Navigation module for a semi-autonomous vehicle

Abstract: A fuzzy logic based navigation module (14) for use on a semi-autonomous vehicle computes the safety of moving from predefined local regions to adjacent local regions (10) for the purpose of planning a safe path for the vehicle to take to reach its intended destination. Image understanding software (12) provides the input to the system (14) in the form of values for linguistic variables for each local region which impact the safe movement (16,18) of the vehicle. Using a five level hierarchial rule base structure, the fuzzy inference process computes intermediate and final safety values which are affected not only by local conditions but also by nearby conditions in order to allow a minimum clearance for the vehicle.

Description: A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. Background of the Invention The present invention relates in general to the field of fuzzy logic and more particularly to the use of fuzzy logic to help navigate a semi-autonomous vehicle such as the Mars Rover. Systems have been developed for navigating manned vehicles in which the driver is advised of the route to take as the vehicle proceeds. Such a system is disclosed in US-A-4882,696. It is envisioned that certain exploration functions - planetary, space, undersea - will best be performed by unmanned vehicles capable of operating with minimal supervision by a remote human operator. Such vehicles are termed semi-autonomous as they must operate for periods of time in a completely autonomous manner. During these periods of the autonomous behavior, an on-board subsystem must navigate the vehicle through a potentially hazardous environment to a selected destination and guarantee the safety of the vehicle during transit. This navigation system must be provided information about the features of the terrain or space to be traversed so as to choose a best path and avoid any hazards. It is anticipated that such data will be provided by both external sources and on-board sensor subsystems. In addition to acquiring gross information about the terrain or space to be traversed from an external source, such as an orbiting satellite for planetary exploration, it is anticipated that more fine grained data will be acquired from on-board sensor subsystems such as optical vision modules. Data from these disparate sources must be integrated to describe the terrain or space to be traversed. However, the inputs which it receives about the environment from these various sources - image understanding software, maps, satellites - will most probably be uncertain and incomplete. This information can be uncertain because there could be evidence which supports the data but there also could be evidence which refutes the data. For the example of a planetary semi-autonomous vehicle, the image understanding software could determine that a certain region is composed of packed gravel, which would be safe but the degree of belief might not be 100% and the region in question could also contain loose gravel, which is unsafe. The information may be incomplete as certain sections of the terrain may not be accessible to the sensors or may contain features which can not be distinguished by the sensors. In order to navigate in an environment which is not completely known and understood, the navigation module needs to reason with the uncertain information and compute the best path - criteria such as shortest distance, minimum time, and least power consumed - which also guarantees the safety of the vehicle. Existing systems are essentially deterministic in that they follow a fixed set of rules and assume complete and accurate data about the environment to be traversed. When that assumption fails, the navigation system typically fails. Summary of the Invention According to a first aspect of the invention, there is provided a method for navigating a semi-autonomous vehicle, said method comprising sensing conditions on terrain within sensor range; producing raw image data about said terrain; extracting features from said raw data in the form of feature data for a plurality of local regions on said terrain, and processing said feature data to determine the relative safety of moving said semi-autonomous vehicle from at least one of said local regions to an adjacent local region, characterised by defining variables for each of said features using fuzzy membership distributions; defining fuzzy rules which determine a safety value for a given combination of values for said variables; and inputting values for said one local region for said variables to said fuzzy rules and determining an output safety value using a fuzzy inference process. According to a second aspect of the invention, there is provided a method for navigating a semi-autonomous vehicle, the method comprising sensing conditions on terrain within sensor range; producing raw image data about the terrain; extracting features from the raw data in the form of feature data for a plurality of local regions on the terrain, and processing the feature data to determine the relative safety of moving the vehicle from one local region to an adjacent local region, characterised by considering the terrain as represented by a cluster of seven hexes having a center hex adjacent to each of six other hexes; determining a regional safety value for each hex of the cluster in response to respective characteristics of the feature data for each hex of the cluster; and determining a respective directional safety value for travelling from the center hex to each of the other hexes of the cluster in response to the regional safety value of the center hex and of the corresponding adjacent hex of the cluster. The fuzzy logic based navigation module of the present invention has input variables defined for terrain type, rock size, and slope between local regions. A local region is defined as a hex shaped area within the sensor range of a vehicle to be navigated. The fuzzy logic system can be embedded on a microprocessor on board the vehicle. Each hex (or pair of neighboring hexes) has associated with it a value for each of the input variables. These values are determined by an image processing module