Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models

Soft robots have the potential to significantly change the way that robots interact with the environment and with humans. However, accurately modeling soft robot and soft actuator dynamics in order to perform model-based control can be extremely difficult. Deep neural networks are a powerful tool for modeling systems with complex dynamics such as the pneumatic, continuum joint, six degree-of-freedom robot shown in this paper. Unfortunately it is also difficult to apply standard model-based control techniques using a neural net. In this work, we show that the gradients used within a neural net to relate system states and inputs to outputs can be used to formulate a linearized discrete state space representation of the system. Using the state space representation, model predictive control (MPC) was developed with a six degree of freedom pneumatic robot with compliant plastic joints and rigid links. Using this neural net model, we were able to achieve an average steady state error across all joints of approximately 1 and 2° with and without integral control respectively. We also implemented a first-principles based model for MPC and the learned model performed better in terms of steady state error, rise time, and overshoot. Overall, our results show the potential of combining empirical modeling approaches with model-based control for soft robots and soft actuators

Visual Odometry and Control for an Omnidirectional Mobile Robot with a Downward-Facing Camera

©2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/IROS.2010.5649749Presented at the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 18-22 Oct. 2010, Taipei.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

Tactile Sensing over Articulated Joints with Stretchable Sensors

©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the World Haptics Conference (WHC), 2013, 14-17 April 2013.DOI: 10.1109/WHC.2013.6548392Biological 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

Vida del V.M. Juan de Avila

Enc. PastaSign.: *8, **2, A-Z8, Aa-Ee8, Ff

Effects of Force Feedback and Arm Compliance on Teleoperation for a Hygiene Task,

©2010 Springer-Verlag Berlin Heidelberg. The original publication is available at www.springerlink.com: http://dx.doi.org/10.1007/978-3-642-14064-8_36Presented at EuroHaptics 2010, Amsterdam, July 8-10, 2010.DOI: 10.1007/978-3-642-14064-8_36Teleoperated 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