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Healthcare Robotics Lab

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Author: Kemp, Charles C. × Author: Killpack, Marc D. × End date: 2017 × Start date: 2010 ×

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Now showing 1 - 7 of 7
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    A Robotic System for Reaching in Dense Clutter that Integrates Model Predictive Control, Learning, Haptic Mapping, and Planning
    (Georgia Institute of Technology, 2014-09) Bhattacharjee, Tapomayukh ; Grice, Phillip M. ; Kapusta, Ariel ; Killpack, Marc D. ; Park, Daehyung ; Kemp, Charles C.
    We present a system that enables a robot to reach locations in dense clutter using only haptic sensing. Our system integrates model predictive control [1], learned initial conditions [2], tactile recognition of object types [3], haptic mapping, and geometric planning to efficiently reach locations using whole- arm tactile sensing [4]. We motivate our work, present a system architecture, summarize each component of the system, and present results from our evaluation of the system reaching to target locations in dense artificial foliage.
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    Fast Reaching in Clutter While Regulating Forces Using Model Predictive Control
    (Georgia Institute of Technology, 2013-10) Killpack, Marc D. ; Kemp, Charles C.
    Moving a robot arm quickly in cluttered and unmodeled workspaces can be difficult because of the inherent risk of high impact forces. Additionally, compliance by itself is not enough to limit contact forces due to multi-contact phenomena (jamming, etc.). The work in this paper extends our previous research on manipulation in cluttered environments by explicitly modeling robot arm dynamics and using model predictive control (MPC) with whole-arm tactile sensing to improve the speed and force control. We first derive discretetime dynamic equations of motion that we use for MPC. Then we formulate a multi-time step model predictive controller that uses this dynamic model. These changes allow us to control contact forces while increasing overall end effector speed. We also describe a constraint that regulates joint velocities in order to mitigate unexpected impact forces while reaching to a goal. We present results using tests from a simulated three link planar arm that is representative of the kinematics and mass of an average male’s torso, shoulder and elbow joints reaching in high and low clutter scenarios. These results show that our controller allows the arm to reach a goal up to twice as fast as our previous work, while still controlling the contact forces to be near a user-defined threshold.
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    Whole-arm Tactile Sensing for Beneficial and Acceptable Contact During Robotic Assistance
    (Georgia Institute of Technology, 2013-06) Grice, Phillip M. ; Killpack, Marc D. ; Jain, Advait ; Vaish, Sarvagya ; Hawke, Jeffrey ; Kemp, Charles C.
    Many assistive tasks involve manipulation near the care-receiver's body, including self-care tasks such as dressing, feeding, and personal hygiene. A robot can provide assistance with these tasks by moving its end effector to poses near the care-receiver's body. However, perceiving and maneuvering around the care-receiver's body can be challenging due to a variety of issues, including convoluted geometry, compliant materials, body motion, hidden surfaces, and the object upon which the body is resting (e.g., a wheelchair or bed). Using geometric simulations, we first show that an assistive robot can achieve a much larger percentage of end-effector poses near the care-receiver's body if its arm is allowed to make contact. Second, we present a novel system with a custom controller and whole-arm tactile sensor array that enables a Willow Garage PR2 to regulate contact forces across its entire arm while moving its end effector to a commanded pose. We then describe tests with two people with motor impairments, one of whom used the system to grasp and pull a blanket over himself and to grab a cloth and wipe his face, all while in bed at his home. Finally, we describe a study with eight able-bodied users in which they used the system to place objects near their bodies. On average, users perceived the system to be safe and comfortable, even though substantial contact occurred between the robot's arm and the user's body.
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    Reaching in clutter with whole-arm tactile sensing
    (Georgia Institute of Technology, 2013-04) Jain, Advait ; Killpack, Marc D. ; Edsinger, Aaron ; Kemp, Charles C.
