Despite the giant leap made in object 6D pose estimation and robotic grasping under structured scenarios, most approaches depend heavily on the exact CAD models of target objects beforehand, thereby limiting their wide applications. To address this, we propose a novel knowledge-guided network - KGNet to estimate the pose and size of category-level unseen objects. This network includes three primary innovations: knowledge-guided categorical model generation, pointwise deformation probability matrix and synergetic RGBD feature fusion, with the former two leveraging categorical object knowledge for unseen object reconstruction and the latter one facilitating pose-sensitive feature extraction. Exten-sive experiments on CAMERA25 and REAL275 verify their effectiveness, and KGNet achieves the SOTA performance on these two acknowledged benchmarks. Additionally, a real-world robotic grasping experiment is conducted, and its results further qualitatively prove the practicability and robustness of KGNet.

In this paper, we present a framework to generate time-optimal trajectories for dynamic target pick-and-place tasks. We develop an optimization-based trajectory generation method for manipulators, which can conduct spatial-temporal deformation under user-defined requirements. We formulate the problem of dynamic target pick-and-place, in which the trajectory duration and jerk are optimized and terminal states are adjusted instead of being fixed. The motions are constrained within the mechanical limits and to avoid collisions. Constraints transcription is adopted to convert constraints to weighted penalties. Then the problem can be solved based on the trajectory generation method with a high-level optimizer. We integrate the proposed method with online perception into a robot arm platform, in which a conveyor belt is used to transport the objects. Simulations and real-world experiments are conducted under a range of object speeds. Results show that the proposed method achieves online grasping under the object velocity up to 0.5m/s with an average computing time of 190ms.

Four-wheel independently driven mobile robots are widely used in industrial automation, intelligent inspection, and outdoor exploration. The traditional kinematic control is usually applied for them, where only the chassis kinematics is taken into account and the robot dynamics (especially the wheel dynamics) is normally ignored. It may lead to some performance limitations such as the chattering phenomenon during robot rotating, because of the overactuation characteristic by four driving wheels. To address these problems, the integrated dynamic model is proposed, which includes chassis kinematics, chassis dynamics, wheel-ground interaction, and wheel dynamics. Subsequently, different from kinematic control, a model-based coordinated adaptive robust controller is developed, which generally consists of three-level designs for different parts of robot dynamics, and directly generates the motor driving torque commands for four wheels. The stability and tracking performance are theoretically guaranteed. Comparative experiments are carried out, and the results show the better performance of our proposed scheme.