actuator selection

To address the lack of integrated gearbox parameter optimization and CAD modeling automation in planetary gear actuator design, this paper proposes the first computational framework jointly optimizing gearbox configuration, geometric parameters, and structural layout. The method integrates multi-objective optimization—minimizing mass and axial width while maximizing transmission efficiency—with parametric CAD modeling, enabling fully automated 3D modeling and 3D-printing-ready CAD generation for four planetary gearbox topologies: simple, compound, Woods, and double-simple planetary gearboxes (SSPG, CPG, WPG, DSPG). Its key contributions include systematically characterizing performance boundaries (efficiency, backlash, stiffness) across transmission ratios for each topology and establishing a standardized, motor- and ratio-aware CAD library. Experimental validation shows SSPG achieves 60–80% efficiency, 0.59° backlash, and 242.7 Nm/rad stiffness; CPG attains 60% efficiency, 2.6° backlash, and 201.6 Nm/rad stiffness.

In human–robot collaborative scenarios involving soft robots, motion components must simultaneously satisfy mechanical performance requirements and collision-free motion constraints. Method: This paper proposes the first multi-objective optimization framework that unifies structural compliance design and motion planning. It integrates gradient-enhanced topology optimization, nonlinear contact modeling, model predictive control (MPC), and real-time collision detection to jointly generate task-driven stiffness distributions and motion trajectories. Contribution/Results: The framework innovatively couples physical properties (e.g., stiffness/compliance) with kinematic constraints—including dynamic collision avoidance—at the optimization level, enabling online co-regulation of stiffness and trajectory. Experimental validation—spanning simulation and physical hardware—demonstrates a 62% reduction in collision impact force, a task success rate of 98.3%, and an end-to-end response latency under 50 ms.

To address the challenge of rapidly optimizing the stator slot fill factor (SFF) in electric vehicle traction motors, this paper proposes a mechanism-driven Bayesian optimization (BO) framework. The method explicitly incorporates electromagnetic physical constraints into the BO pipeline, integrating physics-informed modeling, an adaptive acquisition function, and multi-fidelity simulation to construct a high-fidelity, generalizable Gaussian process surrogate model. In permanent magnet synchronous motor (PMSM) design, the approach significantly enhances optimization reliability under limited data: it reduces the required number of iterations by 60% and improves optimization accuracy for key performance metrics—including efficiency and torque density—by 35%. This work establishes a new paradigm for efficient, interpretable, and physics-aware motor design.

Existing CAD systems suffer from a fundamental disconnect between feature-based parametric modeling and B-rep–based direct modeling, hindering cross-paradigm collaborative editing of geometry, topology, and parametric constraints. To address this, we propose a unified constraint graph model and a hybrid modeling kernel interface—enabling, for the first time, bidirectional, seamless integration of both paradigms. Our approach extends the constraint solver, introduces a topology-event–driven mapping mechanism, and designs parameter semantic extraction and incremental synchronization algorithms to support real-time, cross-mode collaboration. Evaluated on mainstream CAD platforms, our system achieves sub-80-ms editing latency, 99.2% constraint fidelity, and efficient handling of complex assemblies. This work breaks down longstanding paradigm barriers in CAD modeling and establishes a foundational architectural framework for next-generation intelligent CAD systems.

Robotic physical interaction suffers from coupled force–velocity control and a lack of unified experimental benchmarks. Method: This work introduces IC2D, an impedance control testbed featuring a novel electro-mechano-hydraulic compatible architecture—designed for high reliability and zero backlash—overcoming the limitation of conventional platforms that support only a single actuation modality. It enables hybrid electric/hydraulic actuation and combined linear/rotary motion. Through co-design across mechanical, electrical, and hydraulic domains; integration of multi-source high-precision sensing (force, position, velocity); real-time impedance/admittance control interfaces; and modular, reconfigurable kinematic structures, the platform enables quantitative assessment of joint-level interaction dynamics. Contribution/Results: IC2D establishes a state-of-the-art, multifunctional benchmarking system. It has successfully validated dynamic responses of diverse controllers and bandwidth characteristics of actuators, providing standardized experimental grounding for impedance control algorithm development and evaluation.

This work addresses the lack of reusable, cross-platform compliant control infrastructure in existing robotic software, which hinders unified algorithm implementation and high-level interfacing. The authors propose a robot-agnostic, modular compliance control framework that decouples controller infrastructure from control laws via a plugin architecture. It supports variable impedance control in both joint and Cartesian spaces and, for the first time, enables online adaptation of the primary compliance direction according to task geometry—overcoming the limitations of fixed coordinate frames. Built upon the ROS ecosystem, the framework leverages Pinocchio to parse URDF models for kinematic and dynamic computations and employs runtime plugin loading with generic wrappers to interface heterogeneous hardware. Real-world and simulated experiments demonstrate significant performance improvements in contact-intensive tasks and seamless transferability across multiple robotic arms.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

This work proposes a modular hybrid-actuation haptic interface architecture to address the challenge of efficiently and flexibly rendering large-scale force feedback in reconfigurable multi-degree-of-freedom systems. The design integrates electric motors and unidirectional brakes within a single compact module, transmitting forces via cables to deliver both smooth active output (up to 6 N) and high-magnitude transient collision feedback (up to 186 N). By enabling arbitrary configuration and supporting high-fidelity force feedback across multiple degrees of freedom, the system significantly expands the dynamic range of haptic rendering while maintaining a compact form factor. This approach facilitates versatile deployment scenarios without compromising the richness or realism of the tactile experience.

This study addresses the limited compliance of conventional rigid actuators in uncertain environments, which often results in poor force control accuracy and potential damage. To overcome this, the authors propose a low-cost series elastic module that imparts compliance to off-the-shelf black-box actuators, thereby enhancing force control performance. The module features a torsional elastic element optimized via finite element analysis and leverages Hooke’s law to enable high-fidelity force sensing. Experimental results demonstrate that the system’s force control bandwidth increases from 10.32 Hz to 30.32 Hz (a 2.93-fold improvement), while achieving 7.63% higher force control accuracy than commercial sensors. With a hardware cost of only £25, the solution offers a compelling balance between performance and affordability.

This work addresses the limitations of classical trajectory planning methods, which prioritize kinematic smoothness while neglecting dynamics and actuator control effort, often resulting in large tracking errors and high energy consumption. To overcome these issues, the authors propose a control-aware optimal trajectory planning framework that explicitly integrates the nonlinear dynamics of robotic manipulators and actuator effort over a finite time horizon. A midpoint linearization strategy is introduced to enhance the accuracy of dynamic approximations during large-range motions. By establishing a unified nonlinear closed-loop simulation environment, the study enables, for the first time, an isolated evaluation of trajectory generation methods under identical conditions. Experimental results on a simplified UR5 model demonstrate that the proposed approach significantly reduces tracking error, corrective torque, and overall closed-loop execution cost, achieving substantially lower energy consumption and total operational expense compared to conventional planners such as cubic, quintic, and trapezoidal profiles.