Narrow Results

In the era of Industry 4.0, 3D printing has shown significant outcomes. To address the challenges of large-format complex material printing and forming, such as spatial constraints and excessive support structures in traditional 3D printing, the integration of industrial robots with 3D printing technology is proposed. However, robotic 3D printing introduces challenges in path planning and real-time optimization. This paper presents a methodology for path planning and real-time optimization of robotic arms on a 3D printing platform. The approach involves adjusting the printing path by modifying the nozzle printing posture and implementing obstacle avoidance algorithms. The study uses geometric and algebraic methods to optimize the robotic arm trajectory to improve the precision of reaching print points, reduce the printing cycle, and minimize material wastage. To verify the feasibility of this method, a case study in 3D printing is conducted to examine the practical application of motion planning for robotic arms based on digital twin technology.

Robotic grasping techniques for regular targets with known shapes are now well established. However, unknown shaped objects have complex features such as texture, shape, and appearance, which leads to inaccurate recognition and localization of shaped objects during grasp detection. To improve the generalization ability of the grasping detection network for unfamiliar shaped objects, we propose a lightweight shaped object grasping detection network (LSOGD) based on feature fusion, which solves the problem that the network repeatedly extracts features from images and ultimately improves the accuracy of model detection by combining different features. The effectiveness of LSOGD is confirmed by performance evaluation on the Cornell dataset and Jacquard dataset, where the detection accuracy reaches 97.9% and 96.7% for unknown objects, respectively. In addition, due to the small proportion of shaped objects in the current publicly available dataset, we added a portion of industrial-shaped pieces based on the selection of some shaped objects in the Cornell grasping dataset to build a shaped object dataset named X-Cornell on which the accuracy of our proposed model for grasping and detecting the unknown shaped objects is 94.6%. Finally, an actual robot grasping experiment was conducted using a Realsense d435i camera and a Kinova robotic arm, and the success rate of grasping shaped objects was 94%.

Robots, A Potential Staple in Eye Surgery.

Interviews at Commonwealth Science Conference 2017.

Precision Medicine for Cancer Patients: Interview with Dr Allen Lai.

Three kinds of collision reaction strategies for increasing safety during human and robot interactions without relying on torque sensors are proposed in this paper. In the proposed algorithms, motor torque is estimated by driver current. The generalized momentum observer is used for collision detection, which does not need joints acceleration information and calculates the inverse of the inertia matrix. Three different collision reaction strategies, going away, dragging by hands and mechanical impedance developed in this paper, aim to enhance safety to humans during physical interaction with robots. For verifying the efficiency of the proposed algorithms, experiments are tested between a 1-DOF manipulator system and a human being. At last, the experiments’ results show that the proposed collision reaction algorithms are effective.

The aim of this paper is trying to propose an efficient method of inverse kinematics and motion generation for redundant humanoid robot arm based on the intrinsic principles of human arm motion. The intrinsic principle analysis takes into account both the skeletal kinematics and muscle strength properties. Firstly, this work analyzed the kinematic redundancy problem of a human arm. By analyzing the biological feature of a human arm, the kinematic redundancy boils down to the uncertainty of elbow position. Secondly, because the muscle’s kinematic and strength properties are critical for simulating biometric motion authentically, the muscle strength property was introduced as the criterion for configuration identification and motion generation. Three types of limb configuration, dog walking, gecko climbing, and human walking limb configuration were analyzed, and two geometrical configuration identification rules were deduced to generate biomimetic motion for humanoid robotic arms. By comparing the proposed method with other five IK methods, the proposed method significantly deduced the computing time. Finally, the configuration identification rules were used to generate motions for a 7-DoF humanoid robotic arm. The results showed that the biological rules can generate biomimetic, smooth arm motions for a redundant humanoid robotic arm.

In this paper, we investigate the configuration space ${{𝒮}}_{G,b,\ell }$ associated with the movement of a robotic arm of length $\ell$ on a grid over an underlying graph $G$, anchored at a vertex $b\in G$. We study an associated poset with inconsistent pairs (PIP) ${IP}_{G,b,\ell }$ consisting of indexed paths on $G$. This PIP acts as a combinatorial model for the robotic arm, and we use ${\mathrm{\text{IP}}}_{G,b,\ell }$ to show that the space ${{𝒮}}_{G,b,\ell }$ is a CAT(0) cubical complex, generalizing work of Ardila, Bastidas, Ceballos, and Guo. This establishes that geodesics exist within the configuration space, and yields explicit algorithms for moving the robotic arm between different configurations in an optimal fashion. We also give a tight bound on the diameter of the robotic arm transition graph — the maximal number of moves necessary to change from one configuration to another — and compute this diameter for a large family of underlying graphs $G$.

To reduce the complexity of monitoring and management of robots in service, a six-axis robot control system based on digital twin is proposed. Based on 3D printing technology, a six-axis robot is developed. At the same time, the kinematics of the robot is analyzed, and its kinematics model is built using the D-H rule. The forward and reverse kinematics of the robot are solved. Through the two-way data interaction between the model layer and the entity layer, the simulation operation of the robot and the twin synchronous operation of the virtual real robot are realized. Based on the real-time data drive, the key parameters of the robot are monitored, and the health parameter table of the current, voltage, joint vibration, and other parameters of the robot system is established. Based on the idea of comparison of the same kind, the abnormal state detection of the robot is realized through quantitative analysis. Finally, the feasibility of the proposed system is verified by experiments.

A robotic arm is a mechanical device with a given number of Degrees of Freedom (DoFs) that mimics the functions of a human arm and performs any desired task, such as grasping and moving objects. Current research is directed toward the design of robots and artificial human body parts controlled by brain signals, translating human thoughts into actions. Brain-Computer Interface (BCI) systems have been used to enable people with motor disabilities to control assistive robotic equipment that replaces the lost functions. This paper presents a review of the state-of-the-art of the latest papers dealing with the control of a robotic arm based on Electroencephalogram (EEG). A comparative study of the different methods and techniques used in different blocks of the robotic arm’s noninvasive BCI controlling system is conducted. These blocks include signal acquisition using noninvasive electrodes, signal preprocessing, feature extraction, classification, and command. Additionally, this paper presents a performance comparison of the reviewed controlling systems of robotic arms using EEG signals.

To reduce the complexity of monitoring and management of robots in service, a six-axis robot control system based on digital twin is proposed. Based on 3D printing technology, a six-axis robot is developed. At the same time, the kinematics of the robot is analyzed, and its kinematics model is built using the D-H rule. The forward and reverse kinematics of the robot are solved. Through the two-way data interaction between the model layer and the entity layer, the simulation operation of the robot and the twin synchronous operation of the virtual real robot are realized. Based on the real-time data drive, the key parameters of the robot are monitored, and the health parameter table of the current, voltage, joint vibration, and other parameters of the robot system is established. Based on the idea of comparison of the same kind, the abnormal state detection of the robot is realized through quantitative analysis. Finally, the feasibility of the proposed system is verified by experiments.