Simultaneous localization and mapping systems based on rigid scene assumptions cannot achieve reliable positioning and mapping in a complex environment with many moving objects. To solve this problem, this paper proposes a novel dynamic multi-object lidar odometry (MLO) system based on semantic object recognition technology. The proposed system enables the reliable localization of robots and semantic objects and the generation of long-term static maps in complex dynamic scenes. For ego-motion estimation, the proposed system extracts environmental features that take into account both semantic and geometric consistency constraints. Then, the filtered features can be robust to the semantic movable and unknown dynamic objects. In addition, we propose a new least-squares estimator that uses geometric object points and semantic box planes to realize the multi-object tracking (SGF-MOT) task robustly and precisely. In the mapping module, we implement dynamic semantic object detection using the absolute trajectory tracking list. By using static semantic objects and environmental features, the system eliminates accumulated localization errors and produces a purely static map. Experiments on the public KITTI dataset show that the proposed MLO system provides more accurate and robust object tracking performance and better real-time localization accuracy in complex scenes compared to existing technologies.

The concept of distance between two rigid-body poses is important in path planning, positioning precision, mechanism synthesis, and in many other applications. In the definition of such a distance, two approaches mainly prevail, which lead to a number of formulas devised to match the needs of motion tasks. Despite the different approaches and formulas, some important theoretical results, which drive toward distance-metrics definitions useful for design and application purposes, have been stated. This paper summarizes the two different approaches together with a critical review of the literature on the distance metrics they generated, and, then, it illustrates a technique, previously proposed by the author, for combining different metrics to obtain novel distance-metric definitions that are tailored to specific applications.

Safe and socially compliant navigation in a crowded environment is essential for social robots. Numerous research efforts have shown the advantages of deep reinforcement learning techniques in training efficient policies, while most of them ignore fast-moving pedestrians in the crowd. In this paper, we present a novel design of safety measure, named Risk-Area, considering collision theory and motion characteristics of different robots and humans. The geometry of Risk-Area is formed based on the real-time relative positions and velocities of the agents in the environment. Our approach perceives risk in the environment and encourages the robot to take safe and socially compliant navigation behaviors. The proposed method is verified with three existing well-known deep reinforcement learning models in densely populated environments. Experiment results demonstrate that our approach combined with the reinforcement learning techniques can efficiently perceive risk in the environment and navigate the robot with high safety in the crowds with fast-moving pedestrians.

Due to the flexibility obtained through both materials and structures, soft robots have wide potential applications in complicated internal and external environments. This paper presents a new soft crawling robot with multiple locomotion patterns that integrate inchworm motion and various turning motions. First, the conceptual design of the proposed robot is presented by introducing thick-panel origami into the synthesis of a crawling robot, resulting in a Waterbomb-structure-inspired hybrid mechanism. Second, all locomotion patterns of the robot are precisely described and analyzed by screw theory in an algebraic manner, which include inchworm motion, restricted planar motion, quantitative turning motion, and marginal exploration motion. Then, the output motion parameter for each locomotion pattern is analytically modeled as a function of the robotic dimensional parameters, and the robot can thus be designed and controlled in a customized way for the expected output motion. Finally, the theoretical analysis and derivations are validated by simulation and physical prototype building, which lay the foundations for the design and manufacture of small-scale soft crawling robots with precise output motions in a complex planar environment.

Evolution has shown that legged locomotion is most adequate for tasks requiring versatile movement on land, allowing animals to traverse a wide variety of environments ranging from natural terrain to artificial, man-made landscapes with great ease. By employing well-designed control schemes, this ability could be replicated for legged robots, enabling them to be used in critical situations that still pose great danger to human integrity, such as search and rescue missions, inspection of hazardous areas, and even space exploration. This work characterizes the quadruped robot and contact dynamics that will compose our in-house simulator to be used for prototyping locomotion control schemes applied to quadruped robots. The proposed simulator computes the robot dynamics using the Recursive Newton-Euler and Composite-Rigid-Body algorithms with a few modifications to make certain aspects relevant for contact detection and control more easily accessible; furthermore, a compliant contact force method alongside stick-slip friction modeled the contact dynamics. To allow the robot to move, a simple PD-independent joint controller was implemented to track a desired leg trajectory. With the same robot and controller implemented using the MuJoCo simulation software, this work evaluates the proposed simulator by comparing characteristic locomotion signals such as the trunk pose and the ground reaction forces. Results showed similar behavior for both simulators, especially with regard to the contact detection, despite the significantly different contact models. Lastly, final remarks to enhance our simulator’s performance are suggested to be explored in future works.

