Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system

Murphy, Michael G.

English

NASA Technical Reports Server

FUZZY LOGIC PATH PLANNING SYSTEM FOR COLLISION AVOIDANCE 
BY AN AUTONOMOUS ROVER VEHICLE 
Final Report 
NASA/ASEE Summer Faculty Fellowship Program--1991 
Johnson Space Center 
Prepared By: Michael G. Murphy, Ph.D. 
Academic Rank: Professor 
University & Department: University of Houston-Downtown 
Department of Applied 
Mathematical Sciences 
Houston, TX 77002 
NASA/JSC 
Directorate: Information Systems 
Division: Information Technology 
Branch: Software Technology 
JSC Colleague: Robert N. Lea, Ph.D. 
Date Submitted: August 23, 1991 
Contract Number: NGT -44-001-800 
15-1 
https://ntrs.nasa.gov/search.jsp?R=19920012058 2020-03-24T07:12:49+00:00Z
ABSTRACT 
The Space Exploration Initiative (SEI) of the United States will 
make great demands upon NASA and its limited resources. One 
aspect of great importance will be providing for autonomous 
(unmanned) operation of vehicles and/or subsystems in space flight 
and in exploration of environments such as the surface of Mars. An 
additional, and complicating, factor of SEI is that much of the need 
for autonomy of operation will take place under conditions of great 
uncertainty or ambiguity. Thus, traditional approaches are less 
likely to provide satisfactory results for the ambitious goals of SEI. 
In particular, it is appropriate to consider how control situations 
(one of the major problem areas) can be handled through emerging 
technologies such as fuzzy logic, neural networks, and genetic 
algorithms. The absence of precise mathematical modelling for 
uncertain environments provides an opening for alternative 
approaches that are better suited for such problem domains and 
which in fact may lead to a lower level of computational complexity 
or even the possibility of customized computer chips that will 
handle specific control problems. 
Systems already developed at NASAlJSC have shown the 
benefits of applying fuzzy logic control theory to space-related 
operations. This report is concerned with four major issues 
associated with developing an autonomous collision avoidance 
subsystem within a path planning system designed for application in 
a remote, hostile environment that does not lend itself well to 
remote manipulation of the vehicle involved through Earth-based 
telecommunications. A good focus for this is unmanned exploration 
of the surface of Mars. The uncertainties involved indicate that 
robust approaches such as fuzzy logic control are particularly 
appropriate. 
Four major issues addressed in this report are: 
1. avoidance of a single fuzzy moving obstacle; 
2. backoff from a deadend in a static obstacle environment; 
3. fusion of sensor data to detect obstacles; 
4. options for adaptive learning in a path planning system. 
It is likely that the approaches described and references given will 
be useful for other problems with differing situations but 
characteristics common to those described here (e.g., autonomy of 
operation under conditions of uncertainty). 
15-2 
INTRODUCTION 
This report addresses four important aspects of autonomous 
collision avoidance in a path planning system: avoidance of a single 
fuzzy moving obstacle; backoff from a deadend in a static obstacle 
environment; the fusion of sensor data from multiple sensor sources 
for obstacle detection; and, options for adaptive learning in a path 
planning system. Previous work dealt with various types of 
stationary obstacle scenarios. 
NASA is currently involved with planning a variety of 
unmanned missions. As a particular focus, this investigation will 
consider some of the issues associated with unmanned surface 
exploration of Mars. 
Examples of the need for collision avoidance by an autonomous 
rover vehicle with a moving obstacle would be: wind-blown debris, 
surface flow or anomalies due to subsurface disturbances, another 
vehicle, etc. The other issues of backoff, sensor fusion, and 
adaptive learning are important in the overall path planning system 
concept. Fuzzy logic control systems have been shown by Robert N. 
Lea of NASA/Johnson Space Center and others to be an effective tool 
in building reliable systems that function well in the presence of 
uncertainty or ambiguity (1,2,3). 
