The future where the industrial shop-floors witness humans and robots working in unison and the domestic households becoming a shared space for both these agents is not very far. The scientific community has been accelerating towards that future by extending their research efforts in human-robot interaction towards human-robot collaboration. It is possible that the anthropomorphic nature of the humanoid robots could deem the most suitable for such collaborations in semi-structured, human-centered environments. Wearable sensing technologies for human agents and efficient human-aware control strategies for the humanoid robot will be key in achieving a seamless human-humanoid collaboration. This is where reliable state estimation strategies become crucial in making sense of the information coming from multiple distributed sensors attached to the human and those on the robot to augment the feedback controllers designed for the humanoid robot to aid their human counterparts. In this context, this thesis investigates the theory of Lie groups for designing state estimation techniques aimed towards humanoid locomotion and human motion estimation. The abstract nature of Lie theory provides a unified approach to handle the three-dimensional machinery and the complex geometry required for modeling free-floating, highly articulated multi-body systems. It enables suitably appropriate methods to perform rigorous calculus over complex nonlinear spaces and to handle the notion of uncertainties in such spaces, which are important for an estimator design. Methods for loosely-coupled and tightly-coupled sensor fusion for floating base estimation of a humanoid robot are presented through the theory of averaging and filtering on Lie groups. The problem of human motion estimation through wearable sensing technologies is tackled through a combination of dynamical systems' theory-based Inverse Kinematics and filtering on Lie groups, demonstrated to be directly applicable also for humanoid state estimation. Experimental validations of the estimators for humanoid base estimation and human motion estimation have been carried out on simulated datasets and datasets collected from real-world experiments conducted on the iCub humanoid robot and Xsens Motion capture technology, respectively.

State Estimation for Human Motion and Humanoid Locomotion

RAMADOSS, PRASHANTH
2022-05-30

Abstract

The future where the industrial shop-floors witness humans and robots working in unison and the domestic households becoming a shared space for both these agents is not very far. The scientific community has been accelerating towards that future by extending their research efforts in human-robot interaction towards human-robot collaboration. It is possible that the anthropomorphic nature of the humanoid robots could deem the most suitable for such collaborations in semi-structured, human-centered environments. Wearable sensing technologies for human agents and efficient human-aware control strategies for the humanoid robot will be key in achieving a seamless human-humanoid collaboration. This is where reliable state estimation strategies become crucial in making sense of the information coming from multiple distributed sensors attached to the human and those on the robot to augment the feedback controllers designed for the humanoid robot to aid their human counterparts. In this context, this thesis investigates the theory of Lie groups for designing state estimation techniques aimed towards humanoid locomotion and human motion estimation. The abstract nature of Lie theory provides a unified approach to handle the three-dimensional machinery and the complex geometry required for modeling free-floating, highly articulated multi-body systems. It enables suitably appropriate methods to perform rigorous calculus over complex nonlinear spaces and to handle the notion of uncertainties in such spaces, which are important for an estimator design. Methods for loosely-coupled and tightly-coupled sensor fusion for floating base estimation of a humanoid robot are presented through the theory of averaging and filtering on Lie groups. The problem of human motion estimation through wearable sensing technologies is tackled through a combination of dynamical systems' theory-based Inverse Kinematics and filtering on Lie groups, demonstrated to be directly applicable also for humanoid state estimation. Experimental validations of the estimators for humanoid base estimation and human motion estimation have been carried out on simulated datasets and datasets collected from real-world experiments conducted on the iCub humanoid robot and Xsens Motion capture technology, respectively.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1083139
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