The article introduces a novel class of 4R1H mechanisms, where 4R indicates four revolute joints and 1H indicates one helical joint. The paper starts with the type synthesis of these mechanisms, which involves combining two kinematic chains with planar and cylindrical motion types into a single closed-loop kinematic chain. If we fix any link in such a chain, we get a workable mechanism. The synthesis procedure considers two options for the relative arrangement of these two kinematic chains. Adding an H joint to the kinematic chain allows us to design mechanisms whose output link performs spatial motion. Using the proposed synthesis procedure, we develop a family of 4R1H mechanisms. Next, we choose one mechanism as a representative example and consider its mobility, singularity, kinematic, and dynamic analysis. Using screw theory, we confirm the mechanism has one degree of freedom and determine its singular configurations. Kinematic analysis provides closed-form expressions to calculate displacements, velocities, and accelerations of all the mechanism links. Dynamic analysis uses these results to compute the motor torque required for one motion cycle. To verify the suggested analytical algorithms and obtained results, we use computer-aided design tools, which allow us to develop virtual and physical prototypes.

This paper deals with generally routed, pre-bent cable-driven continuum robots (CCR). A CCR consists of a flexible backbone to which multiple disks are attached. Cables are passed through holes in the disk, and when pulled, the flexible backbone and the CCR can attain different shapes based on their routing and backbone configuration. An optimization-based approach, using minimization of strain energy, is shown to give good results for the pose and motion of the CCR and to determine contact with external objects. The pose, motion, and the contact obtained from the model are shown to match very well with experimental results obtained from a 3D-printed CCR. An algorithm is proposed to generate the pre-bent backbone for a CCR which on actuation can attain the desired shape. Using the algorithm, three 3D-printed CCRs with pre-bent backbones are fabricated and these are used to demonstrate a compliant gripper that can grip a spherical object similar to that done by tentacles, and another three-fingered gripper with straight backbone CCRs is used to orient a square object gripped at the end.

This paper presents a decentralized, cooperative, real-time avoidance control strategy for robotic manipulators. The proposed avoidance control law builds on the concepts of artificial potential field functions and provides tighter bounds on the minimum safe distance when compared to traditional potential-based controllers. Moreover, the proposed avoidance control law is given in analytical, continuous closed form, avoiding the use of optimization techniques and discrete algorithms, and is rigorously proven to guarantee collision avoidance at all times. Examples of planar and 3D manipulators with cylindrical links under the proposed avoidance control are given and compared with the traditional approach of modeling links and obstacles with multiple spheres. The results show that the proposed avoidance control law can achieve, in general, faster convergence, smaller tracking errors, and lower control torques than the traditional approach. Furthermore, we provide extensions of the avoidance control to robotic manipulators with bounded control torques.

In recent years, many microrobots have been developed for search applications using swarms in places where humans cannot enter, such as disaster sites. Hexapod robots are suitable for moving over uneven terrain. In order to use micro-hexapod robots for swarm exploration, it is necessary to reduce the robot’s size while maintaining its rigidity. Herein, we propose a micro-hexapod with an SU-8 rigid frame that can be assembled from a single sheet. By applying the SU-8 coating as a structure to the hexapod and increasing the rigidity, the substrate size can be reduced to within 40 mm × 40 mm and the total length when assembled to approximately 30 mm. This enables the integration of the micro electromechanical systems (MEMS) process into small and inexpensive hexapod robots. In this study, we assembled the hexapod with a rigid frame from a sheet created using the MEMS process and evaluated the leg motion.

This paper proposes a virtual reality-based dual-mode teleoperation architecture to assist human operators in remotely operating robotic manipulation systems in a safe and flexible way. The architecture, implemented via a finite state machine, enables the operator to switch between two operational modes: the Approach mode, where the operator indirectly controls the robotic system by specifying its target configuration via the immersive virtual reality (VR) interface, and the Telemanip mode, where the operator directly controls the robot end-effector motion via input devices. The two independent control modes have been tested along the task of reaching a glass on a table by a sample population of 18 participants. Two working groups have been considered to distinguish users with previous experience with VR technologies from the novices. The results of the user study presented in this work show the potential of the proposed architecture in terms of usability, both physical and mental workload, and user satisfaction. Finally, a statistical analysis showed no significant differences along these three metrics between the two considered groups demonstrating ease of use of the proposed architecture by both people with and with no previous experience in VR.

