This paper presents an improved S-curve motion profile to address vibration issues in industrial robots. While numerous studies have optimized motion commands based on the S-curve feedrate profile for vibration suppression, their performance in damped systems has often been suboptimal. To this end, the proposed profile integrates an improved S-curve feedrate motion with local input shaping to accelerate vibration attenuation. The design method is also introduced to minimize its total motion time. Compared with the optimized S-curve, and the time-optimal smooth S-curve, the proposed method based on the improved S-curve possesses superior vibration suppression capability. Furthermore, the improved S-curve achieves this with less time delay than the optimized smooth S-curve. Experimental validations on a serial industrial robot and a gantry platform confirm the practical effectiveness of the proposed motion profile in reducing vibrations, supporting its applicability in robotic systems.

This paper presents the design, implementation, and experimental validation of a compact Ackermann-steered mobile robot platform developed for autonomous navigation and diverse service applications. The platform integrates dual stereo cameras and an inertial measurement unit to ensure robust localization, alongside a pan-tilt RGB-D camera designed for active perception tasks. Specifically, the dual stereo cameras mounted at the front and rear provide bidirectional visual odometry, which is fused with inertial measurements via an extended Kalman filter to achieve accurate, drift-reduced state estimation. Furthermore, the pan-tilt actuator enables dynamic viewpoint control, facilitating robust object tracking and dense three-dimensional (3D) perception. The system is built upon the Robot Operating System 2 framework, providing a modular and extensible architecture that allows for seamless integration with various perception and navigation algorithms. Experimental evaluations—including velocity tracking, dual visual odometry benchmarking, mapless navigation, and real-time 3D mapping—validate the effectiveness and versatility of the proposed platform in outdoor environments.

Robots that operate in static environments with fixed obstacles are crucial for automating processes in industries such as manufacturing, warehousing, and healthcare. Although these environments are static, designing a navigation algorithm that achieves high accuracy, reasonable efficiency, and low computational complexity remains a significant challenge. Besides performing well in familiar settings, the algorithm must also be capable of generalizing to other static environments with different obstacle configurations.To address these challenges, this paper presents a navigation algorithm based on deep reinforcement learning (DRL), specifically a combination of double Q-networks, multi-stage learning, and priority experience replay (PER). This algorithm is referred to as PER-MS-DDQN. The key innovation of this research is the simultaneous development of a new reward function derived from the A* algorithm and a new definition of robot states. This combination enhances navigation accuracy, speeds up the learning process, and mitigates the issue of scattered rewards. The proposed algorithm was initially trained in a basic static environment using Grid World and subsequently evaluated both in this familiar setting and in larger, more complex static environments, including the ROS2 software environment. Additionally, the algorithm was tested in a real-world environment. The results indicate that over 500 episodes, the robot achieved a success rate of 99% in reaching its goals. Moreover, there was a significant improvement in both accuracy and navigation time compared to previous methods. These findings demonstrate the practicality and efficiency of the proposed algorithm and its ability to generalize in real-world scenarios.

Tendon-driven continuum robots offer exceptional flexibility and slenderness, making them particularly suitable for inspection and maintenance in constrained and unstructured environments as well as robotic surgery. However, their control remains challenging due to nonlinear behavior, limited sensory feedback, and a significant kinematic dissimilarity from existing controllers. These challenges make continuum robots, similarly to endoscopes, their non-robotic counterparts, require a cost- and time-expensive training for users who operate them in such critical environments. This paper addresses this point by designing and implementing a custom, user-friendly control interface for a tendon-driven continuum robot, leveraging principles from both robotics and gaming to enhance intuitiveness and accessibility. After developing a custom graphical user interface, a prototype has been manufactured to evaluate system performance in a dedicated testing environment. Twelve participants have attempted a timed navigation task using the proposed interface, with data collected to assess usability and control accuracy. Results confirmed the feasibility and effectiveness of the system: users were able to navigate the robot through the course with minimal training. Participant feedback is then analyzed to identify meaningful directions for improvement.

Scholars have proposed a cable-driven parallel robot (CDPR) with aerial and ground actuators, referred to as a cable-towed aerial platform (CTAP), to address the limited ability to withstand external forces of existing unmanned arrival vehicle (UAV)-based aerial platforms. To tackle practical problems (e.g., firefighting), a CTAP may need to perform motion planning in an environment with obstacles and communication failures. To this end, this article proposes an online decentralized planning approach based on multi-agent reinforcement learning (MARL) for a CTAP to achieve real-time motion planning in a communication-denied environment with obstacles. This article then defines the state and action spaces of a MARL-based planner. A reward function is designed for the MARL-based planning approach according to the widely used optimization-based planning approach. This study has successfully trained a MARL-based planner using this approach, and the important training techniques used in this study are reported. Statistical comparison of the MARL-based decentralized planner and an optimization-based centralized planner is conducted in simulation. The MARL-based and optimization-based planners are deployed to a CTAP prototype to address motion planning problems in the real world. Experimental results show that the MARL-based planner can achieve successful, decentralized, and online motion planning for a CTAP.

