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Lie group closed-form formulation of non-redundant dynamic model for generalized momentum based force estimation in branched articulated robots
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DOI：10.1016/j.mechatronics.2026.103575.png)
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Abstract
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Accurate dynamic modelling and efficient force estimation are essential for advanced control of branched articulated robots. However, conventional identification methods often suffer from parameter redundancy and poor scalability to complex topologies, limiting their accuracy and computational efficiency. This paper presents a unified Lie group-based framework for non-redundant dynamic modelling and generalized momentum-based force estimation in branched articulated robots. By introducing motion decomposition and generalized inertia mapping, the proposed approach systematically eliminates parameter redundancy and constructs a non-singular dynamic model for branched articulated robots with revolute and prismatic joints. Analytical closed-form expressions for independent wrenches and dynamic derivatives are derived using Lie group operators, enabling efficient parameter identification and noise-resilient force estimation. Two generalized momentum observers are developed: one leveraging the analytical derivative of the mass matrix, and another employing a modified Coriolis matrix to ensure skew-symmetry, both avoiding the need for acceleration measurements. Simulation and experimental results on a 13-DoF dual-arm robot and a 6-DoF industrial manipulator demonstrate that the proposed method achieves improved computational efficiency and lower force estimation errors compared with conventional approaches. The framework facilitates accurate, efficient, and generalizable dynamic identification and sensorless force estimation for complex robotic systems, supporting advanced compliant control and physical interaction.
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