Abstract
Tactile sensing is a key enabling technology to develop complex behaviours for robots interacting with humans or the environment. This paper discusses computational aspects playing a significant role when extracting information about contact events. Considering a large-scale, capacitance-based robot skin technology we developed in the past few years, we analyse the classical Boussinesq–Cerruti’s solution and the Love’s approach for solving a distributed inverse contact problem, both from a qualitative and a computational perspective. Our contribution is the characterisation of the algorithms’ performance using a freely available dataset and data originating from surfaces provided with robot skin.
Highlights
The problem of characterising the physical interaction between robots and humans or the environment, typically using an artificial sense of touch, has received increasing attention in the literature [1,2,3]
We introduce a solution based on the Boussinesq–Cerruti model [21,22], we point out its limitations, and we introduce the Love’s model [23,24,25], which we adapt to our robot skin design
This paper introduces an approach to extract information about contact events based on large-scale tactile sensors
Summary
The problem of characterising the physical interaction between robots and humans or the environment, typically using an artificial sense of touch, has received increasing attention in the literature [1,2,3]. Extensive work has been done to allow robots to obtain information about contact events [4]. Two classes of approaches for obtaining meaningful information about contact events can be identified. [7], data-driven approaches are appealing when modelling contact events is complex and it is difficult to model the sensor’s response, to take noise into account. Model-driven approaches are adopted whenever a (possibly simplified) model of the force distribution is available. Such models are usually based on principles of contact mechanics, and are aimed at determining closed-form solutions for contact shape reconstruction [8,9,10]
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