SSS: A Hybrid Architecture Applied to Robot Navigation - CiteSeerX

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Once a map has been created, an efficient route can beplotted by a spreading activation algorithm [10] or someother method. To traverse this route, the symbolic systemenables whatever collection of subsumption modules itdeems appropriate for the first segment and parameterizestheir operation in appropriate ways. The symbolic systemdoes not need to constantly fiddle with the subsumptionlayer, it only has to reconfigure the layer when specificevents occur. In our navigation example, this typicallyhappens when the robot has reached the end of one pathsegment and needs to have its course altered to put it onthe next segment. In this case, the alteration ofsubsumption parameters must be swift or the robot willovershoot the intersection. To relieve this real-time burdenfrom the symbolic system we create a Òcontingency table"such as shown in figure 3 (in pseudo-LISP code). Thisstructure is similar to the Òevent dispatch" clauses in theoriginal subsumption architecture [2].(do-until-return(setq recognizers (check-situations))(cond ((and (near-distance? recognizers)(aligned-okay? recognizers)(left-opening? recognizers))(inc-heading! 90)(new-travel! 564)(return recognizers))((beyond-distance? recognizers)(inc-heading! 180)(new-travel! 48)(return recognizers))((no-progress? recognizers)(disable! stay-aligned)(enable! scan-for-escape)(return recognizers))(t nil)))Figure 3 - The Òcontingency table" continuously monitors acollection of special-purpose situation recognizers. When aspecified conjunction occurs, this Òevent" causes a new set ofpermissions and parameters to be passed to the subsumptionsystem. After this, the symbolic system builds a new table.The contingency table allows the symbolic system tospecify a number of events and what response to make ineach case. As suggested by the code, this contingency tablemodule continually checks the status of a number ofspecial-purpose situation recognizers. When one of thelisted conjunctions occurs, the module performs thespecified alterations to the subsumption controller thenreturns the triggering condition to the symbolic system. Iftwo or more events occur simultaneously, the action forthe one listed first is taken. After this one burst of activitythe old contingency table is flushed and the symbolicsystem is free to load an entirely new table.For the navigation application, the contingency tableincludes a check for an opening in the correct direction atan appropriate path displacement, along with commands toreset the orientation and travel distances for the next pathsegment (see figure 3). Notice that the robot also checks tomake sure it is aligned with the average heading when itobserves an IR opening to the left. This is to keep therobot from mistakenly triggering the next subsumptionconfiguration just because the IR signal happened tovanish as the robot was veering around some obstacle inits path.3: Experimental resultsOur first experiment was aimed at validating the claim thatenvironmentally constrained odometry allows us to solvethe loop navigation problem. For this we provided therobot with a rough path to follow of the the form: ((travel1turn1) (travel2 turn2) ...). Travel was specified as anintegral number of inches and turns were specified as anintegral number of degrees. The top half of figure 4 showsthe path the robot took according to its odometry. Thecircles with small projections indicate where the robotobserved openings in the directions indicated. The circlesare the same size as the robot (12 inches diameter) toprovide scale. Notice that neither of the loops appears to beclosed. Based on this information it is questionablewhether the corridor found in the middle of the map is thesame one which the robot later traversed.The symbolic map, which appears in the lower half offigure 4, correctly matches the important intersections toeach other. In this map, nodes are offset slightly to the sideof the path segments. Each node's type is denotediconically as a corner Ð two short lines indicating thedirection of the opening and the direction of free travel.This symmetry reflects the fact that the robot is likely toperceive the same corner when coming out through themarked aperture. Corner nodes are placed at the robotposition where they are sensed; no adjustment is made forthe width of the corridor. This is partially compensated bya wide tolerance in matching nodes.When a new opening is detected the robot compares itwith all the other nodes in the map that have similaropening directions. If there is a match which is no morethan 6 feet from the robot's current position in either x ory, the new opening is considered to be a sighting of the oldnode. In this case we average the positions of the robot andthe node and move both to this new location. Thismerging operation is why the corridors do not look
perfectly straight in the symbolic map. However, when therobot is instructed to follow the same path a second timeand update the map, the changes are minimal.large displacements. However, the local details arequalitatively correct (some of the angularity is due to thetime lag between successive position readings). Ondifferent runs we started the robot in slightly differentdirections. We also altered the positions of variousobstacles along the path and changed the states of some ofthe doors on the corridor. This lead to slightly differentforms for each of the runs. Despite these variations ininitial heading and the configuration of the environment,the symbolic system was able to successfully navigate therobot to its goal in all 5 runs.Figure 5 - These traces illustrate the odometrically perceivedpath of the robot on 5 consecutive runs along the same path.Notice that the wiggles along each segment are different sinceobstacles were moved and doors were altered between runs. Tothe symbolic system, however, all runs seemed identical.Figure 4 - Geometric constraints of the environment allowthe robot to build maps with loops despite poor absoluteodometry. The top picture shows the path the robot thought ittook. The hair-like projections indicate openings it sensed.The lower picture shows the map the robot built during thisexploration.The second experiment shows that the subsumptionsystem is sufficiently competent at local navigation toallow the use of a coarse geometric map. In thisexperiment we had the symbolic system plan a path (fromnode 10 to node 5) using the map it generated in the firstpart. We then told it to configure the two lower layers ofthe architecture to follow this path. The result of fiveconsecutive runs is shown in figure 5. The displayed pathsare based on the robot's odometry and hence do notaccurately reflect the robot's true position in space over4: DiscussionThe SSS architecture is related to a number of recentprojects in robot control architectures. The greatest degreeof similarity is with the ATLANTIS system developed atJPL [11, 9]. In this system there is a subsumption-styleÒcontrol" layer, an operating system-like Òsequencing"layer, and a model-building Òdeliberative" layer. As in ournavigation scheme, this system delegates the task offollowing a particular path segment control to a behaviorbasedsystem while using a rough topological map tospecify the turns between segments. A mobile robotdeveloped at Bell labs [15] also uses this same taskdecomposition as does the AuRA system [1] fromUMASS Amherst.However, in ATLANTIS (and in RAPs [8], itsintellectual predecessor) each of the behaviors in the
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