    Clutter creates challenges for robot manipulation, including a lack of non-contact trajectories and reduced visibility for line-of-sight sensors. We demonstrate that robots can use whole-arm tactile sensing to perceive clutter and maneuver within it, while keeping contact forces low. We first present our approach to manipulation, which emphasizes the benefits of making contact across the entire manipulator and assumes the manipulator has low-stiffness actuation and tactile sensing across its entire surface. We then present a novel controller that exploits these assumptions. The controller only requires haptic sensing, handles multiple contacts, and does not need an explicit model of the environment prior to contact. It uses model predictive control with a time horizon of length one and a linear quasi-static mechanical model. In our experiments, the controller enabled a real robot and a simulated robot to reach goal locations in a variety of environments, including artificial foliage, a cinder block, and randomly generated clutter, while keeping contact forces low. While reaching, the robots performed maneuvers that included bending objects, compressing objects, sliding objects, and pivoting around objects. In simulation, whole-arm tactile sensing also outperformed per-link force–torque sensing in moderate clutter, with the relative benefits increasing with the amount of clutter.
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    Tactile Sensing over Articulated Joints with Stretchable Sensors
    (Georgia Institute of Technology, 2013-04) Bhattacharjee, Tapomayukh ; Jain, Advait ; Vaish, Sarvagya ; Killpack, Marc D. ; Kemp, Charles C.
    Biological organisms benefit from tactile sensing across the entire surfaces of their bodies. Robots may also be able to benefit from this type of sensing, but fully covering a robot with robust and capable tactile sensors entails numerous challenges. To date, most tactile sensors for robots have been used to cover rigid surfaces. In this paper, we focus on the challenge of tactile sensing across articulated joints, which requires sensing across a surface whose geometry varies over time. We first demonstrate the importance of sensing across joints by simulating a planar arm reaching in clutter and finding the frequency of contact at the joints. We then present a simple model of how much a tactile sensor would need to stretch in order to cover a 2 degree-of-freedom (DoF) wrist joint. Next, we describe and characterize a new tactile sensor made with stretchable fabrics. Finally, we present results for a stretchable sleeve with 25 tactile sensors that covers the forearm, 2 DoF wrist, and end effector of a humanoid robot. This sleeve enabled the robot to reach a target in instrumented clutter and reduce contact forces.
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    Visual Odometry and Control for an Omnidirectional Mobile Robot with a Downward-Facing Camera
    (Georgia Institute of Technology, 2010-10) Killpack, Marc D. ; Deyle, Travis ; Anderson, Cressel D. ; Kemp, Charles C.
    An omnidirectional Mecanum base allows for more flexible mobile manipulation. However, slipping of the Mecanum wheels results in poor dead-reckoning estimates from wheel encoders, limiting the accuracy and overall utility of this type of base. We present a system with a downwardfacing camera and light ring to provide robust visual odometry estimates. We mounted the system under the robot which allows it to operate in conditions such as large crowds or low ambient lighting. We demonstrate that the visual odometry estimates are sufficient to generate closed-loop PID (Proportional Integral Derivative) and LQR (Linear Quadratic Regulator) controllers for motion control in three different scenarios: waypoint tracking, small disturbance rejection, and sideways motion. We report quantitative measurements that demonstrate superior control performance when using visual odometry compared to wheel encoders. Finally, we show that this system provides highfidelity odometry estimates and is able to compensate for wheel slip on a four-wheeled omnidirectional mobile robot base.
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    Effects of Force Feedback and Arm Compliance on Teleoperation for a Hygiene Task,
    (Georgia Institute of Technology, 2010-07) King, Chih-Hung ; Killpack, Marc D. ; Kemp, Charles C.
    Teleoperated assistive robots with compliant arms may be well-suited to tasks that require contact with people and operation within human environments. However, little is known about the effects of force feedback and compliance on task performance. In this paper, we present a pilot study that we conducted to investigate the effects of force feedback and arm compliance on the performance of a simulated hygiene task. In this study, each subject (n=12) teleoperated a compliant arm to clean dry-erase marks off a mannequin with or without force feedback, and with lower or higher stiffness settings for the robot’s arm. Under all four conditions, subjects successfully removed the dry-erase marks, but trials performed with stiffer settings were completed significantly faster. The presence of force feedback significantly reduced the mean contact force, although the trials took significantly longer.