The research into the use of fuzzy logic in the decision and 
control process for autonomous path planning including collision 
avoidance is a 'new aspect of a continuing problem domain 
(4,5,6,7,8,9). The theory of fuzzy sets and fuzzy logic was 
introduced in 1965 by L. A. Zadeh of the University of California, 
Berkeley (10). The book by Klir and Folger (11) gives a good 
treatment of the fundamentals of this field, while the book by Kosko 
(12) addresses more advanced aspects as well as the interface 
between the application of fuzzy and neural approaches to problem-
solving. Until the last few years, there has been a dearth of 
commercial applications of fuzzy logic control (13). At the present, 
there is a tidal wave of applications coming from Japan, addressing 
problems in subway systems, process control in industry, automatic 
transmissions, camera display integrity, washing machines, vacuum 
cleaners, etc. Togai InfraLogic, Inc., of California has marketed a 
fuzzy logic expert system shell for ease of fuzzy logic applications 
development (14). 
15-3 
For true autonomy of operation, higher-level path planning is 
necessary to ensure integrity of the physical system, allow for 
conservative modification of guidance rules based on experience, and 
facilitate efficient backoff from deadend approaches that interfere 
with accomplishing the original goal of the mission. A consideration 
in this investigation is to seek generalized features that encourage 
extension and adaptation of this path planning system to other 
environments (e.g., autonomous collision avoidance for space 
vehicles with respect to other space vehicles, natural and man-made 
space debris, etc.) Other types of uncertainty modelling, such as 
Dempster-Shafer theory, may well be useful tools to complement 
the strengths of fuzzy logic. 
AVOIDANCE OF MOVING OBSTACLES 
For purposes of this report, we will address a single, moving 
fuzzy obstacle; that is, an obstacle with a fuzzy radius wand either 
a fuzzy speed [m - .1m , m + .1m] or a fuzzy direction [e - .1e , e + .1e]. 
See Figure 1 for a graphic representation of these situations. 
destination 
o ~~~ {"" ... ~,..)tIt L~~ ~ 3 r. ",~t; 
obstacle 
o 
source 
a. fixed direction, fuzzy speed 
obstacle 
destination 
o 
o 
source 
b. fixed speed, fuzzy direction 
Figure 1. - Moving Obstacle Scenario 
Using the simplest possible approach to a potentially 
complicated problem, it is best to not try to project the exact path 
of the moving obstacle. Instead, the best approach is to use fuzzy 
rules and a fuzzy inferencing mechanism to take an imprecise 
environment and assess the likelihood of collision based on the 
current situation. If necessary, collision avoidance techniques are 
then applied to avoid the danger. Figure 2 contains two key rules for 
15-4 
the system and membership functions for key fuzzy linguistic 
variables. To activate the avoidance system means to make changes 
as needed in speed and direction to avoid collision. 
Sample Fuzzy Rules: (fuzzy linguistic variables are underlined) 
If the paths of the vehicle and the obstacle ~ 
and if the time of crossing is similar. 
then the obstacle is a hazard. 
If the obstacle is ~ and if the obstacle is a hazard. 
then activate the collision avoidance system. 
Sample Membership Functions for Fuzzy Linguistic Variables: 
o - closest distance 
of paths 
hazard 
ILl , 
h .. degree to which 
obstacle is hazard 
activate 
T .. threshold 
similar 
t .. difference of time when 
closest distance of path is attained 
d = critical distance·factor 
to adjust for various speeds 
Figure 2. - Sample Rules and Membership Functions 
15-5 
Figure 3 has the architecture for a fuzzy avoidance system for 
a moving fuzzy obstacle. Implementation may well result in 
modification of this architecture and/or subsystems to enhance 
performance. In general, this will be a subsystem of a general path 
planning system for autonomous exploration with collision 
avoidance. 
d desire 
direct ion 
, database I 
actual parameters 
(direction, speed) 
vehicle control 
system 
r- ~ 
no control 
changes 
control 
command 
changes 
.' 
