Hybrid boom lifts operating in cluttered workspaces with rigid obstacles face a control conflict between accurate trajectory tracking and safe obstacle avoidance, which can lead to discontinuous joint velocities and abrupt end-effector motions. Existing methods still struggle to balance tracking performance, collision safety, and motion smoothness. To address this problem, this paper presents a Dual-Adaptive Control (DAC) framework that coordinates task priorities through two synergistic mechanisms. The Adaptive Obstacle Response mechanism introduces a dual-threshold strategy with critical and warning distances, adjusting the commanded avoidance velocity online according to estimated collision risks. In parallel, the Adaptive Control Priority mechanism links feedback gains to the pseudo-distance so that control authority between tracking and avoidance is reallocated smoothly rather than abruptly. Simulation and experimental studies on a 15 m hybrid boom lift show that, compared with a conventional fixed-gain method and representative real-time obstacle-avoidance algorithms including APF, VFF, CBF-QP, and WGPM, the DAC framework maintains continuous joint velocities while improving trajectory smoothness. Additional validations include moving-obstacle speed-sweep simulation, 3D avoidance under irregular composite obstacles, and tests under two payload configurations, Empty and approximately 190 kg Loaded, conducted with identical controller parameters to verify parameter consistency. The peak rate of tracking-error change is reduced by 89.7%, and the peak joint angular acceleration is reduced by more than 99%, supporting the engineering applicability of the proposed approach under the tested scenarios.
Underwater multi-robot and swarm systems require advanced methodologies for controlling collective behavior. Conventional single-robot techniques, such as tethering, sonar imaging, and acoustic localization, are not scalable or effective for swarm applications. This work introduces novel approaches for visual detection, relative localization, and control of a system of multiple autonomous underwater robots. In particular, we focus on agent-to-agent visual localization to inform control strategies and enable the emergence of coordinated swarm behaviors. The proposed methodologies have been implemented and tested on a fleet of autonomous underwater robots developed as part of the HSURF project. We present a thorough experimental validation of the integrated detection-to-control system, demonstrating its robustness and effectiveness in realistic, real-world-like conditions. This research marks a significant step forward in developing scalable solutions for underwater multi-robot systems.
Accurate odometry estimation is fundamental to autonomous navigation, enabling precise localization and continuous motion tracking. While vision-based and lidar-based odometry systems have reached a high level of maturity, their performance deteriorates significantly in adverse environmental conditions, such as low visibility, inclement weather, or dynamic lighting. In contrast, 4D radar offers inherent robustness to such challenges, along with extended range and velocity-sensing capabilities, making it a compelling alternative for long-term, all-weather perception. In this paper, we present an end-to-end neural network-based odometry framework that directly leverages raw ADC data from 4D radars, surpassing conventional methods that are limited to processing only 4D point cloud information. We introduce a lossless encoding scheme that converts the raw ADC signals into structured heatmap representations, preserving essential spatial and temporal information. To extract meaningful features from this rich representation, we design a hybrid attention architecture that combines intra-frame self-attention for enhanced spatial understanding with inter-frame cross-attention to model fine-grained temporal dependencies. Comprehensive evaluations on the Coloradar dataset show that our method significantly outperforms state-of-the-art radar-based odometry approaches based on traditional feature matching or deep learning. It achieves superior accuracy and robustness, highlighting its strong potential for real-world deployment in autonomous navigation systems under challenging conditions. Our code is available at https://github.com/MoYuGit/Deep_Radar_Odometry.
To address the limitations of traditional unmanned aerial vehicles (UAVs) and wall-climbing robots in bridge-pier defect detection, this research proposes a circular-climbing robot specifically designed for bridge piers. First, based on the geometric characteristics of bridge piers and their construction environment, a circular robot frame is designed, and its mechanical behavior is analyzed to determine the conditions for stable climbing. Second, based on attitude information collected by multiple gyroscopes, an attitude estimation method for the circular robot and a multi-wheel cooperative motion control algorithm are developed. On this basis, a multi-motor master-slave fuzzy proportional integral derivative (PID) controller is implemented on a field programmable gate array (FPGA) platform to perform fuzzy inference on the attitude information, generate real-time control parameters, and adaptively adjust the speeds of the crawling mechanisms, thereby achieving coordinated motion control of the multi-wheel system. Finally, experiments are conducted to verify the system's reliability in terms of climbing stability and motion control accuracy. A comparative analysis further shows that the proposed robot exhibits a more stable mechanical structure and greater adaptability than existing wall-climbing robots, and offers advantages in contact pressure adjustment, load capacity, and scalability, enabling it to carry various bridge pier inspection devices for different application needs. This work provides a practical platform for the automated health monitoring of bridge piers.
In recent years, the demand for autonomous navigation of underwater biomimetic robots in complex environments has continued to grow, among which monocular visual simultaneous localization and mapping (SLAM) has emerged as a key technology for achieving lightweight navigation and localization. However, typical underwater illumination conditions, such as low-light and artificial lighting, significantly degrade image quality and feature observability, thereby severely limiting the robustness and accuracy of existing visual SLAM systems. To address these challenges, this paper proposes a monocular visual SLAM system for bionic robotic fish, termed GSL-SLAM, built upon the ORB-SLAM3 framework. The proposed system integrates adaptive underwater image enhancement with learning-based feature extraction and matching to improve visual usability and feature matching stability under complex illumination conditions. A multi-threaded system architecture is further adopted to ensure real-time performance. Extensive experiments conducted on both a laboratory pool dataset and the public AquaticVision dataset demonstrate that the proposed method consistently outperforms ORB-SLAM3 under various underwater lighting conditions, including natural light, low light, and artificial illumination. Compared with the baseline method, GSL-SLAM achieves significant improvements in initialization efficiency, map completeness, and localization accuracy, with particularly notable advantages under low-light and artificial lighting scenarios. Moreover, the system operates stably at approximately 20 Hz on the embedded platform, satisfying the real-time perception and navigation requirements of the bionic robotic fish. The experimental results indicate that GSL-SLAM substantially enhances the robustness and practicality of monocular visual SLAM in complex underwater lighting environments, providing an effective visual localization and mapping solution for lightweight autonomous underwater robots.
