Generating compact polygonal models from point clouds is a key problem in 3D vision and computer graphics. However, due to inherent limitations of LiDAR scanning (e.g. range constraints and occlusions), critical scene information is often missing, leading to degraded reconstruction accuracy. To address this, we propose a plane assembling strategy that effectively recovers missing details while maintaining model compactness. We classify all the planes extracted from the scene into three categories: highly visible, barely visible, and invisible. The invisible planes, which are recovered by scene structure analysis, indicate the missing details. The three types of planes correspond to the three growth priorities. Each plane grows according to the priority level, and the space is partitioned progressively, that is, the hierarchical partition. Subsequently, we generate a watertight polygonal mesh from the partition via a min-cut-based optimization. Finally, comparisons on public datasets show the effectiveness and superiority of our method against mainstream approaches.

Circular electrical connectors (CECs) have a wide range of applications in scenarios that require reliable connections. However, sockets are often located in narrow scenes with random spatial orientations, complex lighting conditions, and obstructions from cables, making it difficult to accurately locate them through cameras. Besides, due to the complex geometric structure of CECs and the presence of electrode protection slots, the existing research on the assembly of cylindrical or polygonal pegs and holes may not be applicable to the assembly of such components. To this end, this article proposes a novel robotic assembly strategy for CECs with small relative initial deviations, whose core is to design a search trajectory and heuristic force strategy to perceive force/pose (F/P) discontinuity characteristics under different geometric constraints. This assembly strategy is independent of the CEC's size and is not affected by the socket's spatial orientation. The experiments with two different sizes of CECs on a robot equipped with a 6-dimensional force/torque ($\mathbf{F} / \mathbf{T}$) sensor are conducted, and the effectiveness and robustness of the proposed assembly strategy for CECs are demonstrated.

Dual-arm robots, with their high flexibility and broad operational range, are widely used in industrial and household transportation tasks. However, their high degrees of motion freedom and the closed-chain constraints formed during transportation pose challenges for path planning and force control. This study proposes a dual-agent control framework based on the Soft-Actor-Critic (SAC) algorithm of Deep Reinforcement Learning (DRL), where one agent is responsible for path planning and the other handles force control. This framework enables dual-arm robots to achieve dynamic obstacle avoidance, avoid unsolvable configurations and singularities, and generate efficient and smooth paths, while also fulfilling internal force tracking requirements based on task demands. Additionally, it addresses the gap in transferring the force control agent from simulation to real-world applications through a state mapping network, and the force control agent does not require retraining for different objects. Finally, the effectiveness of the proposed framework is validated through two scenes, including multiple bookshelves stacking and dynamic obstacle avoidance during the box transportation. Validation was also carried out with objects of different geometries and weights.

The classical image-based visual servoing (IBVS) methods exhibit strong robustness to robot modeling and camera calibration errors, but suffer from uncontrollable spatial trajectories and local convergence issues. Although current IBVS variants employ specialized visual features or information to improve trajectory controllability, these approaches impose restrictive geometric constraints, such as requiring coplanar feature points or configurations approximately orthogonal to the optical axis of the camera. Furthermore, despite conclusive evidence of local minima (LM) in the IBVS scheme, there is currently no solution to address this challenge. For the reason, this article first introduces a novel virtual image construction method and proposes a decoupling visual servoing control law based on virtual image, achieving explicit separation of translational and rotational error dynamics. Then, through Monte Carlo simulations, the spatial distribution patterns of local minima in IBVS are systematically studied, and an escape algorithm leveraging virtual image is further designed. To the best of our knowledge, this may be the first attempt to address the issue of local minima only through IBVS to a certain extent. Comparative simulations and experiments validate the effectiveness and superiority of the proposed decoupling control law based on virtual image, and the success rate of the local minima escape algorithm is 100% under different configurations.

Electrical connectors (ECs) are extensively employed in industrial scenarios, and their assembly quality is crucial. However, these connectors are often located in confined spaces, which poses challenges of complex lighting conditions and visual occlusions during the execution of robotic assembly tasks. Hence, guiding robots to assemble solely through force/torque (F/T) feedback is an alternative way. However, there is currently limited research on achieving assembly tasks solely through F/T information, especially when the spatial pose of the socket is uncertain, and how to achieve the robotic assembly of ECs remains a difficult problem. To this end, this paper proposes a novel strategy for assembling ECs under large initial pose deviations. Specifically, inspired by observing the assembly process of humans without visual assistance, the robotic assembly is first divided into two stages: by arbitrary surface tracking, the relative pose of the current EC is confirmed and adjusted to a dual-point contact state, i.e. the conversion from non-contact to dual-point contact; by setting heuristic F/T, the alignment of the edge, plane and slots of EC is driven, i.e. the alignment of the plug and socket. Next, we analyse the geometric constraint states at these stages and formulate the corresponding desired contact F/T strategies. To our knowledge, this may be the first attempt to achieve the robotic assembly of ECs under large initial deviations solely using F/T information. Finally, experiments on a UR5 robot indicate that the proposed strategy exhibits robustness to large initial pose deviations and can overcome obstacles caused by friction and jamming to ensure the robotic assembly of ECs. Note to Practitioners—The automatic assembly of ECs in confined spaces with visual occlusions remains an unsolved challenge, especially in aerospace scenarios where manual assembly may not be appropriate. The proposed assembly strategy is based on F/T perception without visual assistance, which can effectively address the perception and assembly processes of plugs under large initial pose deviations, demonstrating robust performance. With the requirement of only a F/T sensor and no need for precise dimensions of the assembly object, this strategy exhibits favourable deployment characteristics for automatic assembly platforms. Furthermore, the proposed strategy can be optimized by the integration of learning-based methods into the perception process, thereby further improving assembly efficiency.

