To address the issues of slow convergence, susceptibility to local optima and deadlock, and reduced population diversity in later stages of ant colony optimization (ACO), this paper proposes an improved ACO algorithm based on reinforcement learning (RBIACO). First, an exploration auxiliary factor is introduced to avoid deadlock and encourage ants to actively explore unvisited areas, thereby enhancing global search capability. Second, an improved pheromone increment formula is proposed, which assigns differentiated pheromone updates to nodes of different types within the same feasible path, alleviating the masking of key nodes caused by uniform updates in traditional methods. Furthermore, inspired by Q‐learning, a node value estimation mechanism is designed to evaluate the potential value of each node and is applied as a coefficient to the pheromone update process. Leveraging the search and self‐evolution features of ant colonies, this mechanism effectively improves overall algorithm performance. Finally, simulations on maps of varying scales were conducted, comparing RBIACO with basic ACO, particle swarm optimization (PSO), and IQBACO algorithms. The results demonstrate that RBIACO achieves significantly faster convergence, better global search ability, and higher search efficiency.
To address the issues of insertion deflection caused by the inherent properties of standard puncture needles in clinical prostate particle implantation, the overemphasis of existing control algorithms on single puncture positioning performance at the expense of continuous puncture reliability, and the lack of precise support for soft tissue environmental stiffness, this study proposes a reliability control algorithm for puncture deflection and continuous positioning for prostate particle implantation. First, an interaction model between the puncture needle and glandular soft tissue is constructed, the deflection mechanism is analyzed, and the core role of soft tissue stiffness parameters in deflection regulation is clarified. Second, by integrating Hooke’s law with measurements from force and displacement sensors, the least squares method is adopted to achieve online estimation of human prostate soft tissue environmental stiffness, providing real-time stiffness parameters for deflection control. Finally, a closed-loop control model incorporating data acquisition, deviation identification and instruction feedback is constructed, the needle posture is adjusted via spatial transformation of the puncture robotic arm, a particle filter algorithm is adopted to evaluate positioning reliability in real time, and the spatial adjustment of the needle tip is derived to ensure continuous puncture accuracy. Comparative experiments on prostate bionic soft tissues demonstrate that the proposed online stiffness estimation method takes approximately 3 s and can rapidly and synchronously track dynamic stiffness variations during puncture, and that the deflection and continuous puncture control method is effective and reliable, achieving a target positioning overlap rate of 95.6%. This algorithm ensures the therapeutic efficacy of prostate particle implantation and provides a phased theoretical reference for research on precision control systems for puncture robots.
Accurate localization of unmanned aerial vehicles (UAVs) is essential for applications such as structural health monitoring, especially in environments where Global Positioning System (GPS) signals are denied or unreliable, like indoor spaces, tunnels, urban canyons, or areas beneath large structures. To address this challenge, we propose Cross-Fusion, a novel method for real-time UAV localization that integrates data from a 3D Light Detection and Ranging (LiDAR) and a monocular camera. A key contribution is its cross-session fusion strategy, which integrates visual and geometric information collected from multiple agents during routine baseline surveys to improve localization consistency and map completeness. The system employs LiDAR-based odometry for motion tracking and image-based feature matching via a single red-green-blue (RGB) camera to correct drift and improve accuracy. Unlike visual-inertial systems, Cross-Fusion maintains a simple sensor setup and avoids the complexity of stereo or global shutter configurations. Experimental results demonstrate that Cross-Fusion achieves localization accuracy comparable to GPS-based methods and performs reliably in challenging feature-sparse environments.
Water sample and monitoring is essential for protecting aquatic ecosystems and detecting environmental pollution. This paper presents the design and experimental validation of a bio-inspired miniature submarine for low-cost water sample and monitoring. Inspired by the jet propulsion mechanism of squids, the proposed system employs pump-driven water jets for propulsion and steering, combined with a pump-based buoyancy control mechanism that enables both depth regulation and water sampling. The vehicle integrates low-cost, commercially available components including an ESP32 microcontroller, IMU, pressure sensor, GPS receiver, and LoRa communication module. The complete system can be constructed at a hardware cost of approximately $122.5, making it suitable for educational and environmental monitoring applications. Experimental validation was conducted through pool tests and field trials in a lake. During a 360 degrees rotation test, roll and pitch deviations remained within +/- 2 degrees and +/- 1.5 degrees, respectively, demonstrating stable attitude control. Steering experiments showed a heading step response with approximately 2 s rise time and 5 s settling time. Depth control experiments achieved a target depth of 2.5 m with steady-state error within +/- 0.1 m. Field experiments further demonstrated reliable navigation and successful water sampling operations. The results confirm that the proposed platform provides a compact, stable, and cost-effective solution for small-scale environmental monitoring in shallow-water environments.
Achieving precise robotic manipulation is difficult when objects are unknown or not pretrained, and the environment is unpredictable. Traditional cameras often struggle with depth perception and lag when a robot gets close to its target. To address these limitations, this study explores the design and implementation of an image-based visual servoing (IBVS) system for a 5-degree-of-freedom (DOF) robotic arm mounted with an eye-in-hand camera and sonar sensor. The proposed system enables real-time object tracking and autonomous grasp using fusion of visual and depth feedback in the Robot Operating System (ROS) 2 and MoveIt Servo frameworks. A Channel and Spatial Reliability Tracker (CSRT)-based tracking algorithm is applied for precise object localization and consistent performance after a comparative analysis with some other tracking algorithms for object dynamic movement up to 30 rpm. The depth estimation using the sonar sensor provides an average error of 1.2 cm in the 5-30 cm working range to provide accurate reconstruction of the object's 3D location. The MoveIt Servo controller generates joint-space trajectories with smooth velocity changes. Experimental trials with 40 grasping tasks had a total success rate of 80% in manipulation, with consistent performance for objects of different sizes and shapes. Results above confirm the correctness of the presented method in providing adaptive robot control and reducing human operators' manual operation effort. This hybrid feedback approach provides a reliable foundation for real-time visual servoing, overcoming the latency and precision constraints of traditional vision-only methods.
