Enabling quadruped robots to navigate complex, unstructured 3-D environments using onboard sensors remains a significant challenge, particularly in the absence of maps. This difficulty stems from coupling long-horizon decision-making with terrain-aware control under partial observability and limited onboard perception. To address this, we propose a depth-based distillation learning framework featuring a hierarchical architecture that decouples high-level navigation from low-level locomotion. The navigation policy predicts velocity commands from raw depth and goal observations, while the locomotion controller executes motor actions based on proprioception and compact depth features distilled from privileged geometric scans of terrain. To improve representation quality and training efficiency, we incorporate contrastive forward prediction and inverse dynamics modeling into the reinforcement learning loop. A GPU-parallel Warp-based depth renderer is integrated into Isaac Gym to accelerate visual simulation and support large-scale training. To ensure robustness in cluttered environments, we employ an adaptive curriculum learning strategy that progressively increases terrain complexity during training. Our system enables robust, map-free navigation across both structured and unstructured terrain, and demonstrates successful zero-shot deployment on a real quadruped robot.

With respect to the accurate tracking control for unmanned surface vehicles (USVs), this paper proposes a novel hierarchical control structure consisting of a velocity planner and a tracking controller. With full consideration of control accuracy and tracking safety, a model predictive control planning method based on dynamic artificial potential field method (DAPF-MPC) is proposed, aiming to generate the current optimal reference tracking velocity and achieve online obstacle avoidance guidance. Simultaneously, a fixed-time generalized super-twisting controller based on an extended state observer (ESO-FiTGST) is proposed to restrict the convergence time and tackle with challenge resulting from the uncertain disturbances and model parameters. Noticeably, the design process of the proposed control law and its rigorous stability analysis, especially for the computable fixed-time convergence property, are detailed. Ultimately, the simulation and experimental results demonstrate the superiority and feasibility of the proposed method, providing a reliable and effective reference solution for tracking control tasks of USVs in aquatic scenarios with complex obstacles.

This paper focuses on the design of event-triggered controllers for the synchronization of delayed Takagi–Sugeno (T–S) fuzzy neural networks (NNs) under deception attacks. The traditional event-triggered mechanism (ETM) determines the next trigger based on the current sample, resulting in network congestion. Furthermore, such methods suffer from the issues of deception attacks and unmeasurable system states. To enhance the system stability, we adaptively detect the occurrence of events over a period of time. In addition, deception attacks are recharacterized to describe general scenarios. Specifically, the following enhancements are implemented: First, we use a Bernoulli process to model the occurrence of deception attacks, which can describe a variety of attack scenarios as a type of general Markov process. Second, we introduce a sum-based dynamic discrete event-triggered mechanism (SDDETM), which uses a combination of past sampled measurements and internal dynamic variables to determine subsequent triggering events. Finally, we incorporate a dynamic output feedback controller (DOFC) to ensure the system stability. The concurrent design of the DOFC and SDDETM parameters is achieved through the application of the cone complement linearization (CCL) algorithm. We further perform two simulation examples to validate the effectiveness of the algorithm.

Gait planning is one of the most critical technologies for quadruped robots. However, far too little attention has been paid to the tight coupling mechanism of gait planning with terrain understanding and energy efficiency. To date, it is still challenging to plan optimal gait strategies that are highly adapted to terrain features with stable and efficient transitions. Accordingly, this paper proposes an adaptive gait control framework for quadruped robots that combines terrain recognition, Cost of Transport (CoT), and the Froude (Fr) number. More specifically, an optimal gait selection strategy for quadruped robots is designed based on different terrain texture features and the CoT characteristics of different gaits. To address the gait transition process induced thereby, an adaptive method for gait parameters based on the Fr number is further proposed, which can make the process more stable. Besides, model predictive control (MPC) and whole-body control (WBC) are employed as the motion controllers for the quadruped robot. Furthermore, simulation and experimental results indicate that the proposed method possesses superior terrain adaptability, energy efficiency, and motion stability during gait transitions, which is beneficial for the quadruped robots to maintain stable motion and reduce energy consumption when performing tasks in changeable terrains.Note to Practitioners—This paper is motivated by the problem of adaptive gait planning for quadruped robots that walks through different terrains. We propose a method that ensures optimal gait selection by robots facing diverse terrains and maintains the stability of gait transition. The proposed control framework, upon testing in a simulated environment, can be directly deployed on real-world robot without further adjustments and allows the robot to traverse various terrains with minimal sim-to-real issues. Hopefully, our proposed method can provide valuable guidance and support for facilitating the enhancement of capabilities in performing prolonged endurance tasks in unstructured environments for quadruped robots.

