37 research outputs found
Mass digitization system
No full textA mass digitization system may include a work Surface rotat ably coupled to a Support structure, and a motor coupled to the work Surface to selectively rotate the work Surface. An imag ing station may be positioned proximate the work Surface to capture digital images of items on a receiving Surface of the work surface. The motor may rotate the work surface and the imaging station may include an imaging device to capture images of items on the receiving Surface as the items are positioned in the image capture area of the imaging device
Real-time evolutionary model predictive control using a graphics processing unit
No full textWith humanoid robots becoming more complex and operating in un-modeled or human environments, there is a growing need for control methods that are scalable and robust, while still maintaining compliance for safety reasons. Model Predictive Control (MPC) is an optimal control method which has proven robust to modeling error and disturbances. However, it can be difficult to implement for high degree of freedom (DoF) systems due to the optimization problem that must be solved. While evolutionary algorithms have proven effective for complex large-scale optimization problems, they have not been formulated to find solutions quickly enough for use with MPC. This work details the implementation of a parallelized evolutionary MPC (EMPC) algorithm which is able to run in real-time through the use of a Graphics Processing Unit (GPU). This parallelization is accomplished by simulating candidate control input trajectories in parallel on the GPU. We show that this framework is more flexible in terms of cost function definition than traditional MPC and that it shows promise for finding solutions for high DoF systems
Limit-point buckling analyses using solid, shell and solid–shell elements
No full textIn this paper, the recently-developed solid-shell element SHB8PS is used for the analysis of a representative set of popular limit-point buckling benchmark problems. For this purpose, the element has been implemented in Abaqus/Standard finite element software and the modified Riks method was employed as an efficient path-following strategy. For the. benchmark problems tested, the new element shows better performance compared to solid elements and often performs as well as state-of-the-art shell elements. In contrast to shell elements, it allows for the accurate prescription of boundary conditions as applied to the actual edges of the structure.Agence Nationale de la Recherche, France (ANR-005-RNMP-007
Automated Tracking and Estimation for Control of Non-rigid Cloth
No full textThis report is a summary of research conducted on cloth tracking for automated textile manufacturing during a two semester long research course at Georgia Tech. This work was completed in 2009. Advances in current sensing technology such as the Microsoft Kinect would now allow me to relax certain assumptions and generally improve the tracking performance. This is because a major part of my approach described in this paper was to track features in a 2D image and use these to estimate the cloth deformation. Innovations such as the Kinect would improve estimation due to the automatic depth information obtained when tracking 2D pixel locations. Additionally, higher resolution camera images would probably give better quality feature tracking. However, although I would use different technology now to implement this tracker, the algorithm described and implemented in this paper is still a viable approach which is why I am publishing this as a tech report for reference. In addition, although the related work is a bit exhaustive, it will be useful to a reader who is new to methods for tracking and estimation as well as modeling of cloth
Fast reaching in clutter while regulating forces using model predictive control
No full textMoving 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 discrete-time 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\u27s 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
Model predictive control for fast reaching in clutter
No full textA key challenge for haptically reaching in dense clutter is the frequent contact that can occur between the robot’s arm and the environment. We have previously used single-time-step model predictive control (MPC) to enable a robot to slowly reach into dense clutter using a quasistatic mechanical model. Rapid reaching in clutter would be desirable, but entails additional challenges due to dynamic phenomena that can lead to higher forces from impacts and other types of contact. In this paper, we present a multi-time-step MPC formulation that enables a robot to rapidly reach a target position in dense clutter, while regulating whole-body contact forces to be below a given threshold. Our controller models the dynamics of the arm in contact with the environment in order to predict how contact forces will change and how the robot’s end effector will move. It also models how joint velocities will influence potential impact forces. At each time step, our controller uses linear models to generate a convex optimization problem that it can solve efficiently. Through tens of thousands of trials in simulation, we show that with our dynamic MPC a simulated robot can, on average, reach goals 1.4 to 2 times faster than our previous controller, while attaining comparable success rates and fewer occurrences of high forces. We also conducted trials using a real 7 degree-of-freedom (DoF) humanoid robot arm with whole-arm tactile sensing. Our controller enabled the robot to rapidly reach target positions in dense artificial foliage while keeping contact forces low
Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models
No full textSoft 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
Comparing Model Predictive Control and input shaping for improved response of low-impedance robots
No full textWith an increasing number of robots that can exhibit compliant behavior for safety in operating near humans (either through passive components or active control), additional methods for controlling these robots are needed. In particular, robot arms with low impedance can be safer for working in delicate environments if the effects of dealing with an underdamped robot system can be mitigated to improve performance. This paper focuses on comparing methods that allow a seven degree of freedom Series Elastic Actuator arm to operate with very low impedance while mitigating unwanted oscillation at the end effector. We show that by implementing feedback linearizion in conjunction with input shaping we can reduce residual oscillation for a seven degree of freedom robot arm. We also show that a Cartesian Model Predictive Controller (MPC) is able to significantly reduce residual oscillations while maintaining compliance. Comparing these two methods shows that for our tests for large displacements, MPC has a maximum overshoot of only 0.26% in the worst case where input shaping has at least 5.80% overshoot even in the best case. In addition, despite the fact that MPC is a feedback controller (unlike the open-loop input shaping method), it is still able to maintain compliance at the joints and end effector where we estimated MPC to exhibit a stiffness of 234 N/m as compared to the nominal low impedance controller with a stiffness of 262 N/m. Similar to input shaping (which is a command generation method), MPC is able to generate these commands without any slewing or path planning
Comparison of linearized dynamic robot manipulator models for model predictive control
No full textWhen using model predictive control (MPC) to perform low-level control of humanoid robot manipulators, computational tractability can be a limiting factor. This is because using complex models can have a negative impact on control performance, especially as the number of degrees of freedom increases. In an effort to address this issue, we compare three different methods for linearizing the dynamics of a robot arm for MPC. The methods we compare are a Taylor Series approximation method (TS), a Fixed-State approximation method (FS), and a Coupling-Torque approximation method (CT). In simulation we compare the relative control performance when these models are used with MPC. Through these comparisons we show that the CT approximation method is best for reducing model complexity without reducing the performance of MPC. We also demonstrate the CT approximation method on two real robots, a robot with series elastic actuators and a soft, inflatable robot
Variable stiffness adaptation to mitigate system failure in inflatable robots
No full textAlthough inflatable soft robots are not yet a common robot platform, air leaking from the internal structure is a common and undesirable mode of failure for these platforms. In this paper we demonstrate a method to detect leaks in the structural chamber of an inflatable, pneumatically actuated robot. We then show that our method can adaptively lower commanded joint stiffness which slows the mass flow rate of the leak. This extends the operational life of the robot by decreasing long term error during operation by as much as 50% of the steady state error at the end effector when compared to the same leak if our adaptation method is not used. In future applications where we expect soft, inflatable robots to be useful, our methods can enable failure mitigation in resource-limited situations such as space exploration or disaster response