which is external to the present invention. The fuzzy logic system further comprises a set of rules, or knowledge base, organized into a hierarchical structure of five levels. Level 1 determines a regional safety value for a cluster of seven hexes based on terrain type and rock size. Level 2 determines a directional safety value for traveling from the center of the cluster to its six adjacent hexes based on regional safety and slope. Level 3 determines a new regional safety value for the center hex based on the regional safety of the adjacent hexes. Level 4 determines new directional safety values based on the directional safety of adjacent hexes. Level 5 determines final directional safety values based on the level 4 values and the regional safety of the center hex. Each hex is processed by the fuzzy rule base as the center hex of a cluster and upon processing every hex, a mapping of safety values for moving from every hex to an adjacent hex is produced. With this information, a best path can be selected for getting from one point on the terrain to a target point. The fuzzy logic system of the present invention can be built using currently existing tools which allow a developer to write the rules in a high level English like language to be compiled into C-code for a target processor. Brief Description of the Drawings The invention will now be described in greater detail, by way of example, with reference to the drawings, in which:- Figure 1 is a block diagram of a navigation system within which the present invention can be practiced; Figure 2 shows fuzzy membership functions for terrain type; Figure 3 shows fuzzy membership functions for rocks; Figure 4 shows fuzzy membership functions for slope; Figure 5 shows fuzzy membership functions for safety; Figure 6 shows the fuzzy inference process of the present invention; Figure 7 depicts a level 1 evaluation in accordance with the present invention; Figure 8 depicts a level 2 evaluation in accordance with the present invention; Figure 9 depicts a level 3 evaluation in accordance with the present invention; Figure 10 depicts a level 4 evaluation in accordance with the present invention; and Figure 11 depicts a level 5 evaluation in accordance with the present invention. Detailed Description of the Invention Referring now to Figure 1, a block diagram of the modules required for a semi-autonomous vehicle navigation system is shown. Sensor Module 10 provides raw terrain data to Image Understanding Module 12. This data might originate from a variable depth range camera, 2D stereo camera, or other types of known sensors. Image Understanding Module 12 processes the data transmitted by Sensor Module 10 and extracts certain features that have been identified. These features include terrain type (dust, loose sand, packed sand, loose gravel, packed gravel, and rock), slope, and rocks. Pictures from an orbiter could also be used to facilitate the feature identification and extraction process. After the Image Understanding Module 12 has completed the features extraction from the current sector within sensor range, the feature information is formatted in some predefined manner and then communicated to Navigation Module 14. Navigation Module 14 utilizes a priori information (a precomputed path based on pictures collected from an orbiter and relayed to Earth), along with features determined locally by the Sensor Module 10 and Image Understanding Module 12, to compute a local path for the vehicle which minimizes the deviation from the precomputed path while maintaining minimal object clearance, inclination, and other traversal safety parameters. The computed safe path is then sent to Guidance Module 16. Guidance Module 16 calculates the required steering commands from the path coordinates using the vehicles location and transmits this information to Mobility Module 18. Mobility Module 18 receives, processes, and implements the steering commands transmitted by Guidance Module 16 and also monitors the vehicle's motion and manages the interfaces with the lower level systems or modules such as motors, controls, feedback systems, and sensors. Upon completion of local sensor data collection, image processing, navigation, and movements, the vehicle is at a new position and the cycle repeats. The focus of the present invention is on Navigation Module 14. The particular problem solved by the present invention is how to process the large amount of information extracted by Image Understanding Module 12 concerning the terrain type, slope and rocks within a given sector, in such a way as to provide a useful input to various known path planning algorithms. Fuzzy logic is used as the underlying reasoning mechanism to label regions within the sector, as having some degree of safety, thus the present invention is referred to herein as a fuzzy region classifier. Other possible parameters besides safety, such as fuel consumption, region traversibility, cohesion of terrain type, distance from current to goal hex, and distance from start to current hex could also be used, but the implementation described herein uses safety as the criterion for computing a best path. For purposes of the description of the present invention, it can be assumed that Image Understanding Module 12 is able to analyze features contained in hex shaped areas of a size equal to the resolution of the Image Understanding Module 12, which may be one meter for example. Associated with each hex are the attributes, or features, which are extracted by Image Understanding Module 12. Each hex has a terrain type (a coefficient of traction), six slope values (tenths logic knowledge base can also be converted into assembly code for a target processor using Togai InfraLogic FPL (micro Fuzzy Programming Language) Development System. This is a set of tools for developing microcomputer based embedded fuzzy logic systems. With the appropriate compilers and/or assemblers, the fuzzy logic system of the present invention can be embedded in a navigation system such as shown in Figure 1. Appendix A contains a complete source code listing embodying the present invention. Upon compilation of this source code, an object code version of the program can be run on a target