Guidance system

The present invention consists of comparing a feature pattern extracted from an actual observed image and the prediction pattern of an environmental object predicted in relation to the positional information of a mobile object and finding the difference between both the patterns and then guiding and controlling a mobile object so as to reduce the difference, in that a selection sequence rule of the environmental objects serving as guidance targets is selected and applied by an operator and that the prediction patterns for the guidance targets are created in accordance with the selection sequence rule of the objects. Thus, the comparisons between the prediction patterns of the objects according to the selection sequence rule selected and applied beforehand and the feature pattern from the observed image can be made quickly and accurately, so that the autonomous guidance of the mobile object can have the reliability enhanced more.

BACKGROUND OF THE INVENTION 
The present invention relates to a guidance system for a mobile object such 
as robot, and more particularly to a guidance system which can move a 
mobile object to a destination in adaptation to an environment upon 
understanding the surrounding situation. 
Most of robots presently operating are used under well-ordered environments 
such as factories. In recent years, however, mobile robots usable also 
outside the well-ordered environments hve been required. In order to 
realize the requirement, a robot needs to be given an autonomous moving 
function. 
An example of such a mobile robot is disclosed in a literature by Moravec, 
entitled "The Stanford Cart and The CMU Rover" published on Feb. 24, 1983. 
With the mobile robot of this type, an image obtained with a television 
camera is processed to extract the feature points of the image, a 
three-dimensional object distribution involving a direction and a distance 
is calculated on the basis of the distribution of the feature points, and 
a distance to an object is found on the basis of the calculated result, 
whereupon a guidance signal is delivered to the mobile robot proper. In 
such image processing, complicated calculatons are necessary for the 
detection of the feature points and the extraction of the 
three-dimensional information. It has therefore been difficult to move the 
mobile object at high speed and at high precision. 
SUMMARY OF THE INVENTION 
An object of the present invention is to provide a guidance system which 
selectively applies the patterns of objects in an environment serving as 
the guidance targets of a mobile object, thereby to facilitate image 
processing. 
Another object of the present inventon is to provide a guidance system 
which can guide a mobile object at high speed and at high precision. 
The present invention for accomplishing the objects consists, in comparing 
a feature pattern extracted from an actual observed image and the 
prediction pattern of an environmental object predicted in relation to the 
positional information of a mobile object and finding the difference 
between both the patterns and then guiding and controlling the mobile 
object so as to reduce the difference, in that the selection sequence rule 
of the environmental objects serving as guidance targets is selected and 
applied by an operator and that the prediction patterns for the guidance 
targets are created in accordance with the selection sequence rule of the 
objects. 
Thus, the comparisons between the prediction patterns of the objects 
according to the selection sequence rule selected and applied beforehand 
and the feature pattern from the observed image can be made quickly and 
accurately, so that the autonomous guidance of the mobile object can have 
the reliability enhanced more.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
Now, embodiments of the present invention will be described with reference 
to the drawings. 
FIG. 1 shows one embodiment of the system of the present invention. In this 
figure, numeral 1 designates a mobile robot, which is furnished with a 
vehicle mechanism 1A for running within a specified environmental region. 
The vehicle mechanism 1A of the mobile robot 1 is controlled by a movement 
control device 2, and the movement magnitude thereof is detected by a 
movement magnitude detector 3. A television camera 4 for imaging the 
surrounding enivornment is installed on the mobile robot 1. An image 
picked up by the television camera 4 is delivered to a display device 5, 
and also to feature pattern detection means 6. The feature pattern 
detection means 6 detects the feature pattern of the observed image. First 
memory means 7 stores the selection sequence rules of environmental 
objects. Second memory means 8 is constructed of object memory means 9 and 
object element memory means 10. The object memory means 9 stores the 
environmental objects on the basis of the so-called frame theory. For 
example, as regards a room shown in FIG. 2, the object memory means 9 
stores the frame of the room, the frame of a floor, the frame of a wall, 
etc. hierarchically as illustrated in FIG. 3. In association with the 
object memory means 9, the object element memory means 10 stores object 
elements, for example, the height, depth and width of an obstacle for the 
frame of the obstacle as illustrated in FIG. 3. The object memory means 9 
mentioned above delivers the list of the environmental objects to the 
display device 5. An input device 11 is manipulated by an operator so as 
to select and apply the selection sequence rule of the environmental 
objects which serve as the guidance targets of the mobile object. 
Prediction pattern generaton means 12 reads out the information items of 
the selected object within the enviromental region and the elements 
thereof from the object memory means 9 and the object element memory means 
10 respectively and finds the prediction pattern of the object on the 
basis of the positional information of the mobile robot received from the 
movement magnitude detector 3, and it delivers the prediction pattern to 
the display device 5 and the movement control device 2. The movement 
control device 2 comprises pattern matching decision means 20 to decide 
the propriety of the matching between the feature pattern from the feature 
pattern detection means 6 and the prediction pattern from the prediction 
pattern generation means 12 and to deliver an "mismatch" signal to the 
display devie 5 when the matching is improper, potential field calculation 
means 21 to respond to a "match" signal from the pattern matching decision 
means 20 to calculate the field of a potential .phi..sub.A acting between 
the prediction pattern and the feature pattern, for the purpose of 
controlling the vehicle mechanism 1A of the mobile robot 1 so as to 
diminish the deviation of both the patterns, and action force calculation 
means 22 to calculate the gradient vector of the force of action F.sub.B 
of the feature pattern on the prediction pattern as established by the 
field of the potential .phi..sub.A and to deliver the gradient vector as a 
guidance signal. 
