimensional images using a pre-trained neural network without requiring any prior information on the dynamics or state of the target. (ii) A technique for modeling and controlling space robot manipulator trajectories using probabilistic modeling and reproduction to previously unseen situations to avoid complex trajectory optimizations on board. This also minimizes the attitude disturbances of spacecraft induced on it due to the motion of the robot arm. This architecture uses a centralized camera network as the main sensor, and the trajectory learning module of the 7 degrees of freedom robotic arm is integrated into the GNC system. The intelligent visual GNC system is demonstrated by simulation of a conceptual mission—AISAT. The mission is a micro-satellite to carry out on-orbit manipulation around a non-cooperative CubeSat. The simulation shows how the GNC system works in discrete-time simulation with the control and trajectory planning are generated in Matlab/Simulink. The physics rendering engine, Eevee, renders the whole simulation to provide a graphic realism for the DL pose estimation. In the end, the testbeds developed to evaluate and demonstrate the GNC system are also introduced. The novel intelligent GNC system can be a stepping stone toward future fully autonomous orbital robot systems.

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“…Space manipulator systems, of the kinds used in space missions, have attracted much attention due to their high performance in active debris removal (Shan et al, 2016 ) and on-orbit servicing (Flores-Abad et al, 2014 ) in recent years. Over the past decades, a succession of technological advances has been made in both hardware device designs (Yoshida, 2009 ; Jaekel et al, 2018 ) and software algorithm developments (Nanos and Papadopoulos, 2017 ; Valverde and Tsiotras, 2018 ; Virgili-Llop and Romano, 2019 ; Liu et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vibration Characteristics of a Space Manipulator From Air Bearing Supported Test Data

Wei

Liang

et al. 2021

Front. Robot. AI

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Space manipulators have attracted much attention due to their implications in on-orbit servicing in recent years. Air bearing based support equipment is widely used for ground test to offset the effect of gravity. However, an air bearing support introduces a new problem caused by additional inertial and mass properties. Additional mass and inertial load will influence the dynamics behavior, especially stiffness information and vibration response of the whole ground test system. In this paper, a set of procedures are presented to remove the influence of air bearings and identify the true equivalent joint stiffness and damping from the test data of a motor-braked space manipulator with an air bearing support. First, inertia parameters are identified. Then, the equivalent joint stiffness and damping are determined by using a genetic algorithm (GA) method. Finally, true vibration characteristics of the manipulator are estimated by removing the additional inertia caused by the air bearings. Moreover, simulations and experiments are carried out to validate the presented procedures.

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On the Dynamics and Control of Free-floating Space Manipulator Systems in the Presence of Angular Momentum

Cited by 34 publications

References 23 publications

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

Intelligent Spacecraft Visual GNC Architecture With the State-Of-the-Art AI Components for On-Orbit Manipulation

Estimation of Vibration Characteristics of a Space Manipulator From Air Bearing Supported Test Data

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