Space-based Robot Research Articles

Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows

This paper presents a real-time path planning approach for controlling the motion of space-based robots. The algorithm can plan three-dimensional trajectories for agents in a complex environment which includes numerous static and dynamic obstacles, path constraints, and/or performance constraints. This approach is extended based on the dynamic window approach (DWA). As the classic reactive method for obstacle avoidance, DWA uses an optimized function to select the best motion command. The original DWA optimization function consists of three weight terms. Changing the weights of these terms will change the behavior of the algorithm. In this paper, to improve the evaluation ability of the optimization function and the robot’s ability to adapt to the environment, a new optimization function is designed and combined with fuzzy logic to adjust the weights of each parameter of the optimization function. Given that DWA has the defect of local minima, which makes the robot hard to escape U-shaped obstacles, a dual dynamic window method and local goals are adopted in this article to help the robot escape local minima. By comparison, the proposed method is superior to traditional DWA and fuzzy DWA (F_DWA) in terms of computational efficiency, smoothness and security.

Maneuver Guidance and Formation Maintenance for Control of Leaderless Space-Robot Teams

A basic problem for a group of space-based robots is how to maintain a rigid formation during orbital maneuvers. To a leaderless team with homogeneous space-based robots, a multirobot controller architecture is proposed in which information flow is included. For each robot, there are two coupled feedback loops with different control objectives. One feedback loop is for guidance and is designed so that each robot can rapidly maneuver and rendezvous at the target point in a neighboring orbit. The other feedback loop is for formation control and is designed so that all robots in the group maintain a desired formation during a maneuver. A modified linear quadratic regulator method is used to design the guidance and control laws, where separate performance indexes are used for the two loops. By analyzing the interaction between the formation control and the maneuver guidance, a new restriction on the communication topology is proposed. An estimator for the state of the formation center is included in this control loop, and to guarantee a consensus on the formation center state estimates among all of the robots, a distributed Kalman filter based on the consensus algorithm is developed. Simulation results demonstrate the approach.

Real-time maneuver optimization of space-based robots in a dynamic environment: Theory and on-orbit experiments

This paper presents the development of a real-time path-planning optimization approach to controlling the motion of space-based robots. The algorithm is capable of planning three dimensional trajectories for a robot to navigate within complex surroundings that include numerous static and dynamic obstacles, path constraints and performance limitations. The methodology employs a unique transformation that enables rapid generation of feasible solutions for complex geometries, making it suitable for application to real-time operations and dynamic environments. This strategy was implemented on the Synchronized Position Hold Engage Reorient Experimental Satellite (SPHERES) test-bed on the International Space Station (ISS), and experimental testing was conducted onboard the ISS during Expedition 17 by the first author. Lessons learned from the on-orbit tests were used to further refine the algorithm for future implementations.

The Technologies for Remote Reconfiguration of Artificial Intelligence of Robotic Systems in Case of Mission or Driving Conditions Change

This report considers the creation of a controller intended for reconfiguring the artificial intelligence of robotic vehicles. The functional structure of hardware-reconfigurable digital module for intellectual control of robotic vehicles is proposed and further interaction between its functional modules and remote support center in different situations requiring reconfiguration is concerned. The procedures of self-check and self-testing of the hardware-reconfigurable digital module for intellectual control of mobile space-based robots are described, which are necessary to ensure reliability of reconfiguration.

A Fast Adaptive Artificial Neural Network Controller for Flexible Link Manipulators

This paper describes a hybrid approach to the problem of controlling flexible link manipulators in the dynamic phase of the trajectory. A flexible beam/arm is an appealing option for civil and military applications, such as space-based robot manipulators. However, flexibility brings with it unwanted oscillations and severe chattering which may even lead to an unstable system. To tackle these challenges, a novel control architecture scheme is presented. First, a neural network controller based on the robot’s dynamic equation of motion is elaborated. Its aim is to produce a fast and stable control of the joint position and velocity and damp the vibration of each arm. Then, an adaptive Cerebellar Model Articulation Controller (CMAC) is implemented to balance unmodeled dynamics, enhancing the precision of the control. Efficiency of the new controller obtained is tested on a two-link flexible manipulator. Simulation results on a dynamic trajectory with a sinusoidal form show the effectiveness of the proposed control strategy.

Adaptive cerebellar model articulation controller–nonlinear control system for flexible link manipulator

This paper deals with the problem of controlling flexible link manipulators on the dynamic phase of the trajectory. A flexible beam/arm is an appealing option for civil and military applications, such as space-based robot manipulators. However, flexibility brings with it unwanted oscillations and severe chattering which may even lead to an unstable system. To tackle these challenges, a novel control architecture scheme is presented. First, a nonlinear controller based on the equation of motion of the robot is elaborated. Its aim is to produce a stable tracking control and dump the vibration of the links. Then, an adaptive cerebellar model articulation controller is implemented to compensate for structured and unstructured uncertainties. Efficiency of the new controller obtained is tested facing an important variation of the dynamic parameters of the manipulator. Simulation results on a dynamic trajectory with a high acceleration/deceleration ratio show the effectiveness of the proposed control strategy.

