actuator integration

To address the challenge of simultaneously ensuring safety and motion performance in series elastic actuators (SEAs) during human–robot physical interaction, this paper proposes a novel co-design architecture integrating end-effector PD control with elastic transmission damping—thereby overcoming conventional passivity- and stability-imposed gain limitations inherent in load-side control. Leveraging linear systems theory, we analytically derive stability boundaries across diverse mechanical configurations. Both simulation and hardware experiments demonstrate that the approach achieves high-precision trajectory tracking even under low-stiffness hardware conditions, while actively dissipating energy upon collision to ensure user safety. The core contribution is a unified “control–damping–configuration” coupled design paradigm, which significantly expands the feasible domain for stable, high-gain control. This work provides both theoretical foundations and practical implementation guidelines for safe, high-performance SEA control.

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.

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.

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.

Traditional robot design and control are typically decoupled, leading to morphologies poorly aligned with task requirements. This paper proposes a simulation-driven co-optimization framework for morphology and control, breaking the conventional “design-then-control” paradigm to enable task-oriented, end-to-end joint search. Our method employs gradient-free optimization to simultaneously evolve structural parameters and controller policies within a URDF-based multi-task reinforcement learning simulation environment. Key contributions include: (1) demonstrating that controller retraining significantly improves performance, yielding an average gain of 37%; and (2) revealing an inverse correlation between morphological complexity and controller training budget—providing theoretical justification for structural simplification under resource constraints. We validate the framework across four public simulation benchmarks, showing that co-optimization consistently yields more compact, robust, and task-adapted robot morphologies compared to sequential approaches.

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 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 of achieving both lightweight design and fault-tolerant reliability in space robotic arms under stringent mass constraints. The authors propose an innovative architecture based on time-division multiplexed actuation (TDMA), integrating a vertically stacked rotary gating mechanism with self-rotating TDM motors, electromagnetic clutches, worm-gear reducers, and a dual-encoder system. This integration significantly reduces the number of actuators while enabling sub-0.1-second clutch response, inherent self-locking capability, and high-precision positioning. A complementary trajectory planning algorithm ensures fault-tolerant control even under partial servo failure. The resulting MuxArm prototype weighs only 2.17 kg, can manipulate a 10 kg payload, achieves end-effector positioning accuracy within 1% of arm length, and reduces tendon loading by 50%.

This work addresses the limitation of existing aerial robots, whose positioning accuracy in interactive tasks is typically confined to the centimeter scale, by proposing a high-precision airborne marking method tailored for surfaces such as ceilings. The approach employs an intelligent end-effector that integrates compliance, multi-point contact, active actuation, and self-contained functionality, structurally realized through a stability-optimized Gough-Stewart parallel mechanism. This design enables millimeter-level aerial line drawing without requiring complex system modeling or high-order control strategies. Experimental results demonstrate that the method reliably achieves sub-centimeter marking accuracy even in the absence of a precise environmental model, substantially enhancing the fine manipulation capabilities of aerial robots.

This study addresses the challenge of simultaneously achieving component alignment, system coordination, solution reliability, and computational efficiency in physically interacting interconnected systems within three-dimensional space. To this end, the authors propose a decomposition-based collaborative optimization framework that, for the first time, embeds port-alignment constraints into the SPI² architecture. Treating component positions as design variables, the method employs a penalty function to enforce system-level feasibility and enables automatic generation of initial designs. By integrating gradient-based optimization for enhanced numerical stability and coupling it with NSGA-II for efficient multi-objective search, the approach achieves high-quality coordinated solutions. Demonstrated on automotive powertrain and battery-chassis integration cases, the framework significantly outperforms discrete exhaustive search, delivering superior system-level coordination while substantially reducing computational cost.