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Byline: Darwin G. Caldwell, Ping Yong Chua, Mohd Zaid Amran The human hand with its complex anatomical and sensory structures forms an exceptional end-effector that is capable of performing countless grasping, exploratory, manipulative and prehensile tasks. Key to the effective operation of this 'hand', in terms of strength, compliance, compactness, speed of finger motions and the safe, secure handling needed to minimise potential damage to the grasped object and the hand itself, is the actuation. This paper describes the design, construction and testing of a 23 DOF robotic dextrous end-effector powered by powerful, yet compliant and accurate braided pneumatic Muscle Actuator (pMA)s. The paper discusses the control and drive systems and shows the hand performing a number of complex manipulative tasks involving dexterity and power and concludes by demonstrating that the hand can perform high speed tele-operated catching operations.

Natural rubber (NR) latex is sensitive to mechanical influences which can occur at almost every stage of its manufacturing process. Moreover its mechanical stability can also change during storage or be modified with the addition of suitable soaps such as oleates, laureates, and stearates [1]. Hence the Mechanical Stability Test (MST) is of vital importance to the rubber industry as it gives an indication of the quality of the latex. Currently the assessment is performed manually by trained laboratory technicians, following the procedures as defined by the ISO 35 standard mechanical stability test. However, the test is highly dependent on the human visual capability and the experience of the laboratory technician performing the test, potentially leading to either inconsistent or inaccurate results. In this paper, we proposed a computer vision-based mechanical stability classification system to minimise the potential for biasness in the current standard test. We investigated this with a novel feature descriptor – Histogram of Size Distribution (HSD) that is based on the coagulum count and size. Experimental results demonstrated that the proposed system was able to provide essentially good classification accuracies on the data tested. [ABSTRACT FROM AUTHOR]