defuzzification f 
activate above 
threshold 
.It 
avoidance 
mechanism 
sensor data 
1 
sensor processing 
(fuzzification, fusion) 
l 
fuzzy rulebase 
fuzzy inferencing 
Figure 3. - Architecture for Fuzzy Collision Avoidance Sysem 
SENSOR FUSION 
Sensor fusion, in the sense of combining information based on 
more than one sensor operating simultaneously, promises to give a 
significant improvement in object detection over the use of a single 
sensor source (4,8). The problem, of course, is to have a 
computationally reasonable means of combining and interpreting 
sensor data from dissimilar sources. Dempster-Shafer Theory is a 
15-6 
good candidate for giving useful combinations and interpretations of 
such data, but it is computationally intense (15,16). A formation of 
information in a hierarchical structure can be used to give a 
significant improvement in computational complexity. Our search 
here has been exploratory in nature, and we see directions for 
further work, but it is clear that nothing definitive has been 
established to date. 
BACKOFFTECHNIQUES 
Basically there are two viable techniques for backoff from 
deadends in a static environment that is not fully mapped and where 
uncertainty of information is a regular element of the environment. 
The first technique is based on reversing direction coupled with 
extending the critical distance for sensor processing and synthesis 
so that oscillatory travel patterns are avoided. A possible concern 
with this approach is that the algorithm is essentially a greedy 
heuristic that works under the premise that most of the time making 
a local optimal choice will yield a successful path. The second 
approach is to store a modified world model that would map known 
(or approximate) information regarding the explored environment so 
that a repeat of that exploration would be prevented. The problem 
with this second approach is the need for significant memory to 
store the model and increased processing capability for subsequent 
path development in backoff situations. With neither choice being a 
clear winner, it is likely that the first approach may have an 
advantage in the sense of a lesser degree of complexity. Other 
possibilities are: storing a limited map of the explored region or 
blocking one or more sectors from being chosen for the direction of 
the vehicle until an obstacle threshold has been passed and new data 
is available to evaluate path options. These are desired directions 
and alternatives for expanding and improving the algorithms 
previously reported (5,6,7). 
OPTIONS FOR ADAPTIVE LEARNING IN A PATH PLANNING SYSTEM 
One of the most promising options for adaptive learning in 
control environments has been the use of neural networks 
(5,12,17.18.19.20.21.22.23). A difficulty with applying neural 
networks to adaptive learning in fuzzy environments is the transfer 
15-7 
of techniques from one application to another. Many of the neural 
approaches to control are very domain specific and require extensive 
modification (when possible) to use in conjunction with fuzzy 
systems. One particular approach which is promising is to use 
neural nets (or even neurons) to tune (adjust) the membership 
functions of fuzzy variables. Attempts to find general approaches or 
modify existing designs to accomplish this have been less than 
successful to date, but it is too early to write off this approach. A 
bigger problem will be to develop an adaptive system that will 
operate on data being generated as the system performs and 
continually (or periodically) update parameters of the system to 
improve or maintain optimal (or near optimal) performance. Even 
though this appears to be feasible, it is still unsolved and appears to 
be more difficult than originally viewed and will still face the test 
of convincing skeptical engineers that a new technology that adapts 
to changing circumstances in uncertain environments is a viable 
choice for mission critical problem-solving. A different adaptive 
technology that seems possibly suited for training a fuzzy logic 
control system is genetic algorithms (24). 
CONCLUSION AND FUTURE DIRECTIONS 
Fuzzy logic control provides significant opportunity for 
application to uncertain and/or ambiguous control environments 
such as autonomous collision avoidance. Many areas have been 
identified in this report that warrant further investigation. In brief, 
areas of promise are simulation of avoidance of a moving obstacle, 
use of various approaches to sensor fusion, validation of backoff 
techniques for static collision avoidance environments, and further 
development of adaptation techniques to improve/maintain system 
performance under conditions of uncertainty. Integrating these 
concepts into a viable path planning system is a worthy, if difficult, 
goal. Finally, identification of general concepts that transfer to 
other problem domains is a side goal that may be even richer than 
the application to collision avoidance and path planning. 
