hybrid linkage mechanism design

This work proposes a humanoid dexterous hand design inspired by human hand motion synergies, aiming to balance mechanical simplicity with anthropomorphic dexterity. By integrating biomechanical synergy principles with linkage-based actuation, the design employs a synergy-driven degree-of-freedom reduction strategy and introduces a novel spherical four-bar mechanism to decouple flexion/extension from abduction/adduction at the metacarpophalangeal joints, thereby reproducing natural finger trajectories within a compact structure. The resulting prototype utilizes only 11 actuators to control 19 joints, weighs 520 grams, and costs approximately \$400, demonstrating exceptional human-like motion capabilities, high payload capacity, and versatile grasping and manipulation skills.

This work addresses the longstanding challenge of balancing dexterity, compactness, and low cost in robotic hands by proposing a humanoid hand design based on a hybrid planar–spatial linkage mechanism. Integrated within the volume of a human palm, the prototype incorporates 16 actuators, 20 joints, and all sensing and transmission components, achieving a total mass of 320 grams and a material cost under $400. The linkage mechanism decouples multi-directional motions, enables biomimetic joint coordination, and provides high passive load-bearing capacity, with the thumb replicating human-like opposition and reconfiguration capabilities. The hand attains the highest Kapandji score reported to date and fully reproduces all 33 Feix grasp types, demonstrating stable and dexterous manipulation across a wide range of everyday objects.

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.

This work addresses the inverse design problem of planar linkage mechanism synthesis by reformulating the trajectory–structure mapping as a cross-domain image generation task. Methodologically, we construct an RGB-image-based dataset of mechanism–trajectory pairs and propose a shared-latent-space variational autoencoder framework. Crucially, we introduce a novel color-gradient encoding scheme to embed trajectory velocity information, enabling joint conditional control over both geometric shape and kinematic (velocity) properties. The approach supports end-to-end inverse design of diverse mechanisms—including revolute-joint linkages, sliding mechanisms, and multi-loop topologies with cams, gears, and prismatic joints. Experiments on four-bar, slider-crank, and multi-loop mechanism datasets demonstrate high-fidelity synthesis of feasible mechanism configurations for unseen trajectory curves, validating the effectiveness and generalization capability of image-driven mechanical innovation 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 of motion coupling and independent actuation in four-degree-of-freedom robotic finger joints by proposing a joint-specific hybrid remote actuation architecture. The design integrates rigid linkages with closed-loop tendon transmission mechanisms and incorporates rolling contact joints to maintain constant tendon length, thereby achieving complete mechanical decoupling between the proximal and distal interphalangeal joints along with independent control of each joint. Experimental results validate the effectiveness of this decoupling strategy, demonstrating peak fingertip forces of 21.28 N, 9.22 N, and 5.75 N across the respective joints. Furthermore, the proposed finger successfully grasps objects of diverse geometric shapes. This work provides a reliable mechanism design solution for fine manipulation tasks in dexterous robotic hands.

本文提出了一种具有2自由度MCP关节的紧凑型机器人手指，通过嵌入被动连续可变传动实现宽广的力量-速度操作范围。

研究通过基于浮动参考框架的柔性多体建模方法，解决了过约束空间连杆在工业应用中的装配精度问题，并验证了其自组装倾向。

This study addresses the inherent trade-off between joint torque and arm thickness in robotic manipulators by proposing a multi-objective optimization framework based on the NSGA-II algorithm. Through the co-optimization of tendon routing, pulley configurations, and attachment points, along with the introduction of a strategic shortcut mechanism to extend effective moment arms, the proposed method simultaneously maximizes torque output while minimizing structural thickness. The research reveals non-trivial design patterns that transcend conventional intuition and yields a Pareto-optimal solution set. These findings provide systematic engineering guidance for the design of compact, high-torque robotic arms, demonstrating that counterintuitive mechanical configurations can effectively reconcile competing performance objectives in manipulator design.

This study addresses the challenge of dynamic modeling for mechanisms with variable topology, particularly when constraints such as joint locking, static friction, or ideal contact lead to abrupt changes in degrees of freedom. To ensure physically consistent and continuous dynamic behavior during topological transitions, the work proposes a set of physically coherent switching conditions. Building upon this foundation, two nonsmooth dynamics frameworks are developed: one based on redundant coordinates employing projected equations of motion, and another using minimal coordinates formulated via Voronets equations. The computational characteristics of both approaches are systematically compared. The proposed methodology is successfully validated on a planar 3R mechanism and a 6-DOF industrial manipulator under joint-locking scenarios, significantly enhancing the accuracy and feasibility of forward dynamics simulations for complex systems with varying topology.