Chee Meng Chew

A walking pattern generator for biped robots on uneven terrains

We present a new method to generate biped walking patterns for biped robots on uneven terrains. Our formulation uses a universal stability criterion that checks whether the resultant of the gravity wrench and the inertia wrench of a robot lies in the convex cone of the wrenches resulting from contacts between the robot and the environment. We present an algorithm to compute the feasible acceleration of the robots CoM (center of mass) and use that algorithm to generate biped walking patterns. Our approach is more general and applicable to uneven terrains as compared with prior methods based on the ZMP (zero-moment point) criterion. We highlight its applications on some benchmarks.

Standing posture modeling and control for a humanoid robot

This paper presents a novel approach employing nonlinear control for stabilization of standing posture for a humanoid robot using only hip joint. The robot is modeled as an acrobot where model parameters are estimated through adaptive algorithm. A `non-collocated partial feedback controller is applied. This is integrated with a linear feedback control, through LQR. Improved robustness to external push is demonstrated through evaluation in Webots simulator and on a physical humanoid robot, NUSBIP-III ASLAN. Performance comparison with other controllers verifies the effectiveness of the proposed control system.

Application of deep neural network in estimation of the weld bead parameters

We present a deep learning approach to estimation of the bead parameters in welding tasks. Our model is based on a four-hidden-layer neural network architecture. More specifically, the first three hidden layers of this architecture utilize Sigmoid function to produce their respective intermediate outputs. On the other hand, the last hidden layer uses a linear transformation to generate the final output of this architecture. This transforms our deep network architecture from a classifier to a non-linear regression model. We compare the performance of our deep network with a selected number of results in the literature to show a considerable improvement in reducing the errors in estimation of these values. Furthermore, we show its scalability on estimating the weld bead parameters with same level of accuracy on combination of datasets that pertain to different welding techniques. This is a nontrivial result that is counter-intuitive to the general belief in this field of research.

Collision-free path planning for multi-pass robotic welding

Welding joints for offshore oil rigs present a complicated geometry and require multiple passes. This paper introduces a complete collision-free offline path planning approach for such joints. Collision detection is performed using A* search on a three dimensional grid, where triangular mesh representations of the welding joint and its fixture form the objects. A workflow is proposed for the complete planning process which involves two primary steps; multi-pass planning and intermediate path planning. The paper demonstrates results on a `brace-to-chord joint, which illustrates the feasibility of the proposed approach.

Object detection and motion planning for automated welding of tubular joints

Automatic welding of tubular TKY joints is an important and challenging task for the marine and offshore industry. In this paper, a framework for tubular joint detection and motion planning is proposed. The pose of the real tubular joint is detected using RGB-D sensors, which is used to obtain a real-to-virtual mapping for positioning the workpiece in a virtual environment. For motion planning, a Bi-directional Transition-based Rapidly exploring Random Tree (BiTRRT) algorithm is used to generate trajectories for reaching the desired goals. The complete framework is verified with experiments, and the results show that the robot welding torch is able to transit without collision to desired goals which are close to the tubular joint.

Automated bead layout methodology for robotic multi-pass welding

An automated bead layout methodology is proposed for multi-pass welding on varying seam angle. This methodology will replace the tedious process of `teaching and playback in the current line of robotic welding. To develop the proposed method, manual flux cored arc welding has been conducted on several workpieces. It is then ascertained that the bead size varies from 25 to 30 mm2 in a more ideal welding zone. Therefore, this leads to the assumption of a constant bead size for automated welding. Based on the results from this experiment, the bead layout and welding parameters for new workpiece with different seam angles can be determined. The simulation result show that a uniform bead layout is achieved.

Design of feedforward filling control for joining thick materials using robotic welding systems

In this paper, a filling control strategy is proposed for robotic welding of thick materials which requires multipass welding. The multi-pass welding process is formulated as a closed-loop control design problem. A PI controller is used in the baseline loop for regulating the seam boundary error in the current pass; a non-causal feedforward controller is designed using the H∞ loop shaping technique for regulating the error from previous welding passes. Simulation results show that as compared to without using a feedforward controller, error propagation on the seam boundary will be eliminated within six passes for disturbances occurring only in the first filling pass, and error amplification is contained within four passes for disturbances occurring at the same position in every filling pass.

3D reconstruction of complex weld geometry based on adaptive sampling

We present a low-cost social robot system composed of a mobile base (a robotic cleaner,

Identification and reconstruction of complex weld geometry based on modified entropy

150), an Intel RealSense RGB-D camera (

Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding

100), a touch screen powerful laptop (