Requirements for artificial muscles to design robotic fingers

International audienceThis work is part of the ProMain project that concerns the modeling and the design of a soft robotic hand prosthesis, actuated by artificial muscles and controlled with surface Electromyography (EMG) signals. In a first stage, we designed a robotic finger based on the equivalent mechanical model of the human finger. The model takes into account three phalangeal joints, flexion and extension movements are studied. The robotic finger has three Degrees of Freedom (DoF). The finger is designed to be under-actuated and driven by tendons, i.e. only one servo motor actu-ates the whole finger, and the motor is coupled to the finger mechanism through two flexible wires. As the aim is to design a robotic hand prosthesis that uses artificial muscles, we propose and carry out two experiments to characterize the specifications of the actuator. The first experiment measures the pinch force of the human finger, and the second measures the achieved force using our robotic finger and five different servo motors. It allows us to enhance experimental results with the mathematical model of the finger, to identify the requirements of the artificial muscle

On Addressing the Challenges of Complex Stochastic Games Using “Representative” Moves

The problem of achieving competitive game play in a board game, against an intelligent opponent, is a well-known and studied field of Artificial Intelligence (AI). This area of research has seen major breakthroughs in recent years, particularly in the game of Go. However, popular hobby board games, and particularly Trading Card Games, have unique qualities that make them very challenging to existing game playing techniques, partly due to enormous branching factors. This remains a largely unexamined domain and is the arena we operate in. To attempt to tackle some of these daunting requirements, we introduce the novel concept of “Representative” Moves (RMs). Rather than examine the complete list of available moves at a given node, we rather propose the strategy of considering only a subset of moves that are determined to be representative of the player’s strategic options. We demonstrate that in the context of a simplified Trading Card Game, the use of RMs leads to a greatly improved search speed and an extremely limited branching factor. This permits the AI player to play more intelligently than the same algorithm that does not employ them

Elbow flexion and extension identification using surface electromyography signals

In this paper, a new approach is presented for the analysis and the identification of the surface electromyography (EMG) signals of biceps and triceps muscles. The objective of this study is the accurate classification of elbow flexion and extension movements. We propose a cropping method based on the agreement of the movement changes and the EMG signal using the upper limb kinematic. Then, we perform the extraction and selection of several well known features in time and frequency domain. The selected features are used as inputs for our support vector machine classifier that is designed using an optimal weight vector criterion. Afterward, the training and test steps are performed in the proposed scheme. Finally, numerical simulation assesses the accuracy of the classification, as well as the robustness of the proposed approach considering noisy measurements