In the field of multisensor fusion for simultaneous localization and mapping (SLAM), monocular cameras and IMUs are widely used to build simple and effective visual-inertial systems. However, limited research has explored the integration of motor-encoder devices to enhance SLAM performance. By incorporating such devices, it is possible to significantly improve active capability and field of view (FOV) with minimal additional cost and structural complexity. This article proposes a novel visual-inertial-encoder tightly coupled odometry (VIEO) based on a video detection and ranging (ViDAR) device. A ViDAR calibration method is introduced to ensure accurate initialization for VIEO. In addition, a platform motion decoupled active SLAM method based on deep reinforcement learning (DRL) is proposed. Experimental data demonstrate that the proposed ViDAR and the VIEO algorithm significantly increase cross-frame co-visibility relationships compared to its corresponding visual-inertial odometry (VIO) algorithm, improving state estimation accuracy. Additionally, the DRL-based active SLAM algorithm, with the ability to decouple from platform motion, can increase the diversity weight of the feature points and further enhance the VIEO algorithm's performance. The proposed methodology sheds fresh insights into both the updated platform design and decoupled approach of active SLAM systems in complex environments.

As a key technology for autonomous underwater operations, precise tracking control in tight space environments is a great challenge. With the aid of high maneuverability of the underwater bionic robot, this article proposes an integrated tracking control framework for a robotic dolphin to move through narrow areas, including top-level planning, middle-level tracking, and bottom-level control allocation. First, a nonlinear model predictive control-based planning method is presented with full consideration of tracking accuracy and obstacle avoidance safety. Second, in order to improve the anti-interference ability, we derive a nonlinear path tracking control law by combining the backstepping technique with a nonlinear disturbance observer. More importantly, through hydrodynamic analysis of the bionic multimodal motions under flippers and flukes, a fuzzy-based nonlinear control allocation system is particularly adopted to convert calculated control forces into bionic motion parameters. Finally, extensive simulations and aquatic experiments are conducted, and the obtained results validate the effectiveness of proposed methods, providing a new idea to further ocean exploration.

To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. We propose a garbage detection method based on a modified YOLOv4, allowing high-speed and high-precision object detection. Specifically, the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection accuracy, YOLOv4 is transformed into a four-scale detection method. To improve the detection speed, model pruning is applied to the new model. By virtue of the improved detection methods, the robot can collect garbage autonomously. The detection speed is up to 66.67 frames/s with a mean average precision (mAP) of 95.099%, and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.

A heuristic fast marching (FM*)-based comprehensive path planning system involving task allocation, initial planning, and replanning is presented for the robotic floating garbage cleaning mission. There are three primary contributions in this paper. First, to tackle the invalidation of the Euclidean distance metric in the obstacle environment, the task allocation is modeled as a travelling salesman problem (TSP) employing the FM*-based distance metric in order to obtain an optimal travel sequence. Second, to meet the maneuverability constraint from the surface robot and avoid the collision, a Gaussian filter is employed to adjust the curvature radius of the generated path. Third, for an efficient replanning, a neural network-based replanning point generator with the input of garbage movement vector is provided to strike a compromise for the distance cost and the computational burden. Moreover, a case study and a virtual obstacle experiment in the laboratory water tank demonstrate the feasibility of the proposed comprehensive path planning system. This work lays a firm foundation for the development of intelligent equipment for aquatic environment protection.

Underwater visual localization is an essential technique for the autonomous operation of underwater robots. However, the unique underwater image characteristics, including refraction, sparse features, and severe noise, pose an enormous challenge to it. For addressing these issues, this paper proposes an open-source fiducial-based underwater stereo visual-inertial localization method under the extended Kalman filter (EKF) framework, which is called FBUS-EKF. First, the refraction is corrected by the refractive camera model and akin triangulation. Second, the fiducial marker and a novel marker pose estimation method are applied to alleviate the adverse effect of sparse features. Third, the EKF is utilized to fuse the inertial and visual information so as to reject the serious noise. Finally, extensive experiments on a test bench demonstrate the effectiveness of the FBUS-EKF method, where the typical localization error is less than 3%, namely, the average error is lower than 3 cm within one meter. The obtained results reveal that the FBUS-EKF method has the prospect to be applied in the precise short-range operation and the localization for underwater robots, which offers a valuable insight for further autonomous underwater task.

