14,013 research outputs found

    Motion Planning for Variable Topology Trusses: Reconfiguration and Locomotion

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    Truss robots are highly redundant parallel robotic systems that can be applied in a variety of scenarios. The variable topology truss (VTT) is a class of modular truss robots. As self-reconfigurable modular robots, a VTT is composed of many edge modules that can be rearranged into various structures depending on the task. These robots change their shape by not only controlling joint positions as with fixed morphology robots, but also reconfiguring the connectivity between truss members in order to change their topology. The motion planning problem for VTT robots is difficult due to their varying morphology, high dimensionality, the high likelihood for self-collision, and complex motion constraints. In this paper, a new motion planning framework to dramatically alter the structure of a VTT is presented. It can also be used to solve locomotion tasks that are much more efficient compared with previous work. Several test scenarios are used to show its effectiveness. Supplementary materials are available at https://www.modlabupenn.org/vtt-motion-planning/.Comment: 20 pages, 36 figure

    A sparsity-based method for fault-tolerant manipulation of a redundant robot

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    As an important part of the manufacturing industry, redundant robots can undertake heavy and tough tasks, which human operators are difficult to sustain. Such onerous and repetitive industrial manipulations, that is, positioning and carrying, impose heavy burdens on the load bearing for redundancy robots' joints. Under the circumstances of long-term and intense industrial operations, joints of redundant robots are conceivably to fall into functional failure, which may possibly cause abrupt joint lock or freeze at unknown time instants. Therefore, task accuracy by end-effectors tends to diminish considerably and gradually because of broken-down joints. In this paper, a sparsity-based method for fault-tolerant motion planning of redundant robots is provided for the first time. The developed fault-tolerant redundancy resolution approach is defined as L1-norm based optimization with immediate variables involved to avoid discontinuity in the dynamic solution process. Meanwhile, those potential faulty joint(s) can be located by the designed fault observer with the proposed fault-diagnosis algorithm. The proposed fault-tolerant motion planning method with fault diagnosis is dynamically optimized by resultant primal dual neural networks with provable convergence. Moreover, the sparsity of joint actuation by the proposed method can be enhanced by around 43.87% and 36.51%, respectively, for tracking circle and square paths. Simulation and experimental findings on a redundant robot (KUKA iiwa) prove the efficacy of the developed defect tolerant approach based on sparsity

    Avoiding space robot collisions utilizing the NASA/GSFC tri-mode skin sensor

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    Sensor based robot motion planning research has primarily focused on mobile robots. Consider, however, the case of a robot manipulator expected to operate autonomously in a dynamic environment where unexpected collisions can occur with many parts of the robot. Only a sensor based system capable of generating collision free paths would be acceptable in such situations. Recently, work in this area has been reported in which a deterministic solution for 2DOF systems has been generated. The arm was sensitized with 'skin' of infra-red sensors. We have proposed a heuristic (potential field based) methodology for redundant robots with large DOF's. The key concepts are solving the path planning problem by cooperating global and local planning modules, the use of complete information from the sensors and partial (but appropriate) information from a world model, representation of objects with hyper-ellipsoids in the world model, and the use of variational planning. We intend to sensitize the robot arm with a 'skin' of capacitive proximity sensors. These sensors were developed at NASA, and are exceptionally suited for the space application. In the first part of the report, we discuss the development and modeling of the capacitive proximity sensor. In the second part we discuss the motion planning algorithm

    Reaction-compensation technology for microgravity laboratory robots

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    Robots operating in the microgravity environment of an orbiting laboratory should be capable of manipulating payloads such that the motion of the robot does not disturb adjacent experiments. The current results of a NASA Lewis Research Center technology program to develop smooth, reaction-compensated manipulation based on both mechanism technology and trajectory planning strategies are present. Experimental validation of methods to reduce robot base reactions through the use of redundant degrees of freedom is discussed. Merits of smooth operation roller-driven robot joints for microgravity manipulators are also reviewed

    On the false positives and false negatives of the Jacobian Matrix in kinematically redundant parallel mechanisms

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    The Jacobian matrix is a highly popular tool for the control and performance analysis of closed-loop robots. Its usefulness in parallel mechanisms is certainly apparent, and its application to solve motion planning problems, or other higher level questions, has been seldom queried, or limited to non-redundant systems. In this paper, we discuss the shortcomings of the use of the Jacobian matrix under redundancy, in particular when applied to kinematically redundant parallel architectures with non-serially connected actuators. These architectures have become fairly popular recently as they allow the end-effector to achieve full rotations, which is an impossible task with traditional topologies. The problems with the Jacobian matrix in these novel systems arise from the need to eliminate redundant variables when forming it, resulting in both situations where the Jacobian incorrectly identifies singularities (false positive), and where it fails to identify singularities (false negative). These issues have thus far remained unaddressed in the literature. We highlight these limitations herein by demonstrating several cases using numerical examples of both planar and spatial architectures

    Hybrid Planning: Task-Space Control and Sampling-Based Planning

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    Haschke R. Hybrid Planning: Task-Space Control and Sampling-Based Planning. In: Workshop on Robot Motion Planning: Online, Reactive, and in Real-time. 2012.We propose a hybrid approach to motion planning for redundant robots, which combines a powerful control framework with a sampling-based planner. We argue that a suitably chosen task controller already manages a huge amount of trajectory planning work. However, due to its local approach to obstacle avoidance, it may get stuck in local minima. Therefore we augment it with a globally acting planner, which operates in a lower-dimensional search space, thus circumventing the curse of dimensionality afflicting modern, many-DoF robots

    Hybrid Planning: Task-Space Control and Sampling-Based Planning

    Get PDF
    Haschke R. Hybrid Planning: Task-Space Control and Sampling-Based Planning. In: Workshop on Robot Motion Planning: Online, Reactive, and in Real-time. 2012.We propose a hybrid approach to motion planning for redundant robots, which combines a powerful control framework with a sampling-based planner. We argue that a suitably chosen task controller already manages a huge amount of trajectory planning work. However, due to its local approach to obstacle avoidance, it may get stuck in local minima. Therefore we augment it with a globally acting planner, which operates in a lower-dimensional search space, thus circumventing the curse of dimensionality afflicting modern, many-DoF robots
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