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While solving inverse kinematics on serial kinematic chains is well researched, many methods still seem rather limited in jointly handling more complex geometries, including dexterous multi-finger hands or humanoid robots. In particular, object manipulation and motion tasks would benefit from the ability to define intermediate goals along the kinematic chains, such as an elbow position or wrist orientation...
Solving kinematic motion is a challenging field of research which is relevant for various applications in character animation and robotics. This paper presents a novel and fast hybrid evolutionary algorithm for inverse kinematics which can handle fully constrained and highly articulated geometries with multiple end effectors and individual objectives. Several experiments on the 42 DoF human body mannequin...
This paper presents a novel biologically-inspired approach to solving the inverse kinematics problem efficiently on arbitrary joint chains. It provides high accuracy, convincing success rates and is capable of finding suitable solutions for full pose objectives in real-time while incorporating joint constraints. The algorithm tackles the problem by evolutionary optimization and merges the benefits...
In this paper, we present an efficient and adaptive method for detecting and tracking multiple persons while providing real-time capability and high robustness to outlier noise. Given an RGB-D image data sequence, our algorithm combines two independent approaches for person detection. First, a cluster-based segmentation and classification on RGB-D point clouds and second a face detection on RGB images,...
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