Industrial robots (IRs) have considerable energy-saving potential due to their vast application scale and wide range of applications. Although substantial work on the energy consumption (EC) optimization of IRs has emerged, most optimization approaches require prior knowledge of the IRs' dynamic characteristics and the electro-mechanical parameters of their drive systems, which are typically not provided by IR manufacturers. Therefore, this article proposes an EC modeling and optimization method based on the time-scaling technique and custom identification experimental data without joint torque information. Specifically, this article develops an energy characteristic parameter submodel (ECPSM) to formulate the EC resulting from configuration transitions. In addition, theoretical proof demonstrates that all coefficients in the proposed ECPSM can be identified based on the data of a finite number of identification experiments. Building upon the proposed EC model, a bidirectional dynamic programming (BDP) algorithm optimizes the IR's trajectory for energy-saving, while utilizing parallel processing significantly reduces the time required for the optimization process. Experimental results on the KUKA KR60-3 demonstrate that the proposed method achieves an average relative error of 1.59% for predicting the EC of linear scaling trajectories and 6.19% for nonlinear scaled trajectories. Moreover, the BDP-based optimization method dramatically reduces the computational time required to obtain the optimal scaling trajectory and its EC.

Due to the robustness to robot modeling and camera calibration errors and avoidance of complete target geometry, image-based visual servoing has always been an important topic in the fields such as robotics, computer vision and so forth. When the image information obtained by the camera is mapped to the robotic task space to design the servoing control law, the resulting interaction matrix, which links the spatial velocity of the camera to the temporal variation of the selected image features, depends on the unknown feature depths. The use of inaccurate feature depths may influence the stability and robustness of the controller, and even cause the failure of the task. In this article, based on the perspective camera model, by employing the principle of reduced order observer, a novel logarithmic observer is presented for on-line recovery of feature depth. Compared with the typical observers now available, the presented observer offers several advantages: global convergence, a faster convergence rate of error structure than exponential error structure, a less restricted observability condition and greater robustness against measurements with noise. The comparison results of numerical simulations indicate the superiority of the presented observer, and real experiments with Kinect v2 sensor further validate the effectiveness of the presented observer in practical situation. Note to Practitioners—This article was motivated by the depth problem in the image-based visual servoing scheme, but it can also be used in other situations where the image depth information is needed, such as 3D reconstruction, robot navigation, etc. The existing depth acquisition methods include TOF sensors, stereo vision, depth observers and so on. However, TOF sensors are sensitive to light conditions, and the mounting space of stereo vision is slightly large, and there is contradiction between observation performance and computational complexity in most existing observers. In this article, a novel structure of logarithmic reduced order observer is described in detail, which can be utilized to estimate the depth information of images easily. The simulations and experiments verify the good performance of the observer. The limitations of the given observer are that the estimation accuracy is not very good under weak excitation, and the camera needs to be calibrated in advance. Future work will focus on overcoming these two limitations.

To solve the problems such as poor anti-interference performance and more steady-state current harmonic components of a permanent magnet synchronous motor(PMSM) drive system based on traditional PI control, this paper presents a model predictive control method based on sliding-mode-controller (SMC-MPC). Firstly, a sliding mode speed controller based on exponential reaching law is designed to replace the traditional PI speed controller, and to reduce the influence of the load disturbance on system performance. Secondly, the model predictive current control is adopted based on the discrete model of PMSM to reduce current harmonics and improve the torque control accuracy. Then the cost function is designed by controlling the d- and q-axis current, and the optimal output voltage vector is obtained to drive the motor. Finally, compared with the traditional PI method, the simulation results show that the proposed method not only improves the robustness of the system to load disturbance effectively, but also decreases the ripples of current and torque. The presented method has good dynamic and static performance for PMSM drive system.

Without the 3-D geometry of the target and robust to camera calibration error, image-based visual servoing schemes have gained a lot of attention. However, the depth of the selected feature, which is involved in the interaction matrix relating the time variation of the feature to the velocity twist of the camera, must be estimated correctly to guarantee the stability of the controller. To this end, this paper proposes a new nonlinear reduced-order observer structure to recover the feature depth in real time. Compared with the existing works, the proposed observer has a global asymptotic convergence property and fast convergence rate, and the convergence rate can be easily adjusted only using a single gain parameter. In addition, the proposed observer has a less restrictive observability condition and stronger robustness to noisy measurements. Extensive comparative numerical simulations are carried out to validate the effectiveness of the proposed depth observer.

Particle systems' greatest advantage is well suited for modeling complex fuzzy phenomena, such as explosions, fountain, tornado and fireworks, etc. in 3D graphics. With the increasing requirements on the number of particles and particle-particle interactions, the computational complexity of simulation in particle systems has increased rapidly. Particle systems are traditionally implemented on a general-purpose CPU, and the computational complexity of particle systems limits the number of particles that can be computed at interactive rates. This paper focuses on real-time simulation of large-scale particle systems. We discuss optional integration algorithms based on CUDA (Compute Unified Device Architecture) for both graphic and scientific simulation. The speed of particle systems has been greatly improved, with parallel-core GPUs working in tandem with multi-core CPUs. In order to provide a scalable and portable API library, the object-oriented programming method is adopted to encapsulate the functions of parallel particle system. Results show that our proposed APIs are user-friendly and the parallel implementations are significantly efficient.