microprocessor to perform the fuzzy region classification given an input set of hexes as explained above. Each hex is evaluated with respect to the rules contained therein and the result is a directional safety value assigned to each pair of adjacent hexes. Appendix A contains the complete description of all of the linguistic variables used and their corresponding termset definitions and a complete description of all of the rules used to determine direct and safety values. Each hex is surrounded by six neighbor hexes and the fuzzy rules take into account not only the characteristics of the hex being classified but also the characteristics of its neighbor hexes. The classification scheme of the present invention is divided into a hierarchical structure with five levels. Each level examines certain characteristics of the hexes and possibly the neighbor hexes to determine the safety values for that hex. The results from lower levels of classification are provided as input to a higher level. After describing of meters relative to its six neighbor hexes), a rock value (size in centimeters), and coordinates in a user defined coordinate system. The input to the navigation module of the present invention is a set of hexes as explained above. As also mentioned above, fuzzy logic is used to determine a safety value for each hex based on the characteristics of each hex. Fuzzy logic is a formulation of logic that deals with fuzzy concepts such as very, few, and many in a rigorous mathematical framework. It utilizes IF ... THEN type rules comprised of linguistic variables. A linguistic variable is one whose value is one or more words rather than a number. For example, in the statement "air is hot", the linguistic variable, temperature, has the linguistic value "hot" for air. The mathematical theory of fuzzy logic is well known in the art and numerous references exist which describe the way in which the fuzzy inference process applies a set of fuzzy rules to a set of linguistic input variables (whose values may be certain or fuzzy themselves) to determine crisp (non-fuzzy) values for a set of linguistic output variables. Commonly assigned, co-pending patent application Serial no. 07/775,873 provides such a detailed description of the fuzzy inference process. In creating the navigation module of the present invention, much of the software was generated automatically by the Fuzzy C compiler from Togai InfraLogic, Inc. This tool allows the developer to build the fuzzy logic knowledge base in a high level English like language (see Appendix A). Once the rules are completed, they are converted into C, which can be linked to other software modules. The fuzzy the input variables, these five levels of rules will be described. There are six types of terrain defined. These include dust, loose sand, packed sand, loose gravel, packed gravel, and rock. Figure 2 shows the membership distributions for these classes. The terrain input can have a value from 0 to 175. This number represents the type of terrain as determined by the characterization of its coefficient of friction by the image understanding module. There are three types of rocks defined: small, medium and large. Figure 3 displays the membership distributions for these classes. The domain represents centimeters. The final input is slope. There are three classes for the change in elevation between two adjacent hexes: small, medium and large. Figure 4 displays the membership distributions for these classes. The domain represents elevation change in tenths of a meter. The output of the fuzzy inference process of the present invention is six directional safety values for each hex representing the degree of safety for the vehicle to head in a particular direction from that hex. The directional safety has three classes: low, medium and high. Figure 5 shows the membership distributions for these three classes. The safety value represents degree of confidence, or belief, on a scale of 0 to 100. In order to arrive at final directional safety values for each hex, the fuzzy logic knowledge base of the present invention uses over 200 rules organized in a hierarchical structure composed of five levels. Each hex and its surrounding hexes are individually examined with respect to these five levels of rules in order to determine the six directional safety values for the center hex. The rules therefore must be applied as many times as there are hexes input to the system. All of the rules for each of the five levels are contained in Appendix A. The various levels and their functions will next be described. Level 1 determines a regional safety value for a hex and its surrounding hexes (a cluster). An example of a level one rule is: rule packed_gravel_1_hex0_rule if terrain_type_0 is packed_gravel and rocks_0 is small then S_0_0 is high. end The rule has two clauses in the premise. The first clause checks for the terrain type being of class packed_gravel and the second checks for rocks that are small. The degree to which that hex is safe depends on how well the two clauses match. Figure 6 graphically shows the following example. Suppose the image understanding module characterizes the terrain to have a value of 115 for terrain type and determines that the largest rock is about 25 centimeters in diameter. The packed_gravel_1_hex0_rule rule is evaluated and computes a safety value of about 75. Since the terrain_type is only a partial match with the packed_gravel class, the output from this rule is reduced to this degree of match. This safety value is determined by calculating the centroid of the shaded area in Figure 6. Other rules are usually evaluated and contribute to the final output. Multiple shaded areas corresponding to positive rule outputs are combined and the centroid of the combined areas becomes the output value for a safety variable. Other known output defuzzification methods besides centroid can also be used. The result of level 1 rule evaluations is a regional safety value for each hex in the cluster. In Figure 7, if hex 10 has terrain type of dust, then it is not safe with an extremely high degree of confidence irrespective of any other features it might have. Level 2 computes six directional safety