Recently, head-pose estimation has attracted growing attention due to its inherent value in enhancing the performance of head-related applications. Neural networks often require the handling of various representations of the same rotation space. For instance, Euler angles and standard unit quaternion representations can be employed to represent three-dimensional (3D) rotations. However, these representations suffer from the discontinuity problem (DP). Instead of previous representations, we propose a novel end-to-end landmark-free method that employs a continuous quaternion representation (CQR) based on a symmetric matrix ( A ) for 3D rotations when training deep neural networks. This representation is named ContQuat, which satisfies the continuity property and allows the model to overcome the DP and ambiguity issues encountered in commonly used standard unit quaternion and Euler angle representations, enabling efficient full rotation learning. We also introduce three loss functions appropriate for the proposed method to encapsulate the training loss of the neural network during the optimization process. Furthermore, we present a comprehensive experimental comparison of the proposed method against state-of-the-art approaches on publicly available benchmark datasets. The experimental results and error analysis visualizations demonstrate that the proposed method either outperforms or is highly competitive with the current state-of-the-art techniques. The full code is available at: ( ContQuat ).

This paper presents a novel under-actuated twofinger gripper that passively adapts to various environments and maintains its grip posture using a passive-locking mechanism. The proposed mechanism features fingers with three phalanges, each incorporating four-bar and eight-bar linkages arranged in parallel. These linkages perform crucial functions, including maintaining the grip angle and ensuring passive characteristics during pinch grips. Previous grippers with passive mechanisms and three-phalanx fingers faced issues with gripping instability, particularly when changes in the passive joint angle were caused by object inertia or external lateral forces. To address this problem, we propose a new passive-locking mechanism utilizing an eight-bar linkage. This innovative design is engineered to adapt to environmental conditions, establish a secure grip, and maintain the grip angle of the passive joint after the grip is achieved. To demonstrate the advantages of the proposed mechanism, this paper conducts a fingertip force vector analysis and a mobility analysis according to the pinch sequence. It also details the derivation process and principles of the mechanism. The gripper's operational range and gripping force are examined through kinematic analysis and verified by simulation. Furthermore, the study shows that the proposed mechanism effectively responds to environmental constraints, even in environments with obstacles surrounding the object. Comparative experiments with and without a contact bar indicate that the proposed gripper can stably secure an object in scenarios involving swing motions and external forces of approximately$5 N$.

Head pose estimation is a fundamental function for several applications in human computer interactions. Accurate six degrees of freedom head pose estimation (6DoF-HPE) with full-range angles make up most of these applications, which require sequential images of the human head as input. Most existing head pose estimation methods focus on a three degrees of freedom (3DoF) frontal head, which restricts their applications in real-world scenarios. This study presents a framework designed to estimate a head pose without landmark localization. The novelty of our framework is to estimate the 6DoF head poses under full-range angles in real-time. The proposed framework leverages deep neural networks to detect human heads and predict their angles using single shot multibox detector (SSD) and RepVGG-b1g4 backbone, respectively. This work uses red, green, blue, and depth (RGB-D) data to estimate the rotational and translational components relative to the camera pose. The proposed framework employs a continuous representation to predict the angles and a multi-loss approach to update the loss functions for the training strategy. The regression function combines the geodesic loss with the mean squared error. The ground-truth labels were extracted from the public dataset Carnegie Mellon university (CMU) Panoptic for full head angles. This study provides a comprehensive comparison with state-of-the-art methods using public benchmark datasets. Experiments demonstrate that the proposed method achieves or outperforms state-of-the-art methods. The code and datasets are available at: (github after accepting).

Conventional dexterous grippers are not only heavy and complex but also task dependent. As a result, they cannot provide power, intermediate and precision grasping together. We propose usability evaluation of a novel gripper mechanism which can provide 1-Degree of Freedom (DOF) parallel grasping and 2-DOF independent pinching by using a closed chain architecture. The usability of our design and one commercial product has been evaluated based on various grasping tasks: adaptive grasping, parallel pinching grasping, independent pinching grasping and contact grasping. The success rate has been evaluated to validate the excellent usability of the proposed 3-DOF gripper.

