Ye Dai

Introduction/Objective: High-speed motorized-spindle vibration depends jointly on rotational speed, number of flutes, toolholder diameter, and measurement location. This study combines orthogonal analysis with a Bayesian-Optimized Random Forest (BO-RF) to develop a tool adaptation strategy across speed zones. Methods: An L16(45) orthogonal-array experiment used analyzer-integrated peak displacement as the response. A rotor–bearing–toolholder model generated 504 samples covering 42 speeds and 12 combinations of flute number and toolholder diameter. Bayesian optimization tuned random forest hyperparameters using four-fold cross-validation grouped by spindle speed. Out-of-fold predictions from the four fold-specific models ranked all candidates at each speed, and a continuity rule formed five adaptation zones. Results: Speed had the largest range of level means (0.01825 mm) and accounted for 55.6% of the fitted main-effect variation. BO-RF achieved a root-mean-square error of 1.246 × 10⁻³ and a coefficient of determination of 0.9625. The recommendations were 8-flute–φ30 mm at 1,000–5,000 r/min; 6-flute–φ30 mm at 6,000–14,000 r/min; 4-flute–φ25 mm at 15,000–20,000 r/min; 6-flute–φ20 mm at 21,000–30,000 r/min; and 4-flute–φ20 mm at 31,000–42,000 r/min. Relative to the fixed 6-flute–φ25 mm baseline, zonal improvements ranged from 14.10% to 24.95%, and the mean predicted index across all speeds decreased by 20.10%. Under representative 6-flute conditions, the relative differences between simulated and experimental displacement responses averaged over the speed range were 2.41%–3.02%. Discussion: Speed dominated the measured main effects, while preferred specifications reflected stiffness, rotating mass, and excitation–modal proximity. Representative experiments supported the numerical sample source but did not validate every candidate or adaptation zone. Conclusion: The workflow supports quantitative selection of toolholder diameter and flute number across speed zones within the investigated spindle and operating range.

Advanced biomechanical models are required to support the operations of exoskeleton systems employed for patient rehabilitation. However, current models are unable to account adequately for the non-ideal attachment effects associated with these exoskeleton systems that cannot be fully represented by an ideal rigid-connection model. Therefore, these attachment effects represent sources of residual dynamics remaining unaccounted for by biomechanical models that may jeopardize safe operations. The present work addresses this issue by proposing a physics-informed machine learning approach driven by model–data fusion for identifying the dynamic parameters of coupled lower-limb–exoskeleton systems. First, a baseline coupled dynamic-system model is established with explicit physical meaning under the assumption that the lower limb and exoskeleton are rigidly connected, and the model is linearly parameterized. Then, a data-driven residual compensation method based on a backpropagation network is introduced to capture un-modeled dynamics induced by non-ideal system interfaces. The results of experiments conducted using an anthropomorphic lower-limb dummy coupled with a rehabilitation exoskeleton demonstrate that the proposed residual compensation framework substantially improves joint-torque prediction over that of the physics-based model alone, while preserving model interpretability. The results of momentum-observer-based collision detection experiments further demonstrate that the proposed approach can support model-dependent safety tasks in wearable exoskeleton systems.

In this paper, a cellular robot for space trusses is structured so that it can perform tasks such as moving the truss and assembling the truss. There may be some spatial operating mechanisms on the space truss that cause obstacles to the robot’s movement, especially other mobile mechanical devices that are working, which are dynamic obstacles, so a suitable path planning for the robot is needed. In path planning, A-star algorithm has the advantages of efficient searching speed and good optimization effect, but it can’t deal with the path planning problem with dynamic obstacles, so this paper improves Lifelong Planning A-star (LPA-star) algorithm so that the improved algorithm satisfies the dynamic path planning task. Then a three-dimensional truss mathematical model is established, a dynamic obstacle environment is set up, the improved LPA-star algorithm is used for path planning, and the unimproved LPA-star algorithm and the improved A-star algorithm are used to compare with it. The simulation results show that in the environment set up in this paper, the optimal path length of the improved LPA-star algorithm is shortened by about 25% and the algorithm search time is shortened by about 55% compared with the improved A-star algorithm; while the unimproved LPA-star algorithm is unable to accomplish the dynamic path planning task. Therefore, the improved LPA-star algorithm can reduce the robot’s moving distance and time consumption.

With the progress of science and technology, the traditional robot workplace is fixed, single-function, and inflexible, and may not work properly in some special places, while the modular robot with self-reconfiguration function is a robot that can adapt to new environments and can rely on new task settings, which has a series of universal modules and relies on mutual communication between modules and autonomous reorganization movements to cope with changes in the environment or tasks and recover from the state of destruction. This paper summarizes the representative international research results from the perspective of the hardware design of robots in two aspects based on the design characteristics of self-reconfiguring modular robots around the reconfiguration strategy planning method. At the same time, some existing problems and shortcomings are pointed out on this basis to provide ideas as well as perspectives for future research development.