The recognizing underwater targets is a crucial component of autonomous underwater vehicle patrols and detection efforts. In the process of visual image recognition in real underwater environment, the spatial and semantic features of the target often appear to different degrees of loss, and the scarcity of specific types of underwater samples leads to unbalanced data on categories. This kind of problem makes the target features appear weak and seriously affects the accuracy of underwater target recognition. Traditional deep learning methods based on data and feature enhancement cannot achieve ideal recognition effect. Based on the above difficulties, this paper proposes an improved feature enhancement network for weak feature target recognition. Firstly, a multi-scale spatial and semantic feature enhancement module is constructed to extract the feature information of the extraction target accurately. Secondly, this paper solves the influence of target feature distortion on classification through multi-scale feature comparison of positive and negative samples. Finally, the Rank & Sort Loss function was used to train the depth target detection to solve the problem of recognition accuracy under highly unbalanced sample data. Experimental results show that the recognition accuracy of the proposed method is 2.28% and 3.84% higher than that of the existing algorithms in the recognition of underwater fuzzy and distorted target images, which demonstrates the effectiveness and superiority of the proposed method.

Precise and efficient performance in remote robotic teleoperation relies on intuitive interaction. This requires both accurate control actions and complete perception (vision, haptic, and other sensory feedback) of the remote environment. Especially in immersive remote teleoperation, the complete perception of remote environments in 3D allows operators to gain improved situational awareness. Color and Depth (RGB-D) cameras capture remote environments as dense 3D point clouds for real-time visualization. However, providing enough situational awareness needs fast, high-quality data transmission from acquisition to virtual reality rendering. Unfortunately, dense point-cloud data can suffer from network delays and limits, impacting the teleoperator’s situational awareness. Understanding how the human eye works can help mitigate these challenges. This paper introduces a solution by implementing foveation, mimicking the human eye’s focus by smartly sampling and rendering dense point clouds for an intuitive remote teleoperation interface. This provides high resolution in the user’s central field, which gradually reduces toward the edges. However, this systematic visualization approach in the peripheral vision may benefit or risk losing information and burdening the user’s cognitive load. This work investigates these advantages and drawbacks through an experimental study and describes the overall system, with its software, hardware, and communication framework. This will show significant enhancements in both latency and throughput, surpassing 60% and 40% improvements in both aspects when compared with state-of-the-art research works. A user study reveals that the framework has minimal impact on the user’s visual quality of experience while helping to reduce the error rate significantly. Further, a 50% reduction in task execution time highlights the benefits of the proposed framework in immersive remote telerobotics applications.

Hyper-redundant manipulators are produced by cascading several mechanisms on top of each other as modules. The discrete actuation makes their control easier because discrete actuators usually do not need any feedback to control. So far, several methods have been proposed to solve the inverse kinematic problem of discretely actuated, hyper-redundant manipulators. The two-by-two searching method is better than the other methods in terms of CPU time and error. In this article, the mentioned method is generalized by choosing an arbitrary number of modules as pending modules in each step of the solution instead of the necessary two. For validation, the proposed method is compared with nine meta-heuristic searching algorithms: simulated annealing, genetic algorithm, particle swarm optimization, ant colony optimization, gray wolf optimizer, stochastic fractal search, whale optimization algorithm, Giza pyramid construction, and flying fox optimization. Furthermore, the effect of the number of pending modules on CPU time and error is investigated. All the numerical problems have been solved for two case studies, one is planar and the other is spatial.

Currently, there is an increasing demand for parallel mechanisms with fewer inputs and more outputs in aerospace, antennas, and recreational facilities. Therefore, this paper proposes a new parallel mechanism (PM) with a separation of constraints and drives, facilitating centralized drive management. Based on screw theory, the type synthesis of new parallel mechanisms with 3T (T representing translation), 3R (R representing revolute), 2T1R, and 2R1T is proposed. Three-degree-of-freedom (3-DOF) parallel mechanisms with fewer inputs and multiple outputs are proposed. Taking the 3RPS-PPPS parallel mechanism as an example, the closed-loop vector method is employed to solve for the lengths of actuated branches. A complete Jacobi matrix is constructed. The motion/force transmission performance of the 3RRS/PPPS parallel mechanism is better than the 3RRS parallel mechanism. Centralized management of drive branches greatly reduces the number of drives, making maintenance and repair easier. It has many application scenarios in antennas, recreational facilities, and other occasions.

