In this paper, a tight coupling between computer vision and parallel robotics is exhibited through the projective line geometry. Indeed, contrary to the usual methodology where the robot is modeled independently from the control law that will be implemented, the proposed method takes into account, from the early modeling stage, the fact that vision will be used for control. Hence, kinematic modeling and projective geometry are fused into a control-devoted projective kinematic model. Thus, starting from a vision-based kinematic modeling of a Gough—Stewart manipulator, a visual servoing scheme is presented, where the image projection (edges) of the non-rigidly linked legs are servoed, rather than the end-effector pose or the leg directions.

As the national blood banking community considers limiting the use plasma from female donors because of a rare but potentially catastrophic lung condition, researchers have shown that this policy change might be premature. -more-

Researchers at Clarkson University, NY, have developed wireless bridge sensors which work without batteries. Instead, they are powered by the vibrations caused by passing traffic. This is good news for all the people in charge of maintaining bridges, who will no longer to have to replace batteries installed in hard-to-access locations. As said one of the researchers, ‘We have completely eliminated the battery from the equation. Hermetically sealed wireless sensors powered by bridge vibration can remain on the bridge without need of maintenance for decades, providing continuous monitoring of such parameters as ice conditions, traffic flows and health status.’ Nice job, but read more… read story

This paper presents a computer vision-based method for visually detecting the contact between an end-effector and a target surface under an optical microscope during microrobotic manipulation. Without using proximity or force/touch sensors, this method provides a submicrometer detection accuracy and possesses robustness. Fundamentally, after the establishment of contact in the world frame, further vertical motion of the end-effector (flexible or stiff) induces horizontal motion in the image plane. Contact between a micropipette tip and a glass slide in the scenario of microrobotic cell manipulation is used as an example to elaborate on the detection method. Experimental results demonstrate that the computer vision-based method is capable of achieving contact detection between the micropipette and the glass slide surface with an accuracy of 0.2 m. Furthermore, 1000 experimental trials reveal that the presented method is robust to variations in illumination intensity, microscopy magnification, and microrobot motion speed.

Chemists have built a new wire out of photosensitive materials that is hundreds of times smaller than a human hair. The wire not only carries electricity to be used in vanishingly small circuits, but generates power as well.

This paper proposes a framework that achieves the Learning from Observation paradigm for learning dance motions. The framework enables a humanoid robot to imitate dance motions captured from human demonstrations. This study especially focuses on leg motions to achieve a novel attempt in which a biped-type robot imitates not only upper body motions but also leg motions including steps. Body differences between the robot and the original dancer make the problem difficult because the differences prevent the robot from straightforwardly following the original motions and they also change dynamic body balance. We propose leg task models, which play a key role in solving the problem. Low-level tasks in leg motion are modelled so that they clearly provide essential information required for keeping dynamic stability and important motion characteristics. The models divide the problem of adapting motions into the problem of recognizing a sequence of the tasks and the problem of executing the task sequence. We have developed a method for recognizing the tasks from captured motion data and a method for generating the motions of the tasks that can be executed by existing robots including HRP-2. HRP-2 successfully performed the generated motions, which imitated a traditional folk dance performed by human dancers.

This paper proposes a framework that achieves the Learning from Observation paradigm for learning dance motions. The framework enables a humanoid robot to imitate dance motions captured from human demonstrations. This study especially focuses on leg motions to achieve a novel attempt in which a biped-type robot imitates not only upper body motions but also leg motions including steps. Body differences between the robot and the original dancer make the problem difficult because the differences prevent the robot from straightforwardly following the original motions and they also change dynamic body balance. We propose leg task models, which play a key role in solving the problem. Low-level tasks in leg motion are modelled so that they clearly provide essential information required for keeping dynamic stability and important motion characteristics. The models divide the problem of adapting motions into the problem of recognizing a sequence of the tasks and the problem of executing the task sequence. We have developed a method for recognizing the tasks from captured motion data and a method for generating the motions of the tasks that can be executed by existing robots including HRP-2. HRP-2 successfully performed the generated motions, which imitated a traditional folk dance performed by human dancers.

Scientists still do not know what causes epileptic seizures, but researchers are one step closer to solving this puzzle with the help of their newly developed genetically modified epileptic mouse. -more-

This paper presents a distributed control algorithm for multi-target surveillance by multiple robots. Robots equipped with sensors and communication devices discover and track as many evasive targets as possible in an open region. The algorithm utilizes information from sensors, communication, and a mechanism to predict the minimum time before a robot loses a target. Workload is shared locally between robots using a greedy assignment of targets. Across long distances robots cooperate through explicit communication. The approach is coined Behavioral Cooperative Multi-robot Observation of Multiple Moving Targets. A formal representation of the proposed algorithm as well as proofs of performance guarantee are provided. Extensive simulations confirm the theoretical results in practice.