资源描述
Fusion Strategies for Minimizing Sensing-Level
Uncertainty in Manipulator Control
Abstract：Humanoid robotic applications require robot to act and behave like human being.Follow-ing soft computing like approach human being can think,decide and control himself in unstructured dynamic surroundings,where a great degree of uncertainty exists in the information obtained through sensory organs.In the robotics domain also,one of the key issues in extracting useful knowledge from sensory data is that of coping with information as well as sensory uncertainty at various levels.In this paper a generalized fusion based hybrid classifier(ANN-FDD-FFA)has been developed and applied for validating on generated synthetic data from observation model as well as from real hardware robot.The fusion goal,selected here,is primarily to minimize uncertainties in robotic manipulation tasks that are based on internal(joint sensors)as well as external(vision camera) sensory information.The effectiveness of present methodology has been extensively studied with a specially configured experimental robot having five degrees of freedom and a simulated model of a vision guided manipulator.In the present investigation main uncertainty handling approach includes weighted parameter selection(of geometric fusion)by a trained neural network that is not available in standard manipulator robotic controller designs.These approaches in hybrid configuration has sig-nificantly reduce the uncertainty at different levels for faster and more accurate manipulator control as demonstrated here through rigorous simulations and experimentations.
Key words:sensor,fusion,FDD,FFA,ANN, computing,manipulators,repeatability,accuracy,covariance,matrix,uncertainty,uncertainty ellipsoid.
1.Introduction
applications(industrial,military,scientific,medicine,welfare,household and amusement)are increasingly coming up with recent prog-ress in which a robot has to operate in large and unstructured environment [3,12,15].In most cases,the knowledge of how the surroundings are changing every instant is fundamentally important for an optimal control of robot motions. Mobile robots also essentially have to navigate and operate in very large unstruc-tured dynamic surroundings and deal with significant uncertainty[1,9,19].When-ever a robot is operating in a natural nondeterministic environment,there always exists some degree of uncertainty in the conditions under which a given job will be done.These conditions may,at times,vary while a given operation is being carried out.The major causes leading to the uncertainty are the discrepancies arising in the robot motion parameters and in the various task-defining information.The amount by which they differ from those called for in the process specifications may not always be insignificant.These deviations may be due to inaccuracies in analytical design or in reproductions of programmed motions or because of deterministic as well as random errors in the algorithms,measurement data,data transmission links,and other factors.Changes in the status of the robot like instances of malfunctions,failures,shift in the frame of reference,etc.,also lead to uncertainty in the operating conditions of the robot.The presence of substantial uncertainty significantly affects the robot in the various steps of sensing the state of a task;in adapting to the changes through the control system;and in reasoning to select the actions needed to achieve a goal.
In fact,it is felt that one of the key issues in extracting useful knowledge from data is that of coping with uncertainty at all levels and especially at the sensing level.Along with the quantity of the observed sensory measurements,the qual-ity involved also need to be investigated in terms of the residual uncertainty it propagates to the desired sensory information.
In robotics domain,the uncertainty problem in the sensory interpretation level is a very crucial one for specific tasks like robotised space structure manipulators,robotised surgery etc.where both high level of machine precision and human like prehension are needed The key problem in the sensing process is in making the connection between the signal outputs of all the sensors and the attributes of the three-dimensional world.One of the recent trends is to solve the problem through sensor fusion and there are numerous fusion techniques covering a very broad spectrum of application areas[10,13].Under the backdrop of the study of these research works,it was felt that there is a great need for evolving a generalized and easily apprehensible soft computing based sensor fusion strategy(humanoid approach)for multiple sensory systems.The humanoid approach makes it available for versatile applications.The easily apprehensible character of the development makes it particularly suitable for processing complex,highly nonlinear functional relationships between low-level sensory data and high-level information.The fusion strategies would be most suitable to apply in distributed fusion architectures as it can effectively enable us to minimize the uncertainties at any desired level.A review of some papers on uncertainty analysis in the context of manipulator control [4,14,16,20,23]shows that a common step involved in all these systems is the interpretation of identical information that has been acquired through multiple sensory units.The fused information needs to be represented with minimized uncertainty and the level of this minimization depends on task specific applications.The research study described in this paper has focused on this objective in the context of sensory guided robotic manipulations.As a token application here the challenge of improving repeatability of a very ordinary RSC type robot has been undertaken.
Real-world systems are stochastic in nature having nonlinearity and uncertainty in their behaviors and hence humanoid approach of solutions are only acceptable one in many such tasks.For multivariable input–output systems,effects of such nonlinearity and uncertainty are significant and needs to be addressed properly in order to control them effectively.Take,for example,the case of advanced robotic systems(manipulating robots having redundant degrees of freedom or mobile robots having redundant sensory systems would fall in this category).These systems require various kinds of sensors for responding intelligently to a dynamic environment.They may be equipped with external sensors such as force-torque sensors,range sensors,proximity sensors,ultrasonic and infrared sensors,tactile arrays and other touch sensors,overhead or eye-in-hand vision sensors,cross-fire,overload and slip sensing devices etc.In addition,there are also various internal state sensors such as encoders,tachometers,revolvers and others.More is the number of sensors,more is the computational complexity for controlling the system and more is its intelligence level.Since recent industrial as well as non-industrial applications need robotic systems with high level of intelligence,the complexity associated with it has to be addressed properly.For this purpose,systems equipped with multiple sensors having different ranges of uncertainties has been taken up here for study.