Next, the interaction .PHI..sub.AB between the aforementioned patterns will 
be described. 
Hereunder, in order to simplify the description, it is assumed that a space 
S be a screen as shown in FIG. 4 and that patterns A and B be binary 
images. Here, it is assumed that the pattern A be a pattern based on an 
observed image, while the pattern B be a pattern based on a predicted 
image created with an environment model K. Now, when the pattern A has 
been given, an adaptation law for bringing the position of the pattern B 
close to the pattern A needs to be found out. This adaptation law is 
obtained by introducing the potential .phi..sub.A which is produced on the 
space S by the pattern A. The potential .phi..sub.A can be evaluated 
according to the following equation (1): 
EQU .alpha..phi..sub.A +X.sub.A =0 (1) 
Here, .alpha. denotes a differential operator of 
##EQU1## 
and .sigma..sub.y are parameters which denote the spacial extents of the 
potential .phi..sub.A. In addition, X.sub.A in Eq. (1) is a characteristic 
function in the case where the pattern A is regarded as the subset of the 
space S. 
Next, the action F.sub.B to which the pattern B is subjected by the field 
of the above-stated potential .phi..sub.A of the pattern A on the space S 
can be evaluated according to Eq. (2) given below. Here, in order to 
facilitate the description, only movements in x-axial and y-axial 
directions within a two-dimensional plane shall be handled. 
##EQU2## 
Here, X.sub.B is a characteristic function in the case where the pattern B 
is classed with the subset of the space S, and D denotes a gradient 
operator, namely, a differential operator of 
##EQU3## 
In accordance with Eqs. (1) and (2) mentioned above, it is possible to 
obtain information for transforming the pattern B relative to the pattern 
A, that is, the guidance information of the mobile robot. Since, however, 
Eq. (1) in this computation includes simultaneous equations of infinite 
dimensions, the use of the following approximation equation is favorable; 
##EQU4## 
The output U.sub.B of a manipulation mechanism M.sub.B for manipulating and 
moving the pattern B to the pattern A by the use of the solution of the 
potential .phi..sub.A in Eq. (3) given above can be evaluated according to 
the following equation (4): 
##EQU5## 
Next, the operation of the foregoing embodiment of the system of the 
present invention will be described. 
Prior to the initiation of the guidance of the mobile robot 1, the observed 
image from the television camera 4, the object list from the object memory 
means 9 and the prediction pattern from the prediction pattern generation 
means 12 are displayed on the display device 5. Meanwhile, the feature 
pattern detection means 6 detects the feature pattern from the observed 
image and delivers it to the pattern matching means 20. In addition, the 
prediction pattern generation means 12 delivers the prediction pattern to 
the pattern matching means 20 on the basis of the movement magnitude from 
the movement magnitude detector 3. The pattern matching means 20 delivers 
the "mismatch" signal to the display device 5 when the matching between 
both the patterns is impossible. Subsequently, on the basis of the 
"pattern mismatch" display indicated on the display device 5, the operator 
applies the selection sequence rule of the information items of the 
objects serving as the guidance targets of the mobile robot 1, to the 
first memory means 7 by the use of the input device 11 by referring to the 
observed image, the object list from the object memory means 9 and the 
prediction pattern from the prediction pattern generation means 12 which 
are indicated on the display device 5. Upon completion of this selection, 
the prediction pattern generation means 12 reads out the distribution and 
configuration data of the object from the object memory means 9 and the 
object element memory means 10 respectively in accordance with the 
selected rule and evaluates the prediction pattern of the object. This 
prediction patter is applied to the display device 5. Thus, when the 
operator has judged the prediction pattern improper by referring to the 
observed image and the prediction pattern displayed on the display device 
5, he/she manipulates the input device 11 so as to obtain the prediction 
pattern of another object from the object memory means 9 and to indicate 
it on the display device 5. When this prediction pattern has been judged 
proper, it is applied to the movement control device 2 through operator's 
manipulation of the input device 11. The movement control device 2 is also 
supplied with the feature pattern of the observed image from the feature 
pattern detection means 6. In consequence, the movement control device 2 
compares the feature pattern with the prediction pattern and supplies the 
vehicle mechanism 1A of the mobile robot 1 with the guidance signal for 
guiding the mobile robot 1. Thus, the mobile robot 1 is guided and 
controlled to a target spot. 
As described above, the operator can apply the selection sequence rule of 
environmental objects with the input device 11 and select the prediction 
pattern of an object element corresponding to a certain object element in 
a feature pattern. In this way, the matching between the feature pattern 
and the prediction pattern is appropriately performed, so that the mobile 
robot can be guided at high speed and at high precision. 
While, in the foregoing, the mobile robot has been described by way of 
example, the invention is not restricted thereto. 
As thus far described, according to the present invention, a prediction 
pattern to be compared with the feature pattern of an observed image can 
be applied and set by selecting a rule, so that a very long time is not 
required for image processing as in the prior art. Therefore, a mobile 
object can be guided at high speed and at high precision.