W. S. Owen

Bio: W. S. Owen is an academic researcher from University of Waterloo. The author has contributed to research in topics: Machine tool & Machining. The author has an hindex of 11, co-authored 20 publications receiving 315 citations. Previous affiliations of W. S. Owen include University of Toronto.

The reduction of stick-slip friction in hydraulic actuators

Abstract: The stick-slip friction phenomenon is observed near zero relative velocity, during the transition from static to dynamic friction, when static friction is greater than dynamic friction. This nonlinear change in friction force over a small change in velocity results in difficulties in achieving accurate and repeatable position control. In some cases, the actuator position controller reaches a limit cycle (hunting effect). Friction compensation at low speeds has traditionally been approached through various control techniques. This paper proposes an alternative solution, namely, friction avoidance. By rotating the piston and rod, the Stribeck region of the friction-velocity curve is avoided and the axial friction opposing the piston movement is approximately linearized. Simulation and experimental results are presented to validate this approach.

A multi-arm robotic system for optimal sculpting

Abstract: Recent research has considered robotic machining as an alternative to traditional CNC machining. However, one of the problems with serial link manipulators is that they have a low inherent stiffness due to the cantilever design of the links and low torsional stiffness of the actuators. This problem is addressed during the resolution of a Cartesian-space tool path, defined relative to the blank, into joint space using the pseudo-inverse method of redundancy resolution. The two-armed system is modeled as a single system using a relative Jacobian to relate the joint motions to the relative motion of the tool and blank. The null space of the relative Jacobian is used to optimize the joint trajectories by minimizing the system compliance factor. A min-max optimization and a null-space search are investigated in order to produce a global optimum. Simulation results show that stiff trajectories lower the level of torque consumption, which allows for an increase in productivity. The deflection of the tool relative to the blank is decreased as well.

Laser Scanner-based End-effector Tracking and Joint Variable Extraction for Heavy Machinery

Abstract: A survey of mining accidents has revealed that over 30% of all truck loading accidents can be addressed by providing dipper positioning feedback to the shovel operator. In this paper, a novel approach is presented for estimating a mining shovel’s dipper pose to obtain its arm geometry in real-time utilizing a two-dimensional laser scanner. The low spatial resolution of laser scanners and the need for accurate initialization challenge the reliability and accuracy of most laser-scanner-based object tracking methods. This work addresses these issues by using the shovel dipper’s kinematics model and position history, in conjunction with the dipper geometrical model, to track the dipper in space. The proposed method uses a bootstrap particle filter with a distance transformation in order to perform a global search in the workspace. The particle filter’s result is then used as the initial pose for an Iterative Closest Point algorithm that increases the accuracy of the pose estimate. The proposed method can be applied to other laser scanner-based object tracking applications in outdoor environments. Experiments performed on a mining shovel demonstrate the reliability, accuracy, and computational efficiency of the proposed approach. Moreover, using a single proximal sensor can simplify mounting, reduce maintenance costs and machine down time, and enhance tracking reliability.

Online incremental learning of inverse dynamics incorporating prior knowledge

Abstract: Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship of a manipulator, eliminating the need for any knowledge of the system model. Ideally, such algorithms should be able to process large amounts of data in an online and incremental manner, thus allowing the system to adapt to changes in its model structure or parameters. LocallyWeighted Projection Regression (LWPR) and other non-parametric regression techniques have been applied to learn manipulator inverse dynamics. However, a common issue amongst these learning algorithms is that the system is unable to generalize well outside of regions where it has been trained. Furthermore, learning commences entirely from 'scratch,' making no use of any a-priori knowledge which may be available. In this paper, an online, incremental learning algorithm incorporating prior knowledge is proposed. Prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the available prior information. It is shown that the proposed approach allows the system to operate well even without any initial training data, and further improves performance with additional online training.

Acceleration and torque redistribution for a dual-manipulator system

Abstract: Recent research has considered robotic machining as an alternative to traditional computer numerical control machining, particularly for prototyping applications. However, unlike traditional machine tools, robots are subject to relatively larger dynamic disturbances and operate closer to their torque limits. These factors, combined with inaccurate manipulator and machining process models, can cause joint actuator saturation during operation. This paper presents a trajectory planner that will reduce torques that are near saturation by generating trajectories with a weighted pseudoinverse. Using a relative Jacobian, the tool path is resolved into joint trajectories at the acceleration level. This paper presents a new method for selecting the weighting matrix based on the proximity of the joint torques to saturation limits. This weighting reduces the joint accelerations contributing the most to the torques near saturation, thereby reducing the joint torques. The accelerations of other joints increase to satisfy the increased demand. The effectiveness of the acceleration and torque redistribution algorithm has been demonstrated via extensive simulations.