Robust Control for Space-based Robot With External Disturbances and Uncertain Parameters in Joint Space

讨论了载体位置与姿态均不受控制的漂浮基空间机器人系统的鲁棒控制问题. 利用拉格朗日方法及系统动量守恒关系导出了漂浮基空间机器人欠驱动形式的系统动力学方程, 以确保得到的系统动力学方程关于一组适当选择的组合惯性参数呈线性函数关系. 在此基础上, 借助增广变量法针对末端爪手所持载荷参数存在不确定的情况, 提出了对外部扰动具有鲁棒性的漂浮基空间机器人关节空间轨迹跟踪的拟增广鲁棒控制方案. 所提控制方案由于充分利用空间机器人系统动量守恒关系, 消除了动力学方程中载体位置相关量, 因此具有不需要测量、反馈载体的位置、移动速度和移动加速度的显著优点; 且由于对系统不确定参数及外部扰动始终采用保持鲁棒性的方式而非在线估计的方式, 有效减少了计算量, 因此更适用于机载计算机运算能力有限的空间机器人控制系统实时在线应用. 一个平面两杆空间机器人系统的数值模拟仿真, 证实了方法的有效性.

A constrained motion algorithm for the Shuttle Remote Manipulator System

In the past decade, research advances in robot dynamic simulation have included algorithms for space robots operating as open chain mechanisms and earth-bound robot systems operating under motion constraints. Few researchers, however, have investigated a combination of these conditions, i.e., a space robot performing closed chain operations. This article presents a generalized formulation of the dynamic equations for a space-based robot in both open and closed chain kinematic configurations; this generalization represents a consolidation of several specialized formulations found in the current literature. Using this formulation, we present the development of new mathematical models and algorithms for constrained dynamics of the SRMS and their use in supplementing the on-orbit element simulation (OES) developed by NASA and Lockheed Engineering and Sciences Company (LESC). Emphasis is placed on maximizing computational efficiency in order to achieve real time implementation and on minimizing modifications to the original open chain OES code. Simulation results for grasping a fixed payload and frictionless planar sliding of a payload are provided. Constraint violations are minimized using a simple proportional/derivative control technique. >

Base reaction control for space-based robots operating in microgravity environment

Specialized robots are needed for space stations to conduct experiments and operations without disturbing the microgravity environment through base reactions and/or base motions. Approaches for controlling the robot base include 1) manipulators with redundant degrees of freedom, 2) actuators at the robot base, and 3) a redundant (balancing) arm. An approach making use of manipulator kinematic redundance is explored in detail and both locally and globally optimal trajectory management schemes are discussed. The inverse kinematic problem is solved at discrete time steps simultaneously with the minimization of the robot's base reactions. Numerical examples are presented including various robotic configurations and degrees of manipulator kinematic redundance. A significant reduction in base reactions is observed in each case.

Adaptive control of space-based robot manipulators

A control method is presented that achieves globally stable trajectory tracking in the presence of uncertainties in the inertial parameters of the system. The 15-degree-of-freedom (DOF) system dynamics are divided into two components: a 9-DOF invertible portion and 6-DOF noninvertible portion. A controller is then designed to achieve trajectory tracking of the invertible portion of the system, which consists of the manipulator joint positions and the orientation of the base. The motion of the noninvertible portion is bounded but otherwise unspecified. This portion of the system consists of the position of the robot's base and the position of the reaction wheels. A simulation is presented to demonstrate the effectiveness of the controller. A quadratic polynomial is used to generate the desired trajectory to illustrate the trajectory tracking capability of the controller.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A situated reasoning architecture for space-based repair and replace tasks

An area of increasing interest within AI and Robotics is the integration of techniques from both fields to the problem of controlling autonomous systems. Space-based systems, such as NASA's EVA Retriever, provide complex, realistic domains for this integration research. Space is a dynamic environment, where information is imperfect, and unexpected events are commonplace. As such, space-based robots need low level control for collision detection and avoidance, short-term load management, fine-grained motion, and other physical tasks. In addition, higher level control is required to focus strategic decision making as missions are assigned and carried out. Throughout the system, reasoning and control must be responsive to ongoing change taking place in the environment. This paper reports on current MITRE research aimed at bridging the gap between high level AI planning techniques and task-level robot programming for telerobotic systems. Our approach is based on incorporating situated reasoning into AI and Robotics systems in order to coordinate a robot's activity within its environment. Thus, the focus of this research is on controlling a robot embedded in an environment, as opposed to the generation and execution of lengthy robot plans. We present an integrated system under development in a “component maintenance” domain geared towards repair and replacement of Orbital Replacement Units (ORUs) designed for use aboard NASA's Space Station Freedom. The domain consists of a component-cell containing ORU components and a robot (manipulator and vision system) replacing worn and/or failed components based on the collection of components available at a given time. High level control reasons in “component space” in order to maximize the number operational component-cells over time, while the task-level controls sensors and effectors, detects collisions, and carries out pick and place tasks in “physical space.” Situated reasoning is used throughout the system to cope with, for example, nondeterministic component failures, the uncertain effects of task-level actions, and the actions of external agents operating in the domain.