Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots—ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model—we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size.
Abstract-In this paper, we present a novel tactile-array sensor for use in robotic grippers based on flexible piezoresistive rubber. We start by describing the physical principles of piezoresistive materials, and continue by outlining how to build a flexible tactile-sensor array using conductive thread electrodes. A real-time acquisition system scans the data from the array which is then further processed. We validate the properties of the sensor in an application that classifies a number of household objects while performing a palpation procedure with a robotic gripper. Based on the haptic feedback, we classify various rigid and deformable objects. We represent the array of tactile information as a time series of features and use this as the input for a k-nearest neighbors classifier. Dynamic time warping is used to calculate the distances between different time series. The results from our novel tactile sensor are compared to results obtained from an experimental setup using a Weiss Robotics tactile sensor with similar characteristics. We conclude by exemplifying how the results of the classification can be used in different robotic applications.
For both humans and robots, tactile sensing is important for interaction with the environment: it is the core sensing used for exploration and manipulation of objects. In this paper, we present a method for determining object texture by active exploration with a robotic fingertip equipped with a dynamic tactile transducer based on polyvinylidene fluoride (PVDF) piezoelectric film. DitTerent test surfaces are actively explored and the signal from the sensor is used for feature extraction, which is subsequently used for classification. A comparison between the significance of different extracted features and performance of learning algorithms is done and the best method is further used to classify objects by their surface textures with recognition results higher than 90 percent.
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