Modelling and Control of Robot Manipulators

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Adaptive Control of Hydraulic Actuators With LuGre Model-Based Friction Compensation

Abstract: This paper concerns high-accuracy tracking control for hydraulic actuators with nonlinear friction compensation Typically, LuGre model-based friction compensation has been widely employed in sundry industrial servomechanisms However, due to the piecewise continuous property, it is difficult to be integrated with backstepping design, which needs the time derivation of the employed friction model Hence, nonlinear model-based hydraulic control rarely sets foot in friction compensation with nondifferentiable friction models, such as LuGre model, Stribeck effects, although they can give excellent friction description and prediction In this paper, a novel continuously differentiable nonlinear friction model is first derived by modifying the traditional piecewise continuous LuGre model, then an adaptive backstepping controller is proposed for precise tracking control of hydraulic systems to handle parametric uncertainties along with nonlinear friction compensation In the formulated nonlinear hydraulic system model, friction parameters, servovalve null shift, and orifice-type internal leakage are all uniformly considered in the proposed controller The controller theoretically guarantees asymptotic tracking performance in the presence of parametric uncertainties, and the robustness against unconsidered dynamics, as well as external disturbances, is also ensured via Lyapunov analysis The effectiveness of the proposed controller is demonstrated via comparative experimental results

Robot machining: recent development and future research issues

Abstract: Early studies on robot machining were reported in the 1990s. Even though there are continuous worldwide researches on robot machining ever since, the potential of robot applications in machining has yet to be realized. In this paper, the authors will first look into recent development of robot machining. Such development can be roughly categorized into researches on robot machining system development, robot machining path planning, vibration/chatter analysis including path tracking and compensation, dynamic, or stiffness modeling. These researches will obviously improve the accuracy and efficiency of robot machining and provide useful references for developing robot machining systems for tasks once thought to only be capable by CNC machines. In order to advance the technology of robot machining to the next level so that more practical and competitive systems could be developed, the authors suggest that future researches on robot machining should also focus on robot machining efficiency analysis, stiffness map-based path planning, robotic arm link optimization, planning, and scheduling for a line of machining robots.

Review of Control and Sensor System of Flexible Manipulator

Abstract: Over the last few decades, extensive use of flexible manipulators in various robotic applications has made it as one of the research interests for many scholars over the world. Recent studies on the modeling, sensor systems and controllers for the applications of flexible robotic manipulators are reviewed in order to complement the previous literature surveyed by Benosman & Vey (Robotica 22:533---545, 2004) and Dwivedy & Eberhard (Mech. Mach. Theory 41:749---777, 2006) . A brief introduction of the essential modeling techniques is first presented, followed by a review of the practical alternatives of sensor systems that can help scientists or engineers to choose the appropriate sensors for their applications. It followed by the main goal of this paper with a comprehensive review of the control strategies for the flexible manipulators and flexible joints that were studied in recent literatures. The issues for controlling flexible manipulators are highlighted. Most of the noteworthy control techniques that were not covered in the recent surveys in references (Benosman & Vey Robotica 22:533---545, 2004; Dwivedy & Eberhard Mech. Mach. Theory 41:749---777, 2006) are then reviewed. It concludes by providing some possible issues for future research works.

Manipulator and object tracking for in-hand 3D object modeling

Abstract: Recognizing and manipulating objects is an important task for mobile robots performing useful services in everyday environments. While existing techniques for object recognition related to manipulation provide very good results even for noisy and incomplete data, they are typically trained using data generated in an offline process. As a result, they do not enable a robot to acquire new object models as it operates in an environment. In this paper we develop an approach to building 3D models of unknown objects based on a depth camera observing the robot's hand while moving an object. The approach integrates both shape and appearance information into an articulated Iterative Closest Point approach to track the robot's manipulator and the object. Objects are modeled by sets of surfels, which are small patches providing occlusion and appearance information. Experiments show that our approach provides very good 3D models even when the object is highly symmetric and lacks visual features and the manipulator motion is noisy. Autonomous object modeling represents a step toward improved semantic understanding, which will eventually enable robots to reason about their environments in terms of objects and their relations rather than through raw sensor data.