This study investigated the trajectory-planning problem of a six-axis robotic arm based on deep reinforcement learning. Taking into account several characteristics of robot motion, a multi-objective optimization approach is proposed, which was based on the motivations of deep reinforcement learning and optimal planning. The optimal trajectory was considered with respect to multiple objectives, aiming to minimize factors such as accuracy, energy consumption, and smoothness. The multiple objectives were integrated into the reinforcement learning environment to achieve the desired trajectory. Based on forward and inverse kinematics, the joint angles and Cartesian coordinates were used as the input parameters, while the joint angle estimation served as the output. To enable the environment to rapidly find more-efficient solutions, the decaying episode mechanism was employed throughout the training process. The distribution of the trajectory points was improved in terms of uniformity and smoothness, which greatly contributed to the optimization of the robotic arm’s trajectory. The proposed method demonstrated its effectiveness in comparison with the RRT algorithm, as evidenced by the simulations and physical experiments.

In this paper, a novel path-following and obstacle avoidance control method is given for nonholonomic wheeled mobile robots (NWMRs), based on deep reinforcement learning. The model for path-following is investigated first, and then applied to the proposed reinforcement learning control strategy. The proposed control method can achieve path-following control through interacting with the environment of the set path. The path-following control method is mainly based on the design of the state and reward function in the training of the reinforcement learning. For extra obstacle avoidance problems in following, the state and reward function is redesigned by utilizing both distance and directional perspective aspects, and a minimum representative value is proposed to deal with the occurrence of multiple obstacles in the path-following environment. Through the reinforcement learning algorithm deep deterministic policy gradient (DDPG), the NWMR can gradually achieve the path it is required to follow and avoid the obstacles in simulation experiments, and the effectiveness of the proposed algorithm is verified.

This article presents a tracking control approach with obstacle avoidance for a mobile robot. The control law is composed of two parts. The first is a discrete-time model predictive method-based trajectory tracking control law that is derived using an optimal quadratic algorithm. The second part is the obstacle avoidance strategies that switch according to two different designed obstacle avoidance regions. The controllability of the avoidance control law is analyzed. The simulation results validate the effectiveness of the proposed control law considering both trajectory tracking and obstacle avoidance.

Utilizing Cone Beam Computed Tomography (CBCT) to monitor Minimally Invasive Surgery (MIS) is essential for ensuring surgical safety, particularly in high-precision procedures such as cochlear implantation (CI). Existing CBCT systems—including whole-body units and those tailored for ear diagnostics, dentistry, or head imaging—are typically bulky and poorly integrated, limiting their utility in intraoperative environments. To address this gap, we propose a highly integrated, high-resolution CBCT system specifically designed for intraoperative cochlear MIS. The system incorporates a dual-joint robotic structure, enabling both compact integration and mechanical flexibility by allowing the imaging chain to be folded to accommodate other surgical instruments. From a measurement perspective, the system is engineered to optimize key imaging performance parameters through a balance between Field of View (FOV), spatial resolution, and mechanical stability. Geometric parameters were quantitatively optimized to minimize rotational bending moments, ensuring consistent and precise scanning. Imaging performance was systematically assessed using four types of phantoms. Quantitative measurements demonstrated a spatial resolution of 150 μm (3.3 lp/mm), a Contrast-to-Noise Ratio (CNR) of 8.63 at a 2.65% contrast level, and a 3D uniformity deviation of 1.8%. Additionally, qualitative validation was conducted using a live pig and a human cadaver head to simulate real clinical conditions. These findings highlight the system’s capability to deliver accurate, repeatable, and measurement-driven intraoperative imaging, offering enhanced safety and precision for cochlear MIS. The work contributes not only to imaging system integration but also to quantitative measurement methodology aligned with the focus of instrumentation and measurement research.