Information obtained from different sensors are inherently uncertain,imprecise and inconsistent.Occasionally it may also be incomplete or partial,spurious or incorrect and at times,it is often geographically or geometrically incompatible amongst the different sensor views.Our knowledge of the spatial relationships among objects is also inherently uncertain.Take the example of a man-made object.It may not match its geometric model exactly because of manufacturing tolerance,human/machine errors and other natural uncertainties.Even if it does(in macro level),a sensor cannot measure the geometric features and locate the object exactly because of measurement errors.Even if it can(within certain bounded tolerance limit),a robot using the sensor may not manipulate the object exactly as intended,may be because of all cumulative errors added with the end-effector positioning errors.These errors can be reduced to a very significant level for some tasks,by reengineering the solution,structuring the working environment and using specially suited high precision equipment-but at great cost of time and equipment[20].An alternative solution may be to develop sensor fusion strategies that can minimize and eliminate the uncertainties of any engineering system to a desired level,at a much lesser cost,incorporating all inherent uncertainties.In this paper we focus on developing a FDD-FFA-ANN based hybrid type sensor fusion strategy.
The organization of the paper has been arranged as follows.Section 2 outlines the computational steps through which the overall fusion algorithm has been formulated and developed.These developments and propositions have been applied in Section 3 for validating on synthetic data of an observation model.Section 4 is dedicated towards applying the developed hybrid fusion strategies for improving repeatability of a hardware robot manipulator.Their effectiveness has been extensively studied with a specially configured RCS type experimental robot having five degrees of freedom.A neural network formulation of the fusion algorithm is also presented.Finally,in Section 5 the significant results and inferences have been listed.
2.Formulation of the Fusion Algorithm Structure
The fusion algorithm structure consists of the following computational steps:
(i)The uncertainties in the information derived through processing of multiple noisy sensory data are represented by individual uncertainty ellipsoids.
(ii)The uncertainty ellipsoids are merged in a manner so as to minimize the volume of the fused uncertainty ellipsoid by proper assignment of optimal weighting matrices.
(iii)Fusion in the Differential Domain(FDD)has been developed to further reduce the uncertainty of fused information at finer resolutions through an iterative process that predicts the correction terms for all the sensory information.These terms are then fused and applied to the fused information to increase its precision.
(iv)The Fission Fusion Approach(FFA)is used to minimize uncertainties significantly for some specific sensor models where the covariance matrix of the sensory information can be“fissioned”and information from multiple measurements of the same set of sensors are available for fusion.
(v)An ANN model of the manipulator has been developed for initial estimation of uncertainties(Mean Square Error)of joint sensors which could be further minimized by fusion process(FDD,FFA).
The fusion methods as represented by steps(i)and(ii)give a physical or rather geometric insight for the complicated information processing as it involves the fusion of the uncertainty ellipsoids of each individual sensory information.Given a set of uncertainty ellipsoids associated with each sensor,the problem is to assign weighting matrices(Wi)with each set of sensory system so as to minimize geometrically the volume of the fused uncertainty ellipsoid[17].The parameter representing the information Xi∈Rn is usually determined from a set of sensory observational data,Di∈Rmi.Here,Rn represents the general n-dimensional Euclidean spaces,i denotes the ith sensor,mi is the number of independent measurements,and n is the dimension of information(i=1,...,N,N being the total number of sensor units),and Xi and Di are known to be related through a known nonlinear vector function,
The fused information Xf is then made available in the linear combination
Using Lagrangian optimization,we have the weighting matrices for the geometrically optimized fusion as
3.Fusion to Improve Sensory Information
In multi sensor fusion systems with redundant and/or complimentary sensors,each sensor can always be considered as individual sources of uncertain information,able to communicate,co-operate and co-ordinate with other members of the sensing group.Based on this structure,Durrant-Whyte[7]has presented sensor models described as a probabilistic function of state and decisions communicated from other information sources.They have treated three components of this sensor model:the observation model,that processes the measurement characteristics;the dependency model that describes the sensor’s dependence on other information sources;and the state model that characterizes the sensor’s dependence on its location and internal state.
4.Experimental Verification of Hybrid(Neuro-FFA-FDD)
Approaches for handling Uncertainties in Improving Repeatability of Robotic Manipulator In robotic manipulations,there are a number of sources of uncertainties:
(i)uncertainties associated with sensors,
(ii)uncertainties associated with actuators,
(iii)uncertainties associated with modeling.
In the present investigation,attention is focussed towards uncertainties associated with sensors and their minimization.Most industrial robots execute simple repetitive tasks by playing back prerecorded or preprogrammed sequences of motions that have been previously guided or taught by a user.For this type of performance,the robots do not need any information about its working environment.External sensors are not that important here,as manipulators have to simply move to goal points that have been taught.A“taught”point is that to which the manipulator is moved physically,the corresponding joint position sensors are determined,and the joint-angle values stored.Subsequently,in the next command to the robot to return to the same point in space,each joint is moved to the stored value.The precision with which a manipulator can return to a“taught”point is specified by the factor“repeatability of the manipulator”.An indispensable capability for most manipulator application is to provide a high speed and high precision trajectory.In such applications the repeatability of these manipulators need to be quantified as accurately as possible.For this,the analytical description of the spatial displacement of the robot,as a function of time is primarily required.This,in particular,depends on the functional relation between the joint angle variables and the position and orientation(with respect to a reference co-ordinate frame)of the robot arm end-effector.
5.Conclusions
Future humanoid robots will have to work in a multisensor framework,the fused information needs to be represented with minimized uncertainty.The level up to which the minimization would be significant is once again depends on specific application and the sophistication of the classifier handling the fused information.This paper has proposed and developed a hybrid sensor fusion classifier which consists of three levels of fusion–Geometric Fusion,Fission Fusion Approach (FFA)and Fusion in the Differential Domain(FDD).These are directed towards the objective of minimizing uncertainties associated with any type of information that has been acquired throu
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