transmission design

This work addresses the limitations of existing co-design approaches for legged robots, which often neglect actuator parameter optimization and are confined to open-chain architectures, thereby hindering high-performance jumping. The paper proposes a co-design framework tailored for a planar closed-chain five-bar monopedal robot, uniquely incorporating detailed actuator specifications—such as motor and gearbox mass, efficiency, and peak torque—into whole-system optimization. The framework jointly optimizes mechanical structure, actuator selection, and control policy through a two-stage methodology: first establishing a mapping between gear ratios and actuator performance, then applying the CMA-ES algorithm for global co-optimization. Simulation results demonstrate that the optimized design achieves a 42% increase in jump distance and a 15.8% reduction in mechanical energy consumption compared to a baseline configuration.

This study addresses the challenge that existing bipedal robots fail to replicate the critical role of human toes in agility, energy efficiency, and impact absorption, and lack quantitative validation of active toe benefits. To bridge this gap, the authors develop a 14-degree-of-freedom anthropomorphic bipedal robot platform featuring a lightweight, high-torque, and robust active toe mechanism. Using a unified reinforcement learning training pipeline in a high-fidelity simulation environment, they present the first quantitative assessment of active toes’ comprehensive benefits under identical conditions. Experimental results demonstrate that, at a walking speed of 1.33 m/s, the active toe configuration reduces the cost of transport (CoT) by 17.5%, decreases heel-strike ground reaction forces by 5.0%, and lowers both the average and peak path deviations by 25.0% and 34.0%, respectively, compared to a no-toe configuration.

Traditional robotic design suffers from a long-standing decoupling between structural synthesis and behavioral optimization; existing co-design methods—largely restricted to serial or tree-topology models—fail to capture the coupled dynamics inherent in parallel mechanisms. Method: This paper proposes a drive-space co-design framework for parallel-driven manipulators, explicitly embedding parallel coupling constraints into the dynamic model and jointly optimizing transmission ratios and motion trajectories within the actuator space. A bilevel optimization architecture is adopted: structural parameters are optimized in the outer loop, while trajectory planning is performed in the inner loop using an accurate parallel dynamic model. Contribution/Results: Experiments demonstrate that the method significantly enhances the manipulator’s dynamic load capacity, overcomes performance bottlenecks of conventional tree-structured co-design, and achieves, for the first time, integrated structural-control optimization under parallel kinematic constraints.

To address the high complexity and mass associated with conventional robotic variable-transmission systems—typically reliant on auxiliary actuators—this paper proposes a load-based passive variable-transmission (LBVT) mechanism. The LBVT integrates preloaded springs with a four-bar linkage, enabling purely mechanical, load-responsive adaptation of the gear ratio: it automatically increases the transmission ratio when joint load exceeds 18 N, thereby enhancing torque output. Crucially, it operates without sensors or active control, departing from the traditional closed-loop control paradigm for variable transmission. Theoretical modeling and parametric simulation demonstrate that the transmission ratio can increase by up to 40%, significantly improving actuation efficiency and robustness under dynamic loading conditions—such as those encountered in legged robots—while simultaneously reducing system complexity and mass.

Addressing the structural design challenge of multi-segment continuum robots caused by curvature coupling, this paper proposes a joint topology and sizing optimization method with workspace reachability as a hard constraint. The approach innovatively integrates numerical reachability analysis, torque-aware inverse kinematics modeling, and the Estimation of Distribution Algorithm (EDA), enabling minimization of joint torques under forward/inverse kinematic consistency constraints. Compared to conventional genetic algorithms, EDA improves the composite performance—measuring both robot length and actuation energy consumption—by 4–15% across three representative tasks, significantly enhancing solution quality and convergence efficiency. To the best of our knowledge, this work is the first to synergistically combine reachability analysis, torque-aware kinematics, and EDA for continuum robot structural optimization. It establishes a novel paradigm for autonomous configuration design that simultaneously achieves high workspace reachability and low energy consumption.

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.

Series elastic actuators (SEAs) in parallel kinematic manipulators (PKMs) incur high energy consumption during repetitive pick-and-place tasks. Method: This paper proposes a joint optimization framework for actuator stiffness and motion trajectories, treating stiffness as a design parameter—not a real-time tunable variable—within a dynamic modeling and optimal control framework. Leveraging the cyclic nature of the task and an energy-minimization objective, stiffness configuration and joint trajectories are co-optimized while respecting redundancy-driven actuation constraints. Contribution/Results: The approach innovatively exploits the inherent elastic oscillation of SEAs for passive energy recovery, eliminating the need for complex variable-stiffness hardware. Validated on two representative PKM platforms, experimental results demonstrate significant energy reduction, confirming the effectiveness, generalizability, and advantages of this lightweight, low-cost, low-power design paradigm.

Existing monopod hopping robot co-design approaches predominantly optimize for a single objective—such as maximum jump height or minimum energy consumption—neglecting the critical trade-off between them. Moreover, they often rely on oversimplified actuator models and omit gearbox parameter optimization, severely limiting design reproducibility and performance fidelity. Method: This paper proposes a novel three-stage co-optimization framework that, for the first time, integrates a high-fidelity motor mass model and explicit gearbox parameter optimization into the co-design process. It simultaneously optimizes mechanical architecture (including geometry and transmission) and control policy, while automatically generating manufacturable, parametric CAD models. The method synergistically combines multi-objective optimization, high-fidelity dynamic simulation, and parametric geometric modeling. Contribution/Results: Experimental evaluation demonstrates a 50% reduction in mechanical energy consumption compared to baseline designs, while reliably achieving a stable 0.8 m jump height—significantly improving both design feasibility and balanced performance across competing objectives.

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 study addresses the challenges of conventional independently actuated joints in lower-limb exoskeletons—namely, their mechanical complexity, excessive weight, and reliance on torque sensors—by proposing a cable-driven differential architecture tailored for hip–knee flexion–extension movements. The design employs two motors coupled with a linear differential mapping to enable coordinated torque distribution across joints. Combined with a model-based friction compensation strategy, this approach achieves, for the first time in a differential actuation module, high-precision joint torque estimation without the need for physical torque sensors. Experimental validation demonstrates that the proposed method substantially reduces system complexity and mass, offering an effective sensorless torque control solution for lightweight exoskeletons.