Abstract-This paper presents an analysis result of grasp-move-and-twist motions of robotic

Abstract-If the theory of affordance is applied to a robot, performing the whole process of recognition and planning is not always required in its computer. Since the tactile sensing of a robot is important to perform any task, we focus on tactile sensing and introduce a new concept called the artificial tactile affordance system (ATAS). Its basic idea is the implementation of a recurrent mechanism in which information obtained from the object and the behavior performed by the robot's inducing the next behavior. We intend to implement ATAS based on the following two methodologies: (1) after each rule is transformed into an algorithm, a program module is coded based on the algorithm; ATAS is composed of several program modules, and a module is selected from the set of modules based on sensor information; (2) a set of rules is expressed as a table composed of sensor input columns and behavior output columns, and the table rows correspond to rules; since each rule is transformed to a string of 0 and 1, we treat a long string composed of rule strings as a gene to obtain an optimum gene that adapts to its environment using a genetic algorithm (GA). For methodology 1, we established an ATAS composed of 3 to 5 modules to accomplish such tasks as object grasping, pick and place, cap screwing, and assembling. Using methodology 1, a two-hand-arm robot equipped with an optical threeaxis tactile sensor performed the above tasks. For methodology 2, we propose the Evolutionary Behavior Table System (EBTS) that uses a GA to acquire the autonomous cooperation behavior of multiple mobile robots. In validation experiments, three agents equipped with behavior tables conveyed an object to a specified goal with higher scores than the four-agent condition. Since the redundant agent does not interrupt the other agents, the agent acquires the collective behavior of not interrupting other agents based on its environment information. Methodology 1 is very effective for such fine control as handling tasks of humanoid robots, and methodology 2 is very useful to obtain general robotic behavior that is suitable for the environment.

This paper presents a new algorithm for object handling tasks based on active tactile and slippage sensations using a humanoid robot multi-fingered arm for an object that exists at an arbitrary position. The idea is to enhance real-time object handling tasks based on tactile sensing in humanoid robotics, where grasp, move and release motions are involved. We developed a novel hemisphere-shaped optical three-axis tactile sensor to mount on fingertips of the robot arm. The tactile sensor is capable of defining normal and shearing forces simultaneously. For grasp and release motions, we designed the algorithm based on slippage direction analysis consisting of coordinate transformation of the sensing element for the arm global coordinate. The robot control system uses the analysis results to determine whether an object is in contact with the ground without needing to measure the height of the ground. The algorithm was evaluated in experiments with soft and hard objects, whereby results revealed good performance for the robot fingers in handling an object at an arbitrary position.

Problem statement: Sensitization of robot hand is still remaining as crucial issue since most of robot hand systems nowadays are only capable to grasp a predefined specific object. It is still difficult for robot hand system to realize human-like tactile sensation. Some common problems in robot hand system are low accuracy sensing device, sensors are not robust enough for long time work and heavy duties, inconsistence tactile sensing detection and difficulties in control of sensing fusion with robot trajectory. These problems are apparently drawback the progress to commercializing robot hands as real consumer products. Approach: This study presented the application of optical three-axis tactile sensor to robot hand to improve sensitization quality in robotic hand system. The proposed tactile sensor system was designed with compliance modules to communicate with robot hand control system. The sensing principle used in this tactile sensor comparatively provides better sensing accuracy to detect contact phenomena from acquisition of three axial directions of forces. Methodology of force and slippage detection in the tactile sensor system was presented. Accordingly, the optimization of robot hand control algorithm to comply with the tactile sensor system was presented and verified in experiment of grasping and twisting. Results: The tactile sensor presented in this study is capable of detecting normal and shear force simultaneously. The proposed methodology was verified in experiment with paper cup and water, in which the result shows the robot control system managed to respond to the proposed object stiffness distinction parameters and effectively respond to sudden change of object weight during grasping. An experiment of grasping and twisting motions was conducted using a bottle cap. In order to perform simultaneous grasping and twisting tasks, optimization of the control algorithm was conducted with additional parameters to satisfy the desired tasks. Conclusion: Experimental result shows that the robot hand managed to perform grasping and twisting of bottle cap smoothly. The overall results revealed good performance of the proposed optical three-axis tactile sensor system and robot hand control algorithm for future application in a real artificial robot hand. In addition, slippage sensation measured in a robot control system could contribute a better maneuvering of the robot arm-finger system.

Problem statement: To advance the robust object recognition of robots, we present an algorithm for object exploration based on three-axis tactile data that is necessary and sufficient for the evaluation of contact phenomena. Approach: The object surface contour is acquired by controlling the finger position so that the normal force, measured by optical three-axis tactile sensors, remains constant as two fingertips slide along the object surface. In this algorithm, when the robot grasps an object, the tangential force increment is checked to judge the initial contact state because it is more sensitive than the normal force. After contact between the fingertips and the object, the normal force is adjusted to remain constant with a tolerant value between the upper and lower thresholds. Results: In the verification test, shape exploration experiments were conducted using a hand-arm robot equipped with our tactile sensor and a hard sinusoidal-shaped wooden object. Experimental results show that the hand-arm robot is capable of gathering the object contour having a concave or convex portion because its finger position controlled by three-axis tactile sensing information follows the object surface. Conclusion/Recommendations: We derive a control algorithm in robot fingers based on time tangential force increment and normal force detection to perform a shape exploration procedure.