Object detection has been vigorously investigated for years but fast accurate detection for real-world scenes remains a very challenging problem. Overcoming drawbacks of single-stage detectors, we take aim at precisely detecting objects for static and temporal scenes in real time. Firstly, as a dual refinement mechanism, a novel anchor-offset detection is designed, which includes an anchor refinement, a feature location refinement, and a deformable detection head. This new detection mode is able to simultaneously perform two-step regression and capture accurate object features. Based on the anchor-offset detection, a dual refinement network (DRNet) is developed for high-performance static detection, where a multi-deformable head is further designed to leverage contextual information for describing objects. As for temporal detection in videos, temporal refinement networks (TRNet) and temporal dual refinement networks (TDRNet) are developed by propagating the refinement information across time. We also propose a soft refinement strategy to temporally match object motion with the previous refinement. Our proposed methods are evaluated on PASCAL VOC, COCO, and ImageNet VID datasets. Extensive comparisons on static and temporal detection verify the superiority of DRNet, TRNet, and TDRNet. Consequently, our developed approaches run in a fairly fast speed, and in the meantime achieve a significantly enhanced detection accuracy, i.e., 84.4% mAP on VOC 2007, 83.6% mAP on VOC 2012, 69.4% mAP on VID 2017, and 42.4% AP on COCO. Ultimately, producing encouraging results, our methods are applied to online underwater object detection and grasping with an autonomous system. Codes are publicly available at https://github.com/SeanChenxy/TDRN.

In this article, an extended state observer (ESO)-based controller with a model predictive governor is designed for 3-D trajectory tracking of underactuated underwater vehicles. The proposed control scheme takes three primary challenges including underactuated property, velocity constraint, and lumped disturbance into consideration. With respect to the model predictive governor, an underactuated kinematic tracking error model is utilized to produce reference velocities. Meanwhile, a heading angle compensation mechanism is utilized to avoid the steady tracking errors resulting from dynamics coupling of the vehicle. Besides, an ESO is designed to estimate the lumped disturbances and unmeasured velocity states. Based on the ESO, a kinetic controller is offered to accomplish the precise velocity tracking only in virtue of the position and orientation information. Note that this article details both the design process of the control scheme and rigorous theoretical analysis. Eventually, simulation and experimental results demonstrate the feasibility and superiority of the proposed method. Notably, this work lays the foundation for the underactuated trajectory tracking control in complicated and turbulent underwater environments.

In this article, a robot system for intelligent water surface cleaner named IWSCR is developed to collect floating plastic garbage. It is able to accomplish three major tasks autonomously, i.e., cruise and detection, tracking and steering, and grasping and collection. The challenges behind these tasks involve how to realize the accurate and real-time garbage detection, how to resist the disturbances while IWSCR conducts vision-based steering, and how to grasp the floating garbage reliably despite the turbulent conditions on the surface of the water. To overcome these difficulties, three key techniques are proposed for IWSCR. First, the YOLOv3 network, which is widely applied in the high speed and accuracy object detection field, is trained on the proposed floating garbage dataset to realize accurate and real-time garbage detection. Next, to improve the ability of resisting disturbances, a control law based on the sliding-mode controller is proposed for vision-based steering. Furthermore, inspired by the stability of floating bottles in fluid, a feasible grasping strategy is utilized for IWSCR. Finally, the experimental results demonstrate that IWSCR is competent to carry out the task of water surface cleaning.

Most existing robotic fish have a large body size driven by servo motor system, while conventional small-sized actuators hardly generate a high swimming performance. This paper reports a miniature untethered robotic fish, whose body length is 69 mm. In particular, a newly designed magnetic actuator system (MAS) is equipped, which guarantees both small-sized dimension and flexibility of the robot. More specifically, the magnetic field generated by a permanent magnet is first investigated based on Biot-Savart law. Then, a novel tail-beating rhythm called magnetically actuated pulse width modulation (MAPWM) is modeled for the new actuator system. Further, an MAPWM-based control method is presented, in which the duty ratio of MAPWAM is innovatively utilized to realize the turning maneuvers for the first time. In addition, Lagrangian method is employed to establish the dynamic model to assess the MAPWM-based control method and the turning performance of the robotic fish. To further improve the maneuverability, the effect of a shape-variable caudal fin is analyzed based on computational fluid dynamics and the built dynamic model. Finally, combined with the MAS, the MAPWM-based control method, and the optimally selected caudal fin, extensive aquatic experiments are conducted on the robotic prototype. The results indicate that the developed miniature robotic fish achieves a considerably higher level of maneuverability in terms of turning radius when compared to swimming robots with equivalent dimensions.

Object detection methods fall into two categories, i.e., two-stage and single-stage detectors. The former is characterized by high detection accuracy while the latter usually has a considerable inference speed. Hence, it is imperative to fuse their merits for a better accuracy vs. speed trade-off. To this end, we propose a dual refinement network (DRN) to boost the performance of the single-stage detector. Inheriting from the advantages of two-stage approaches (i.e., two-step regression and accurate features for detection), anchor refinement and feature offset refinement are conducted in a novel anchor-offset detection, where the detection head is comprised of deformable convolutions. Moreover, to leverage contextual information for describing objects, we design a multi-deformable head, in which multiple detection paths with different receptive field sizes devote themselves to detecting objects. Extensive experiments on PASCAL VOC and ImageNet VID datasets are conducted, and we achieve a state-of-the-art detection performance in terms of both accuracy and inference speed.