In order to improve the global convergence performance of the super-twisting sliding mode control (STSMC) for the uncertain hybrid mechanism, especially in the high-speed scenario, and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes, an intelligent fixed-time super-twisting sliding mode control (IFTSTSMC) is proposed. Firstly, a fixed-time super-twisting sliding mode control (FTSTSMC) algorithm is designed by adding the exponential power terms with the fixed-time performance parameters in sliding variables and the transcendental function of the super-twisting algorithm in order to enhance the global convergence performance of the STSMC. Secondly, the existence condition of FTSTSMC for the uncertain hybrid mechanism is analyzed. The IFTSTSMC is designed by introducing RBF neural network to break through the limited range of uncertainties in FTSTSMC and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes. Then, the Lyapunov stability of the proposed method and the global fixed-time convergence of the system are proved theoretically. Finally, the effectiveness and superiority of the proposed control method are verified by the simulation and the automobile electro-coating conveying prototype experiment comparing with two classical finite-time sliding mode control methods.

Thanks to its real-time computation efficiency, deep reinforcement learning (DRL) has been widely applied in motion planning for mobile robots. In DRL-based methods, a DRL model computes an action for a robot based on the states of its surrounding obstacles, including other robots that may communicate with it. These methods always assume that the environment is attack-free and the obtained obstacles’ states are reliable. However, in the real world, a robot may suffer from obstacle localization attacks (OLAs), such as sensor attacks, communication attacks, and remote-control attacks, which cause the robot to retrieve inaccurate positions of the surrounding obstacles. In this paper, we propose a robust motion planning method ObsGAN-DRL, integrating a generative adversarial network (GAN) into DRL models to mitigate OLAs in the environment. First, ObsGAN-DRL learns a generator based on the GAN model to compute the approximation of obstacles’ accurate positions in benign and attack scenarios. Therefore, no detectors are required for ObsGAN-DRL. Second, by using the approximation positions of the surrounding obstacles, ObsGAN-DRL can leverage the state-of-the-art DRL methods to compute collision-free motion commands (e.g., velocity) efficiently. Comprehensive experiments show that ObsGAN-DRL can mitigate OLAs effectively and guarantee safety. We also demonstrate the generalization of ObsGAN-DRL.

This paper introduces a sophisticated trajectory capture and playback mechanism for collaborative robots, aimed at enhancing accuracy and operational efficiency through several innovative techniques. The Ju-Gibbs attitude quaternion is utilized for enhanced kinematic modeling across multi-axis systems, which simplifies variables, reduces dimensions, and enhances symbolic clarity, thus surpassing the limitations of traditional rotation vectors and unit quaternions. A new sliding filter is developed to effectively reduce noise and optimize trajectory details more efficiently. Additionally, an automated mechanism is implemented for adjusting the sampling rate and removing static data points at the trajectory’s start and end, further refining data collection accuracy. These advancements have been successfully replicated on the Kuka robot LBR iiwa 7 R800, demonstrating the practical applicability of the solutions in real-world settings.

The early applications of Visual Simultaneous Localization and Mapping (VSLAM) technology were primarily focused on static environments, relying on the static nature of the environment for map construction and localization. However, in practical applications, we often encounter various dynamic environments, such as city streets, where moving objects are present. These dynamic objects can make it challenging for robots to accurately understand their own position. This paper proposes a real-time localization and mapping method tailored for dynamic environments to effectively deal with the interference of moving objects in such settings. Firstly, depth images are clustered, and they are subdivided into sub-point clouds to obtain clearer local information. Secondly, when processing regular frames, we fully exploit the structural invariance of static sub-point clouds and their relative relationships. Among these, the concept of the sub-point cloud is introduced as novel idea in this paper. By utilizing the results computed based on sub-poses, we can effectively quantify the disparities between regular frames and reference frames. This enables us to accurately detect dynamic areas within the regular frames. Furthermore, by refining the dynamic areas of keyframes using historical observation data, the robustness of the system is further enhanced. We conducted comprehensive experimental evaluations on challenging dynamic sequences from the TUM dataset and compared our approach with state-of-the-art dynamic VSLAM systems. The experimental results demonstrate that our method significantly enhances the accuracy and robustness of pose estimation. Additionally, we validated the effectiveness of the system in dynamic environments through real-world scenario tests.