With complex dynamic characteristics in many variables changing during actual operation, a Mecanum-wheeled mobile robot (MWMR) causes many difficulties for path planning and control. In addition, in many applications, robots need to operate automatically and find the optimal path. This article proposes intelligent MWMR path planning based on the real-time rapidly exploring random tree* (RT-RRT*) and optimal interval type 2 fuzzy logic controller (IT2FLC). First, the path planning of the MWMRs in dynamic environments is developed by RT-RRT*. Second, the optimal IT2FLC is designed for MWMRs based on the genetic algorithm. The pre-treatment and post-treatment coefficients of IT2FLC are optimized with the goal of achieving the best trajectory quality of the robot. This helps the robot to operate independently and accurately, and complete tasks in environments with many complex disturbances. Various trajectories are given to test the performance of the proposed approach. The results show that the efficiency of the designed method is better than that of a type 1 fuzzy logic controller (traditional fuzzy logic controller) and proportional–integral–derivative under the same conditions.

Offline reinforcement learning, which learns solely from datasets without environmental interaction, has gained attention. This approach, similar to traditional online deep reinforcement learning, is particularly promising for robot control applications. Nevertheless, its robustness against real-world challenges, such as joint actuator faults in robots, remains a critical concern. This study evaluates the robustness of existing offline reinforcement learning methods using legged robots from OpenAI Gym based on average episodic rewards. For robustness evaluation, we simulate failures by incorporating both random and adversarial perturbations, representing worst-case scenarios, into the joint torque signals. Our experiments show that existing offline reinforcement learning methods exhibit significant vulnerabilities to these action perturbations and are more vulnerable than online reinforcement learning methods, highlighting the need for more robust approaches in this field.

Objective Traditional computed tomography (CT)-guided tumor ablation procedures are often hampered by the need for repeated scans and significant radiation exposure, primarily due to challenges in manual needle insertion and the iterative realignment process. This research introduces an advanced intraoperative CT (iCT)-guided robotic system. The core aim is to enhance procedural accuracy and efficiency by implementing precise, real-time remote-center-of-motion (RCM) control for needle interventions. Methods This study develops an integrated iCT-guided robotic needle biopsy/ablation system. A key distinction from conventional iterative navigation systems is their capability for surgeons to dynamically adjust the intervention line in situ. Real-time visualization of relative positioning is achieved using 3D (three-dimensional) Slicer software. This is coupled with a novel and robust RCM control strategy, meticulously engineered based on screw axis theory and velocity twist, and further optimized with interpolation and feedforward control laws. This framework enables precise, surgeon-guided adjustments of the guiding catheter's posture throughout the procedure. Results The RCM control system effectively and reliably governed the motion of the robotic guidance arm. This ensured that the catheter tube consistently maintained its accurate orientation relative to the target even after multiple surgeon-adjusted posture modifications. Experimental validation demonstrated the system's high performance, achieving exceptionally low RCM errors as 2×10 −4 m. Consequently, any deliberate pause during the robotic arm's movement could be utilized to define a precisely targeted and safe puncture path. Conclusion and significance Comprehensive numerical modeling and experimental evaluations using a TM5-700 robotic arm with phantoms have provided validation for the proposed screw axis-based RCM control concept. Integrating iCT guidance with this RCM system indicates increased precision, operational flexibility, and overall safety of robotic needle-based interventions. This work presents a noteworthy contribution with potential for clinical adoption in various minimally invasive procedures.

In this article, considering uncertainty and nonlinearity factors, the resonance phenomenon of conveyance is studied and a fuzzy adaptive back-stepping controller is proposed for rope lifting system with time-varying length and distributed parameters. Firstly, the dynamic model is constructed and derived by continuum mechanics, and the dynamical behavior is obtained by time-frequency analysis method. Secondly, the fuzzy adaptive back-stepping control method is proposed to suppress nonstationary vibration. Thirdly, based on Lyapunov function, the asymptotic stability of the system under the proposed control algorithm is verified. Finally, compared with proportion–integration–differentiation (PID) back-stepping controller, the efficiency of the proposed controller is proven by applying excitation with different frequency and uncertain parameters, and the adaptability and robustness are illustrated to validate under mixed frequency disturbance by simulation compared with PID back-stepping controller. Based on the dynamical behavior analysis and control method proposed in this article, the robustness of vibration controller of time-varying lifting system under uncertain nonlinear time-varying disturbance will be greatly improved.

This paper proposes an improved switching nonlinear proportional-derivative (S-NPD) control algorithm for a six-degree-of-freedom Stewart platform which has strong nonlinearity and uncertainty characteristics, designed to achieve vibration isolation and suppression over a wide bandwidth range while solving the problem of the difficulty in parameter tuning. The controller is designed based on the idea of variable-structure control, which achieves independent real-time adjustments of the control gains of the six legs and takes the sign of the proportional and differential errors as the switching function to realize the feed-forward correction of the proportional gain. This control input can track the expected value more precisely, thus strengthening the system's ability to cope with high dynamics. To validate the results of the proposed approach, numerical simulations are compared with those obtained from PD and NPD controls. The findings confirmed that the designed S-NPD controller shows commendable stability and robustness, and the vibration attenuation of the upper platform under low- and medium-frequency conditions is substantially improved. In addition, it obtains smoother vibration waveforms and lower control force under random disturbance conditions. Consequently, it is proved that the S-NPD control can obtain better regulation accuracy. Compared with the PD and NPD controls, the S-NPD control achieves up to 64% and 31.4% relative performance improvement in vibration attenuation rate, respectively.