15-8 
r 
1. 
REFERENCES 
Lea, Robert N.: Automated Space Vehicle Control for 
Rendezvous Proximity Operations. Telematics and 
Informatics, vol. 5, no. 3, 1988, pp. 179-185. 
2. Lea, Robert N.: Applications of Fuzzy Sets to Data Monitoring 
and Process Control. Proceedings of the ISA '88 
International Conference and Exhibit, October, 1988, pp. 
1495-1502. 
3. Lea, Robert N.; Togai, Masaki; Teichrow, Jon; and Jani, 
Yashvant: Fuzzy Logic Approach to Combined 
Translational and Rotational Control of a Spacecraft in 
Proximity of the Space Station. Proceedings of' the Third 
International Fuzzy Systems Association Congress, 
August, 1989. 
4. Dougherty, E.; Giardina, C.: Mathematical Methods for Artificial 
Intelligence and Autonomous Systems. Prentice Hall, 
1988. 
5. Murphy, Michael: Fuzzy Logic Control System to Provide 
Autonomous Collision Avoidance for Mars Rover Vehicle. 
Final Report of Summer Faculty Fellowship at NASAlJSC, 
1990. 
6. Lea, R.; Murphy, M.; and Walters, L.: Fuzzy Logic Control for 
Autonomous Collision Avoidance. North American Fuzzy 
Information Processing Society 1991 Workshop 
Proceedings, May, 1991, pp. 154-158. 
7. Lea, R.; Jani, Y.; Murphy, M.; and Togai, M.: Design and 
Performance of a Fuzzy Logic Based Vehicle Controller 
for Autonomous Collision Avoidance. Fuzzy and Neural 
Systems, and Vehicle Applications '91, (to appear; Tokyo, 
Japan: November, 1991). 
15-9 
8. Yen, John; and Pfluger, Nathan: Designing an Adaptive Path 
Execution System. Proceedings of the 1991 IEEE 
International Conference on Systems, Man and 
Cybernetics, (to appear). 
9. IEEE Expert, vol. 6, no. 4, August, 1991 (4 papers on vision and 
navigation). 
10. Zadeh, L. A.: Fuzzy sets. Information and Control, vol. 8, pp. 
338-353. 
11. Klir, George J.; and Folger, Tina A.: Fuzzy Sets, Uncertainty, 
and Information. Prentice-Hall, 1988. 
12. Kosko, Bart: Neural Networks and Fuzzy Systems: A' Dynamical 
Systems Approach to Machine Intelligence. Prentice 
Hall, 1992 [sic]. 
13. Lee, Chuen-Chien: Fuzzy Logic in Control Systems: Fuzzy Logic 
Controller-Parts I and II. IEEE Transactions on Systems, 
Man, and Cybernetics, vol. 20, no. 2, 1990, pp. 404-435. 
14. TILShell User's Manual, Release 1.1. Togai InfraLogic Inc., Jan., 
1990. 
15. DeKorvin, A.; Kleyle, R.; and Murphy, M.: Properties of States of 
Recognition Arising from a Set of Knowledge Systems 
with Imperfect Reporting. Stochastic Analysis and 
Applications, vol. 9, no. 2, 1991, pp. 133-146. 
16. DeKorvin, A.; Kleyle, R.; and Murphy, M.: Methods for Efficient 
Object Detection under Conditions of Uncertainty and 
with Implications for Path Planning. North American 
Fuzzy Information Processing Society 1991 Workshop 
Proceedings, May, 1991, pp. 42-46. 
17. Widrow, Bernard; and Stearns, Samuel D.: Adaptive Signal 
Processing. Prentice Hall, 1985. 
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Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle

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