Recent developments in robotics have enabled humanoid robots to be used in tasks where they have to physically interact with humans, including robot-supported caregiving. This interaction—referred to as physical human–robot interaction (pHRI)—requires physical contact between the robot and the human body; one way to improve this is to use efficient sensing methods for the physical contact. In this paper, we use a flexible tactile sensing array and integrate it as a tactile skin for the humanoid robot HRP-4C. As the sensor can take any shape due to its flexible property, a particular focus is given on its spatial calibration, i.e., the determination of the locations of the sensor cells and their normals when attached to the robot. For this purpose, a novel method of spatial calibration using B-spline surfaces has been developed. We demonstrate with two methods that this calibration method gives a good approximation of the sensor position and show that our flexible tactile sensor can be fully integrated on a robot and used as input for robot control tasks. These contributions are a first step toward the use of flexible tactile sensors in pHRI applications.

Many studies faced the problem of vehicle autonomous navigation in different fields, but nowadays just a few of them uses all the implicit information coming from the context in which such navigation is occurring. This results in a huge potential information loss that prevents us from adapting the vehicle's behavior to each different situation it may be in. In a previous work, we defined a method to model the static context of navigation using ontologies and take it into account in the command law when performing a local navigation task. In this paper, we extend our model of the context of navigation, and define a software architecture able to update the context dynamically, by using sensor information. The method is tested with real-time experiments on driving simulator. They show that the Context of Navigation can be effectively updated during the navigation and leads to a smarter vehicle's behavior on the road.