values for each hex. Figure 8 shows an example of a high directional safety value. Travelling from hex 10 to hex 20 would be safe with a very high degree of confidence if the slope is low and there are no features which make either hex 10 or hex 20 unsafe. Level 3 examines the cross-coupling of the level 1 regional safety values between adjacent hexes. In Figure 9, if hex 20 is not safe then hex 10 becomes less safe according to the fuzzy rules in level 3. This allows minimal clearance of one hex width from a particularly unsafe hex. Level 4 examines the cross-coupling of the level 2 directional safety values. The current directional safety value becomes less safe at this level if there is an adjacent directional safety value that is not safe. In Figure 10, if the directional safety value of travelling from hex 10 to hex 20 is low then the level 4 rules would infer that travelling from hex 10 to hex 30 or hex 40 is also unsafe. Level 5 examines the cross-coupling of the level 3 regional safety values with the level 4 directional safety values. At level 5 a directional safety value is determined to be unsafe if the hex's safety value is low. In Figure 11, if the regional safety value of hex 10 is low then the directional safety of all six directions is lowered. The directional safety values output during level 5 are the final directional safety values computed. These safety values are input to any of a number of known algorithms which can compute a "best" path which maintains a certain level of safety while minimizing the deviation from a pre-computed path based on non-local data as described earlier. An example of a simple algorithm is one based on best first search. It should be noted that conventional logic systems could be used to achieve the results of the present invention but at a higher cost. Such systems would require a much larger number of rules. These rules might be more difficult to read and write, thus making the knowledge base more difficult to validate, debug, and maintain. Other techniques could also have been developed, however, an accurate model would be very expensive to develop. In an environment that has many unknowns, a model based approach would be more difficult to tune as the system collects data and experiences new situations. The present rule based system utilizing fuzzy logic can accommodate uncertain and incomplete data and can support self-tuning mechanisms.

Claims: A method for navigating a semi-autonomous vehicle, said method comprising sensing conditions on terrain within sensor range; producing raw image data about said terrain; extracting features from said raw data in the form of feature data for a plurality of local regions on said terrain, and processing said feature data to determine the relative safety of moving said semi-autonomous vehicle from at least one of said local regions to an adjacent local region, characterised by defining variables for each of said features using fuzzy membership distributions; defining fuzzy rules which determine a safety value for a given combination of values for said variables; and inputting values for said one local region for said variables to said fuzzy rules and determining an output safety value using a fuzzy inference process. The method of claim 1, wherein the step of defining fuzzy rules determines regional safety values and directional safety values for a plurality of local regions; the step of inputting values also includes inputting values for adjacent local regions to said fuzzy rules and determining a plurality of directional safety values for said local region; and repeating said steps for each local region on said terrain. The method of claim 2 wherein said fuzzy rules are organized into a plurality of levels and wherein the outputs of each lower level are inputs to a higher level. The method of claim 3 wherein regional safety values and directional safety values of a local region affect regional and directional safety values for adjacent local regions at a plurality of levels. A method for navigating a semi-autonomous vehicle, the method comprising sensing conditions on terrain within sensor range; producing raw image data about the terrain; extracting features from the raw data in the form of feature data for a plurality of local regions on the terrain, and processing the feature data to determine the relative safety of moving the vehicle from one local region to an adjacent local region, characterised by considering the terrain as represented by a cluster of seven hexes having a center hex adjacent to each of six other hexes; determining a regional safety value for each hex of the cluster in response to respective characteristics of the feature data for each hex of the cluster; and determining a respective directional safety value for travelling from the center hex to each of the other hexes of the cluster in response to the regional safety value of the center hex and of the corresponding adjacent hex of the cluster. The method as in claim 5 further including determining an updated regional safety value for the center hex in response to the regional safety values of the other hexes of the cluster. The method as in claim 6, further including determining a respective first updated directional safety value for travelling from the center hex to each of the other hexes in the cluster in response to the directional safety values. The method as in claim 7 further including determining a respective second updated directional safety value for travelling from the center hex to each of the other hexes in the cluster in response to the first updated directional safety values and the updated regional safety value of the center hex. The method as in claim 5 wherein the characteristics of the feature data are selected from the group consisting of dust, loose sand, packed sand loose gravel, packed gravel, rocks, slope of terrain between adjacent hexes and combinations thereof.

Inventors: BONISSONE PIERO PATRONE, CAREY BERNARD JOSEPH, DAUSCH MARK EDWARD, BONISSONE, PIERO PATRONE, CAREY, BERNARD JOSEPH, DAUSCH, MARK EDWARD
Applicants: GEN ELECTRIC, GENERAL ELECTRIC COMPANY
Application year: 1993
Main classification: G05D1
IPC codes: G05B13, G05D1, G01C21