skip to main content
Explore Scholarly Publications and Datasets in the NSF-PAR

Title: Encoder-less Robot Control for Space Operations with Continuum Robots
Continuum robots have strong potential for application in Space environments. However, their modeling is challenging in comparison with traditional rigid-link robots. The Kinematic-Model-Free (KMF) robot control method has been shown to be extremely effective in permitting a rigid-link robot to learn approximations of local kinematics and dynamics (“kinodynamics”) at various points in the robot’s task space. These approximations enable the robot to follow various trajectories and even adapt to changes in the robot’s kinematic structure. In this paper, we present the adaptation of the KMF method to a three-section, nine degrees-of-freedom continuum manipulator for both planar and spatial task spaces. Using only an external 3D camera, we show that the KMF method allows the continuum robot to converge to various desired set points in the robot’s task space, avoiding the complexities inherent in solving this problem using traditional inverse kinematics. The success of the method shows that a continuum robot can “learn” enough information from an external camera to reach and track desired points and trajectories, without needing knowledge of exact shape or position of the robot. We similarly apply the method in a simulated example of a continuum robot performing an inspection task on board the ISS.
Authors:
; ; ;
Award ID(s):
1718075
Publication Date:
NSF-PAR ID:
10295509
Journal Name:
IEEE Aerospace Conference
Page Range or eLocation-ID:
1-11
Sponsoring Org:
National Science Foundation
More Like this
  1. ( , ArXivorg)
    In this paper we derive a new capability for robots to measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight and unmapped environments, without requiring external infrastructure. We do so by capturing all of the paths that a WiFi signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to emulate antenna arrays in the air as a robot moves freely in 2D or 3D space. The small differences in the phase and amplitude of WiFi signals are thus processed with knowledge of a robots’ local displacements (often provided via inertial sensors) to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of i) a framework to accommodate arbitrary 2D and 3D trajectories, as well as continuous mobility of both transmitting and receiving robots, while computing AOA profiles between them and ii) an accompanying analysis that provides a lower bound on variance of AOA estimation as a function of robot trajectory geometry that is based on the Cramer Rao Bound and antenna array theory. Thismore »is a critical distinction with previous work on SAR that restricts robot mobility to prescribed motion patterns, does not generalize to the full 3D space, and/or requires transmitting robots to be static during data acquisition periods. In fact, we find that allowing robots to use their full mobility in 3D space while performing SAR, results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the trajectory; a computable quantity for which we derive a closed form. All theoretical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms. Our experimental results bolster our theoretical findings, demonstrating that 3D trajectories provide enhanced and consistent accuracy, with AOA error of less than 10 deg for 95% of trials. We also show that our formulation can be used with an off-the-shelf trajectory estimation sensor (Intel RealSense T265 tracking camera), for estimating the robots’ local displacements, and we provide theoretical as well as empirical results that show the impact of typical trajectory estimation errors on the measured AOA. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 square meter environment with occlusions.« less
  2. ; ; ( , Frontiers in Robotics and AI)
    Soft robots have shown great potential to enable safe interactions with unknown environments due to their inherent compliance and variable stiffness. However, without knowledge of potential contacts, a soft robot could exhibit rigid behaviors in a goal-reaching task and collide into obstacles. In this paper, we introduce a Sliding Mode Augmented by Reactive Transitioning (SMART) controller to detect the contact events, adjust the robot’s desired trajectory, and reject estimated disturbances in a goal reaching task. We employ a sliding mode controller to track the desired trajectory with a nonlinear disturbance observer (NDOB) to estimate the lumped disturbance, and a switching algorithm to adjust the desired robot trajectories. The proposed controller is validated on a pneumatic-driven fabric soft robot whose dynamics is described by a new extended rigid-arm model to fit the actuator design. A stability analysis of the proposed controller is also presented. Experimental results show that, despite modeling uncertainties, the robot can detect obstacles, adjust the reference trajectories to maintain compliance, and recover to track the original desired path once the obstacle is removed. Without force sensors, the proposed model-based controller can adjust the robot’s stiffness based on the estimated disturbance to achieve goal reaching and compliant interaction withmore »unknown obstacles.« less
  3. ; ; ; ; ; ( , The International Journal of Robotics Research)

    In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots’ local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOAmore »estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots’ motion, a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10◦for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m2environment with occlusions.

    « less
  4. ; ; ( , IEEE Transactions on Robotics)
    We consider a class of robotic systems composed of high-elongation linear actuators connected at universal joints. We derive the differential kinematics of such robots, and show that any instantaneous velocity of the nodes can be achieved through actuator motions if the graph describing the robot’s configuration is infinitesimally rigid. We formulate physical constraints that constrain the maximum and minimum length of each actuator, the minimum distance between unconnected actuators, the minimum angle between connected actuators, and constraints that ensure the robot avoids singular configurations. We present two planning algorithms that allow a linear actuator robot to locomote. The first algorithm repeatedly solves a nonlinear optimization problem online to move the robot’s center of mass in a desired direction for one time step. This algorithm can be used for an arbitrary linear actuator robot but does not guarantee persistent feasibility. The second method ensures persistent feasibility with a hierarchical coarse-fine planning decomposition, and applies to linear actuator robots with a certain symmetry property. We compare these two planning methods in simulation studies.
  5. ; ; ; ; ( , IEEE International Conference on Soft Robotics (RoboSoft))
    Soft continuum manipulators provide a safe alternative to traditional rigid manipulators, because their bodies can absorb and distribute contact forces. Soft manipulators have near infinite potential degrees of freedom, but a limited number of control inputs. This underactuation means soft continuum manipulators often lack either the controllability or the dexterity to achieve desired tasks. In this work, we present an extension of McKibben actuators, which have well-known models, that increases the controllable degrees of freedom using active reconfiguration of the constraining fibers. These Active Fiber Reinforced Elastomeric Enclosures (AFREEs) preform some combination of length change and twisting, depending on the fiber configuration. Experimental results shows that by changing the fiber angles within a range of -30 to 30 degrees and actuating the resulting configuration between 10.3 kPa and 24.1 kPa, we can achieve twists between ± 60 degrees and displacements between -2 and 4 mm. By additionally controlling the fiber lengths and pressure, we can modify the AFREE kinematics further, creating dynamic behaviors and trajectories of actuation. The presented actuator creates the possibility to reconFigure actuator kinematics to meet desired soft robot motions.
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email