With the continuous development of robotic technology, there is an increasing demand for efficient, smooth, and precise trajectory planning and interpolation. For the robot manipulators with 6 rotational (6R) joints, smooth task paths are crucial for high-quality task and precise dynamic trajectory tracking. However, nonlinear mapping between joint and cartesian space complicates feedrate scheduling under joint constraints. Existing methods, like S-shape feedrate profiles or time-optimal approaches, are inefficient or compromise trajectory stability. This paper proposes a segment-based dynamically adaptive smooth look-ahead feedrate scheduling method based on local dynamic window and a Maximum Velocity Curve (MVC) for 6R robot manipulators. It balances the efficiency and stability of motion execution while considering spline trajectory and joint constraints. Five types of segmented velocities under the local dynamic window are identified, with adaptive smoothing strategies developed. Feedrate remains constant within segments and transitions smoothly between them, enhancing trajectory quality. The results of the smoothing preprocessing can be directly used for feedrate profile generation, ensuring smooth, non-oscillating motion while meeting performance and constraint requirements, which is better suited for real-time interpolation. Simulation and experimentation confirm the proposed method’s effectiveness. Note to Practitioners —The motivation of this article stems from the need to develop a feedrate scheduling method with joint jerk constraints of 6R robot manipulators for practical tasks like polishing, engraving, welding, and spraying. 6R robots, due to their nonlinear coupling kinematics, often fail to meet joint constraints. Existing methods, inefficient or requiring frequent changes in joint acceleration/jerk for time optimality, adversely affect task quality. To balance smoothness and operational efficiency for 6R robot tasks, we propose the segmented dynamic adaptive look-ahead smoothing feedrate scheduling with joint jerk constraints of 6R robot manipulators, ensuring trajectory execution within joint limits and balancing smoothness and operational efficiency, which is better suited for real-time interpolation. This method can be of great interest to readers working on precision manufacturing and robotics feedrate profile generation method.

With the advantages of large working space, low cost and more flexibility, industrial robots have become an important carrier in intelligent manufacturing. Due to the low rigidity of robotic milling systems, cutting vibrations are inevitable and have a significant impact on surface quality and machining accuracy. To improve the machining performance of the robot, a posture optimization approach based on the dynamic response index is proposed, which combines posture-dependent dynamic characteristics with surface quality for robotic milling. First, modal tests are conducted at sampled points to estimate the posture-dependent dynamic parameters of the robotic milling system. The modal parameters at the unsampled points are further predicted using the inverse distance weighted method. By combining posture-independent modal parameters with calibrating the cutting forces, a dynamic model of a robotic milling system is established and solved with a semi-discretization method. A dynamic response index is then introduced, calculated based on the extraction of the vibration signal peaks. The optimization model is validated through milling experiments, demonstrating that optimizing redundant angles significantly enhances milling stability and quality.

NURBS curve parameter interpolation is extensively employed in precision trajectory tasks for industrial robots due to its smoother performance compared to traditional linear or circular interpolation methods. The trajectory planning systems for industrial robots necessitate four essential functional modules: first, the spline curve discretization technique ensuring chord error compliance; second, the contour scanning technique for determining the maximum feasible feed rate for multi-constraint and multi-segment paths; third, the technique for achieving a smooth feed rate profile; and fourth, the continuous curve parameter interpolation technique. Therefore, this paper proposes a high-precision planar NURBS interpolation system for industrial robots. Firstly, a segmentation method for NURBS curves based on a closed-loop chord error constraint is proposed, which segments the original global NURBS curve into a collection of Bezier curves that strictly meet the chord error constraint. Secondly, a bidirectional scanning technique is presented to meet the joint space constraint, establishing an analytical mapping between the tool tip kinematic constraint and the joint kinematic constraint. Then, based on the traditional S-shaped feed rate profile, an adaptive algorithm with a displacement constraint is introduced, considering the real-time speed adjustment requirements of robots. Finally, a compensation interpolation strategy based on arc length parameterization is adopted to solve the accumulated error problem in parameter interpolation. The effectiveness of and potential for enhancing the quality of planar machining of the proposed planar NURBS interpolation system for industrial robots are validated through simulations and experiments. The results demonstrate the system’s applicability and accuracy, and its ability to improve planar machining quality.

As an important transmission component of industrial robots, the harmonic reducer determines the positioning accuracy, bearing capacity and service life of the robot end-effector. Predicting the performance can grasp the working status in advance and avoid major losses caused by uncertain factors such as component damage. The current paper focuses on a harmonic reducer performance prediction algorithm based on Multivariate State Estimation Technique (MSET) and LargeVis dimensionality reduction. Firstly, an accelerated life test platform is designed to collect multi-dimensional parameters that can characterize the operating state of the harmonic reducer throughout the life cycle. Afterwards, as far as the MSET method is concerned, the fault warning threshold is set according to the residual between the constructed memory matrix of the health state data and the actual observed value. Finally, utilizing LargeVis to reduce the dimensionality of multi-dimensional features, combining with Mahalanobis distance to construct a health index degradation model, and then selecting Long Short-Term Memory (LSTM) network to predict the downward trend of the harmonic reducer. The analysis of the accelerated life test data of the harmonic reducer demonstrates that the proposed method can send out the fault warning signal 18 minutes in advance in the sample with a life of 5.7 hours, and has a strong ability to predict the degradation trend of the harmonic reducer.

Aiming at the problems such as insufficient driving force, body oscillation, and contact instability that easily occur when wheel-based wall-climbing robots operate on discontinuous wall surfaces (such as corners and obstacles), this paper proposes an adaptive control strategy integrating joint impedance control and fuzzy decoupling compensation for a Five-Wheel Wall-Climbing robot(FWWC-robot). This strategy optimizes impedance parameters (mass, damping, and stiffness) online through an adaptive parameter update mechanism based on force error to minimize magnetic gap fluctuations and ensure stable adsorption. Meanwhile, a Mamdani fuzzy controller is used to dynamically compensate for the coupling interference of the front and rear swing arms, realizing real-time adjustment of joint compliance and improving the robot’s motion stability and dynamic adaptability when encountering abrupt changes in wall structures. Simulation and experimental results show that, compared with the traditional impedance control, this method reduces the attitude error by 60%, controls the magnetic gap fluctuation within 0.3 mm, achieves a 100% success rate of obstacle crossing under the working conditions of 63° corner and 30 mm obstacle, and keeps the attitude stability within ±1.2°. It provides an efficient and robust solution for the compliant motion of robots in complex wall environments, and can directly support the automated applications in high-risk operation scenarios such as ship derusting and large oil tank inspection.