Revolutionary progress in kinematically redundant parallel mechanisms: a review on configurable platform innovations

Parallel mechanisms (PMs) have long been the backbone of high-precision industries, valued for their exceptional stiffness, load-carrying capacity, and accuracy in tasks ranging from pick-and-place automation to flight simulators and robotic machine tools. However, their rigid end-effectors have posed a critical limitation: they struggle to adapt to multi-point interactions, irregularly shaped objects, or dynamic environments that demand flexibility. This gap has driven the development of Kinematically Redundant Parallel Mechanisms with Configurable Platforms (PMCPs)—a game-changing innovation that replaces rigid end-effectors with configurable kinematic chains capable of adjusting shape, contact points, and operational modes.

PMCPs merge the best of both worlds: the structural rigidity and precision of traditional PMs with the adaptability of serial manipulators. By integrating open-loop or closed-loop configurable platforms, these mechanisms can perform multiple tasks with a single system, eliminating the need for specialized robots and reducing operational costs. For instance, a single PMCP can switch from handling small, delicate components in microassembly to grasping large, irregular objects in manufacturing, all through platform reconfiguration.

The review categorizes PMCPs into three key groups to clarify their design and application potential. First, by motion type: planar PMCPs operate in 2D spaces, ideal for precision assembly and planar automation, while spatial PMCPs function in 3D, enabling advanced tasks like robotic surgery and aerospace simulations. Second, by degree of freedom (DOF): low-DOF PMCPs (1-5 DOF) excel at specialized tasks such as micro-manipulation and precision positioning, while high-DOF PMCPs (6+ DOF) offer dexterity for humanoid robotics, complex assembly, and multi-axis motion. Third, by platform structure: open-loop platforms feature serial chains for simplicity and flexibility, suitable for light-duty pinching tasks, while closed-loop platforms provide higher stiffness and load-bearing capacity, enabling inside pinching, clasping, and precision applications like haptic feedback devices.

Key technical breakthroughs highlighted in the study address the unique complexity of PMCPs. Mobility analysis, a foundational challenge, leverages advanced methods including screw theory (modeling motions and constraints as six-dimensional vectors), graph theory (visualizing structural connections), and Grassmann–Cayley algebra (handling redundant constraints). These tools enable accurate calculation of DOF even for over-constrained or reconfigurable systems. Additionally, PMCPs’ reconfiguration capabilities—achieved through constraint adjustment or coupling-based design—allow them to switch operational modes, expand workspace, and optimize performance for specific tasks. Singularity avoidance, a critical issue in traditional PMs that causes loss of control, is mitigated through kinematic redundancy, enhancing reliability in dynamic operations.

Across industries, PMCPs are delivering tangible value. In robotic grippers, they enable adaptive grasping of irregular objects, from the 1-DOF compliant gripper for micro-manipulation to the 9-DOF parallel robot with integrated grasping capabilities. Haptic devices benefit from their precision, with examples like the 7-DOF QuadroG robot and Masterfinger-2 delivering realistic force feedback for virtual reality and teleoperation. Origami-inspired PMCPs offer compact, deployable solutions for minimally invasive surgery and terrain-adaptive robots. In micro-robotics, the MiGriBot achieves 1μm accuracy, revolutionizing microassembly and biomedical engineering. Even aerospace benefits, as PMCP-based landing gear adapts to unstructured terrains, improving aircraft and spacecraft stability.

Despite these advancements, critical challenges remain. Systematic type synthesis lacks unified methodologies to handle component coupling, while motion description and dynamic control are complicated by internal DOF and variable topologies. The authors outline future directions: establishing task-aligned design frameworks, developing real-time motion planning and singularity avoidance algorithms, and integrating AI to predict mobility and optimize performance. As these challenges are addressed, PMCPs are poised to redefine adaptive robotics, driving innovation in industries demanding precision, flexibility, and multifunctionality.

About Fudan University

Fudan University is a leading research institution in China, renowned for its excellence in science, engineering, and interdisciplinary innovation. The Institute of AI and Robotics, within the College of Intelligent Robotics and Advanced Manufacturing, is dedicated to advancing cutting-edge robotic technologies and their real-world applications.

Website: https://www.fudan.edu.cn/

About The Hong Kong Polytechnic University

The Hong Kong Polytechnic University (PolyU) is a world-class institution with strong expertise in engineering, manufacturing, and robotics. Its Department of Mechanical Engineering leads research in advanced mechanical systems, supported by specialized research institutes including the Research Institute for Artificial Intelligence of Things and the Research Institute for Advanced Manufacturing.

Website: https://www.polyu.edu.hk/en/

About Chunxu Tian from Fudan University

Chunxu Tian is an Research Fellow at Fudan University. His research interests include advanced robotics, with a focus on Parallel Robots, Dexterous Hands, Bionic UAV and Origami Robots. Tian's innovative research addresses critical challenges in robotics, particularly in overcoming the limitations of traditional parallel robots. His work resolves key conflicts and incompatibilities between various performance aspects, pushing the boundaries from conventional “weak coupling” designs to “strong coupling,” and from “fixed configuration” systems to more adaptive “variable configuration” models. These contributions are driving the development of more versatile and capable parallel robotic systems.

About DanZhang from The Hong Kong Polytechnic University

Professor Dan Zhang is a well-accomplished educator and an internationally renowned expert in the areas of parallel robotic machines and their applications in manufacturing systems. His influential scientific contributions have led to novel robotic system designs and the development of new comprehensive models for a better understanding of globe stiffness and robotic calibrations. His research applications have tackled some of the world's most challenging problems in high dynamic performance manufacturing robotic systems. His innovative calibration method has revolutionised the calibration of robotic systems by providing a rapid and precise means to account for the cumulative effect of various error sources.