一种触觉传感器处理系统

Sensors aad Actuators A49 (1995) 173-180 A tactile sensor data-processing system Hans Odeberg Depariment of Physics and Mearuremenr Technology, Linkdping Universify, S-Sgl83 Lb&ping, Sweden Received 20 December 1994; in revised form 10 March 1995; accepted 23 May 1995 Abstract In this paper, a framework for distributed sensor data processing is presented, where sensors with local intelligence preprocess information. A supervisor gathers the information from the sensor nodes and fuses the local sensor estimates into a global estimate, using fuzzy logic- based algorithms. Since a typical system will contain a large number of sensor nodes, the local processing will probably be performed by simple 8- or l&bit processors. Care is thus taken to make sure that the algorithms perform well on low-end hardware. The concept is then implemented on a sensor system where local sensor processors extract information from tactile matrices, and send the data to a host over a shared serial bus. Using the distributed processing approach yields increased data-acquisition rates and offloads calculations from the system controller. Keywords: Data processing; Tactile sensors 1. Introduction Consider a measurement system such as the one depicted in Fig. 1. A number of sensors connected to a host computer over a shared bus. If &sensors gather large amounts of data and transmit them over the bus, the bandwidth requirements of this bus will be very large. Also, the sensors may be of different types, sending very different types of data. The host will then be burdened with interpreting all this data, regard- less how much of it is really needed. One way to reduce the bus bandwidth requirements is to let the local processors preprocess data, sending relevant high-level information to the host only when required. To avoid making the sensor systems excessively expensive, the Sensors --On SI sz @ J Supervisor ” .ff P Pig. 1. A typical measurement system. 0924-4247/951$09.50 Q 1995 Etsevier Science S.A. All rights reserved SSDIO924-4247(95)01030-5 processing power of each sensor is necessarily limited. Thus there is a need for fast algorithms. To get a consistent representation of the information for all types of sensors, some issues still need to be resolved: l How should the information from sensor to supervisor be represented? Is there a form common to all sensors? l How is this information extracted from the sensor signals? l How does the supervisor combine the information from different sensors? The next four sections will define a theoretical framework that solves these problems. 2. How to represent sensor information Previous attempts at defining a consistent representation of sensor information have centred on probabilistic methods, using Gaussian statistics [ 1,2]. This approach has its merits: there is a well-defined calculus for probabilities, with espe- cially simple formulae for Gaussian distributions. While solv- ing the consistency problem, this approach does leave other problems unsolved. Data are not always Gaussian. While a Gaussian distribution is often a reasonable approximation for many continuous distributions, modelling a binary on-off sensor with a Gaussian would be somewhat difficult. Instead, we propose the use of a possibility measure, or opinion [ 31, /.L The supervisor sends a hypothesis to the sensor, which then replies with an opinion indicating to what degree it thinks the hypothesis possible. 174 H. Odeberg /Sensors and Actuators A 49 (1995) 173-180 Fig. 2. Creating a sensor opinion. The opinion is modelled as a number in the range [ 0, l] with /J= 1 when data are in complete agreement with the hypothesis, p = 0 when they completely contradict it, and p = 1 when the sensor data have no relevance to the hypoth- esis, indicating a zero information content. The advantage this brings is that there is no longer a need to normalize with respect to the entire universe of discourse. However, new formulae for the evaluation and combination of opinions are required. 3. Creating sensor opinions Since the opinion is defined to be in the range [0, I], a natural approach is to use fuzzy logic. A simple triangular shape could be used to map a sensor input to an opinion (Fig. 2). For more complex input-to-opinion mappings, a fuzzy IF-THEN rule base may be used (Fig. 3). The different rules are then combined into a single opinion using one of the available defuzzification techniques. 4. Adapting to change When the characteristics of measured parameters change, due to drift, wear or noise, it is desirable for the system to note these changes and adapt to them. If the location and width of the triangular mapping between sensor input and opinion described above can be characterized by the mean f and standard deviation a of the measured parameter, one solution is to modify them as new data become available, using the standard recursive least-squares formula [4]. In Ref. [ 51, this method is adapted for use on simple fixed-point processors. 5. Combining sensor opinions Assuming that the sensors have calculated their opinions concerning a hypothesis, the remaining problem is to find a function that combines these into a joint estimate. When combining the opinions of sensors, what algorithms should be used? The standard fuzzy AND/OR connectives are cer- tainly not suitable, since they are implemented as min and max functions. Any algorithm that singles out the most extreme value in a data set is not likely to be very efficient with respect to noise suppression and outlier removal. Let us first look at the combination of two sensors, later extending the results to any number of sensor opinions. The require- ments on the function used are: ( 1) If two sensors disagree, there is no knowing which one to trust. When one sensor supports a hypothesis the other sensor rejects, their combined opinion should be a statement of ignorance. Thus: f(ffx, ;-x)=f (1) (2) When combining a sensor that holds no definite opin- ion (p = 1) with another sensor, the result should be domi- nated by the latter: f(t7 CL,) =I& (2) (3) When two sensors have reasonably similar opinions, what should the result be? One approach is to let the opinions reinforce each other, resulting in a synergy effect. Thus if both sensors are reasonably sure of an opinion, the net result should be a certainty equal to or exceeding that of both opin- ions: f(O,O)=O; f(f, i,=f; f(1, l)=l f(P** CL,) Px 1