Development of a micromanipulation system with force sensing

This article provides in-depth knowledge about our undergoing effort to develop an open architecture micromanipulation system with force sensing capabilities. The major requirement to perform any micromanipulation task effectively is to ensure the controlled motion of actuators within nanometer accuracy with low overshoot even under the influence of disturbances. Moreover, to achieve high dexterity in manipulation, control of the interaction forces is required. In micromanipulation, control of interaction forces necessitates force sensing in milli-Newton range with nano-Newton resolution. In this paper, we present a position controller based on a discrete time sliding mode control architecture along with a disturbance observer. Experimental verifications for this controller are demonstrated for 100, 50 and 10 nanometer step inputs applied to PZT stages. Our results indicate that position tracking accuracies up to 10 nanometers, without any overshoot and low steady state error are achievable. Furthermore, the paper includes experimental verification of force sensing within nano-Newton resolution using a piezoresistive cantilever endeffector. Experimental results are compared to the theoretical estimates of the change in attractive forces as a function of decreasing distance and of the pull off force between a silicon tip and a glass surface, respectively. Good agreement among the experimental data and the theoretical estimates has been demonstrated

Design and Control of Compliant Tensegrity Robots Through Simulation and Hardware Validation

To better understand the role of tensegrity structures in biological systems and their application to robotics, the Dynamic Tensegrity Robotics Lab at NASA Ames Research Center has developed and validated two different software environments for the analysis, simulation, and design of tensegrity robots. These tools, along with new control methodologies and the modular hardware components developed to validate them, are presented as a system for the design of actuated tensegrity structures. As evidenced from their appearance in many biological systems, tensegrity ("tensile-integrity") structures have unique physical properties which make them ideal for interaction with uncertain environments. Yet these characteristics, such as variable structural compliance, and global multi-path load distribution through the tension network, make design and control of bio-inspired tensegrity robots extremely challenging. This work presents the progress in using these two tools in tackling the design and control challenges. The results of this analysis includes multiple novel control approaches for mobility and terrain interaction of spherical tensegrity structures. The current hardware prototype of a six-bar tensegrity, code-named ReCTeR, is presented in the context of this validation

A novel plasticity rule can explain the development of sensorimotor intelligence

Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, the self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system specific modifications of the DEP rule but arise rather from the underlying mechanism of spontaneous symmetry breaking due to the tight brain-body-environment coupling. The new synaptic rule is biologically plausible and it would be an interesting target for a neurobiolocal investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video

Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

Projective simulation for artificial intelligence

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes retaine

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system