Pey Yuen Tao

Robust Adaptive Control of Cooperating Mobile Manipulators With Relative Motion

In this paper, coupled dynamics are presented for two cooperating mobile robotic manipulators manipulating an object with relative motion in the presence of system dynamics uncertainty and external disturbances. Centralized robust adaptive controls are introduced to guarantee the motion and force trajectories of the constrained object converge to the desired manifolds with prescribed performance. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The tracking of the constraint trajectory/force up to an ultimately bounded error is achieved. The proposed adaptive controls are robust against relative motion disturbances and parametric uncertainties and validated by simulation studies.

A geometrical approach for online error compensation of industrial manipulators

In this paper, a comprehensive online error compensation approach using offline calibration results is proposed for industrial manipulators (with closed control architecture), in order to improve its accuracy. The contents in this paper include a calibration algorithm based on the product-of-exponential formula, an online error compensation procedure for implementing the calibration results on industrial manipulators, and an experimental study that is conducted on a 6-DOF industrial manipulator from ABB (Model: IRB-4400) with a laser tracker system from Leica (Model: AT901-MR) and its Tracker-Machine control sensor. After implementing the proposed online error compensation approach, the accuracy of the ABB industrial manipulator improved to 0.3 mm, thus verifying the effectiveness of the proposed error compensation approach.

Autonomous stair climbing for mobile tracked robot

In this paper, we consider the problem of overcoming a flight of stairs in an uncertain environment with an autonomous tracked robot. A complete autonomous stair climbing module is developed which handles a number of tasks involved in stair climbing and a divide and conquer approach is adopted where the stair climbing challenge is further divided into several individual tasks such as stairs detection in an uncertain environment, intelligent climbing control to overcome the stairs, detection of the end of the stairs and the subsequent landing procedure to prevent damage to the onboard devices. A fuzzy control is developed and experimental results obtained have clearly illustrated the effectiveness of the proposed approach.

Product-of-exponential (POE) model for kinematic calibration of robots with joint compliance

In this paper, a robot calibration method is proposed where the effects of joint compliance is taken into account when formulating the model. The calibration process identifies the parameters describing the relative poses of the local frames attached to the links of the robot and the parameters determining joint deflections such as joint stiffness, the mass and center of gravity of each link. A kinematic model of the robot which considers the effects of joint compliance is first formulated using the product-of-exponentials approach. Subsequently, the calibration process is described where the parameters within the robot model are updated based on measurement data. Finally, a comprehensive simulation study is conducted to validate the assumptions made during problem formulation and to verify the effectiveness of the proposed approach.

A topological approach of path planning for autonomous robot navigation in dynamic environments

This paper proposes a novel approach, Simultaneous Path Planning and Topological Mapping (SP2ATM), to address the problem of path planning by registering the topology of the perceived dynamic environment as opposed to the conventional grid representation. The local topology is encoded, concurrent and incremental with path planning, by extracting only the admissible free space. The resulting Admissible Space Topological Map (ASTM) then serves as the minimum information to facilitate path planning in the 3D configuration space. Experimental results obtained from our mobile robot X1 in a complex planar environment, validates completeness and optimality of the algorithm.

A sensor-based approach for error compensation of industrial robotic workcells

Industrial robotic manipulators have excellent repeatability while accuracy is significantly poorer. Numerous error sources in the robotic workcell contributes to the accuracy problem. Modeling and identification of all the errors to achieve the required levels of accuracy may be difficult. To resolve the accuracy issues, a sensor based indirect error compensation approach is proposed in this paper where the errors are compensated online via measurements of the work object. The sensor captures a point cloud of the work object and with the CAD model of the work object, the actual relative pose of the sensor frame and work object frame can be established via a point cloud registration. Once this relationship has been established, the robot will be able to move the tool accurately relative to the work object frame near the point of compensation. A data pre-processing technique is proposed to reduce computation time and prevent a local minima solution during point cloud registration. A simulation study is presented to illustrate the effectiveness of the proposed solution.

A calibration framework for industrial robotic work cells

In this paper, a robot accuracy enhancement framework is proposed where localization and calibration techniques are seamlessly integrated and targeted at industrial robotic work cells. The framework focuses on capitalizing on the strengths of one process to compensate for the inherent weakness of the other. Specifically, localization approaches can improve the robot accuracy locally around the compensation pose to high levels via sensor feedback, while calibration improves the nominal robot kinematic model thereby extending the effects of localization to a larger working envelope. In addition, the proposed framework uses localization results as training data for the calibration process without incurring additional measurement costs. A simulation study is conducted to verify the effectiveness of the proposed framework.

Adaptive Neural Network Control for Marine Shafting System Using Dynamic Surface Control

In this paper, we consider the problem of tracking a desired propeller shaft speed while simultaneously minimizing torsional vibrations within the shafting system, in the presence of parametric/functional uncertainties. Neural networks are utilized to compensate for the functional uncertainties in the system model. Under the proposed control, semiglobal uniform boundedness of the closed loop signals is guaranteed, and the number and size of neural networks required are significantly reduced.

Calibration of industrial robots with product-of-exponential (POE) model and adaptive Neural Networks

Robot calibration is to improve the accuracy of the robot model so as to achieve better positioning accuracy within the robot work cell. Model based calibration approaches are in general limited to compensating for geometric errors and are unable to compensate for error sources that do not fit within the proposed robot model. In order to compensate for the unmodeled error sources, a Radial Basis Function (RBF) Neural Network (NN) augmented robot model is proposed together with a two stage calibration process for training the NN. A simulation and an experimental study are conducted to verify the effectiveness of the proposed solution.

A generalized underactuated robot system inversion method using Hamiltonian formalism

A new generalized method of stable model inversion is presented with the aim of providing solutions for the feedforward control of underactuated robots. The area of application is in SISO and MIMO systems within robotics which contain only scleronomous constraints. This generalized restriction is discussed followed by a justification of its sufficiency. The method uses a boundary value problem framework along with Hamiltonian formalism, representing the dynamic equations of motion, to solve for the stable model inversion of a robotic system. The benefits of the method include energy savings, enhanced safety, and robot simplification. An example of the robot feedforward control solution is presented to conclude the work.