In this study, a novel four degrees of freedom (DoF) robotic link mechanism and its torque analysis based on extended coaxial spherical joint module (E-CoSMo) are introduced. The E-CoSMo is a previously developed 4-DoF point-centered rotational parallel mechanism with coaxial actuators. The use of the E-CoSMo on robotic system is expected to improve dynamic performance since all actuators are mounted on fixed base. Furthermore, output torque is amplified in a specific axial direction, due to a coaxial parallel mechanism. Torque analysis of the mechanism is derived based on kinematics of a virtual 4-DoF serial linkages and the E-CoSMo. A virtual force-based position control simulation is performed for verifying the torque analysis. Due to the valid torque analysis, the norm of position error is less than 0. 7 mm. It is confirmed that the output torque is amplified compare to actuator torques, which is a special feature of the E-CoSMo. Control experiments using prototype are also carried out to validate the feasibility.

This paper deals with a path planning problem for turning motion of a car-like vehicle. We propose a turning method which finds a curvature continuous optimal path between two positions for a car-like vehicle.

This paper presents our microscope image stabilization system for in vivo microscopy. This work is a novel robotic application. In vivo microscopy is very in demand due to its potential impact on biological research [1]. However, it has turned out that in vivo microscopy is significantly disturbed by the motion of the imaged tissue of a living animal. The proposed system virtually removes the unwanted motion by synchronizing the motion of the objective lens with it. In order to realize this idea, we have developed a simple contact-type sensor for estimating the motion of organs, and also made a motion compensator for moving the objective lens. Sensing and compensating have been accomplished for 3-D translational motion. Not only laboratory tests with artificial motion, but also in vivo experiments show the successful motion canceling effect of the proposed system.

This paper presents image stabilization for microscopy using horizontal visual feedback control of the objective lens through a five-bar linkage and piezoelectric actuators, and its application to in vivo imaging. Even very small in vivo motion due to heartbeat and breathing makes microscopic observation difficult by blurring the microscope image or impossible by sending a region of interest out of view. In order to remove those unwanted effects of the motion, we have introduced motion-canceling robotic technologies into microscopy. Our image stabilization system through motion-canceling provides users with stabilized image sequences with respect to trembling of in vivo subjects. The developed image stabilization system, in term of robotics, corresponds to a visual feedback control system that consists of a robotic mechanism and a high-speed vision. A high-speed camera installed in the microscope detects the motion of the in vivo subject having topically applied fiducials. To virtually cancel this motion, we move the objective lens, synchronizing the motions of the subject and the lens to remove the relative motion between the two. As a result, we observe motion-free images to m. This technology is one of the very demanding technologies in biological research for in vivo observation with high resolution. In this paper, we verify the effectiveness of the developed system through in vivo experiments.

In this paper, an algorithm for hyper symmetric environment simultaneous localization and mapping (SLAM) is developed based on the generalized Voronoi graph. The hyper symmetric environment is a challenging environment for the SLAM and the most difficult problem on this is a data association. To redeem this problem, we propose three techniques. The first one is a breadth first node navigation algorithm which highly reduces the complexity of the data association. The second scheme is an odometry calibration method which converts untrustable odometry into reliable one. The third method is a multi layered data association scheme. We integrate above three techniques into one framework and propose a SLAM algorithm for the hyper symmetric environment. The simulation result shows that the proposed algorithm can successfully cover a large hyper symmetric environment under 20% odometry uncertainty.

Deals with the parking problem of a car-like mobile robot. The parking problem corresponds to the point-stabilization problem of nonholonomic systems. We propose a simple and efficient algorithm for the problem with stability analysis. Using this algorithm, a car-like mobile robot could be controlled to move to a desired posture within a prescribed boundary. The performance is verified through simulations and experiments using a car-like mobile robot.

Deals with the posture stabilization problem of car-like mobile robots. For a long distance, path-planning should be done first. But, over a short distance like a parking maneuver, path planning is difficult since car-like mobile robots, different from the conventional differential drive mobile robot, have a limit on turning radius. The derivation of a control law is also difficult in the sense that there exists no smooth feedback control for this problem. We propose a simple and efficient algorithm for the problem with stability analysis. Using this algorithm, we can control a car-like mobile robot to move to a desired posture within a prescribed boundary. The performance is verified through simulations and experiments using a car-like mobile robot.