Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (4): 771-784.doi: 10.23919/JSEE.2022.000077
• CLOUD CONTROL SYSTEMS • Next Articles
Weijian PANG1,2(), Hui LI1(
), Xinyi MA1,3(
), Hailin ZHANG1(
)
Received:
2022-02-14
Online:
2022-08-30
Published:
2022-08-30
About author:
Supported by:
Weijian PANG, Hui LI, Xinyi MA, Hailin ZHANG. A semantic-centered cloud control framework for autonomous unmanned system[J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 771-784.
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Table 3
SWRL rules in scene understanding and situation awareness"
Number | Rule body | Description |
Rule#1 | Uxv(?x)^ isIdle(?x, ?y)^ swrlb: booleanNot(?y, false)-> UxvAvailable(?x) | Infer the availability of vehicles |
Rule#2 | UxvAvailable (?x)^ hasEndurance(?x, ?y)^ swrlb: greaterThan(?y, 80) →UxvEndurance (?x, HIGH) | Duration time inference |
Rule#3 | BaseStation(?bs)^ hasPosition(?bs, ?pos1) ^Uxv(?uxv)^ hasPosition(?uxv, ?pos2)^ hasRemainEndurance (?uxv, ?re)^ swrlb:divide(?pos1, ?pos2, ?dis)^ swrlb:lessThan(?dis, ?re) → Alert(?alert)^ hasUxv(?alert, ? uxv)^ hasAlertType(?alert, INSUFFICIENT_ENDURANCE) | Infer low endurance alert event |
Rule#4 | Uxv(?uxv)^ Entity(?entity)^ inFrontOf(?entity, ?uxv) ^ →Obstacle(?entity) | Infer obstacle type |
Rule#5 | Tree(?tree)^ hasHight(?tree,?hight) ^hasWidth(?tree,?width)^ swrlb:divide(?rate, ?width, ?hight)^ swrlb:greatThan(?rate, 4) →hasState(?tree, FALLEN) | Infer entity state |
Rule#6 | Tree(?tree)^ hasHight(?tree,?hight) ^hasWidth(?tree,?width)^ hasDensity(?tree, ?density) ^ swrlb: multiply (?mass, ?width, ?width, ?hight, ?density) →hasMass(?tree, ?mass) | Estimate the mass of the tree |
Rule#7 | Tree(?tree)^ on(?tree, ?road)^ Road(?road)^ hasWidth(?tree, ?wk)^ hasWidth(?road, ?wd)^swrlb: divide(?wd, 2)^ swrlb:greaterThan(?wk, ?wd) →hasState(?road, BLOCKED) | Infer the road is blocked or not |
Rule#8 | Carrier(?u)^ Wrecker(?w)^ Road(?r)^ Tree(?tree)^ at(?u, ?r)^ hasState(?r, BLOCKED)^ on(?tree, ?road)^ hasMass(?t, ?m)^ hasMaxLift(?w, ?l)^ swrlb:lessThan(?m, ?l)→hasCandidateTask(?w, OBSTACLE_CLEANNING) | Infer obstacle cleaning task |
Rule#9 | Tree(?tree)^ hasMass(?tree, ?mass)^ Uxv(?uxv)^ hasMaxGrip(?uxv, ?grip)^ swrlb:greaterThan (?grip, ?mass) → hasCandidateUxv(?task, ?uxv) | Infer candidate task executor |
Rule#10 | Carrier(?c)^ ForkLift(?fl)^ Package(?p)^ WarHouse(?r)^ at(?c, ?wh)^ hasTargetPakage(?c, ?p)^ hasMass (?p, ?m)^ hasMaxLift(?fl, ?l)^ swrlb:lessThan(?m, ?l)→hasCandidateTask(?w, LIFT_COOPERATION) | Infer lift cooperative task |
Rule#11 | Carrier(?c)^ Road(?r)^ MaintenanceSign(?ms)^ at(?c, ?r)^ hasState(?r, BLOCKED)^ hasSign (?r, ?ms)→hasCandidateTask(?w, ROUTE_REPLANNING) | Replanning path when the road is blocked since maintenance |
Rule#12 | Carrier(?u)^ Wrecker(?w)^ Road(?r)^ Tree(?tree)^ at(?u, ?r)^ hasState(?r, BLOCKED)^ on(?tree, ?road)^ hasMass(?t, ?m)^ hasMaxLift(?w, ?l)^ swrlb:greaterThan(?m, ?l)→hasCandidateTask(?w, TOUTE_REPLANNING) | Replanning path when the road is blocked by an obstacle |
Rule#13 | Carrier(?c)^Customer(?p)^ Address(?a)^ at(?c, ?a)^ hasAddress(?p, ?a→hasCandidateTask(?c, PLACING_PACKAGE) | Infer placing package action |
Rule#14 | Carrier(?c)^ ForkLift(?fl)^ Package(?p)^ WarHouse(?r)^ at(?c, ?wh)^ hasTargetPakage(?c, ?p)^ hasMass(?p, ?m)^ hasMaxLift(?fl, ?l)^ swrlb:greaterThan(?m, ?l)→Alert(?alert)^ hasUxv(?alert, ?c)^ hasAlertType(?alert, LIFT_COOPERATION_FAILED) | Infer alert of forklift cannot lift a package |
Table 4
Plot summary of the experimental scenario"
Item | Description | Action |
Task name | Delivery task with exception event | — |
Precondition | #1, #2 delivery UGV, #3 wrecker UGV, #4 forklift UGV located at the depot, new package arrives | — |
Event flow | UGV completes navigate autonomously | — |
UGV report to the cloud control center | — | |
#1 or #2 UGV conduct delivery task(Task1) | Query for related information: candidate vehicle, package location, destination location | |
Get package(Action 11) | ||
Navigate to destination(Action 12) | ||
Complete delivery(Action 13) | ||
#3 wrecker conduct Obstacle_cleanning task (Task 2) | Query for related information: Obstacle type, location | |
Navigate to the obstacle (Action 21) | ||
Clean up obstacle (Action 22) | ||
Navigate to the depot (Action 23) | ||
#4 forklift conduct lift cooperative task when needed (Task 3) | Query for related information: Package location | |
Navigate to package (Action 31) | ||
Load package (Action 32) | ||
Navigate to the depot (Action 33) | ||
Exception handling | #1 or #2 UGV delivery task failed since the road blocked | Generate scene graph and report to control center |
According to the inference result, generate a new “obstacle cleaning” task (Task 2) | ||
#1 or #2 UGV delivery task failed since road maintenance | Acquire sensor data and update cloud KB information | |
According to the inference result, replanning the path | ||
#1 or #2 UGV arrive at a warehouse | Acquire sensor data and update cloud KB information | |
According to the inference result, generate a new “Lift_Cooperation” task (Task 3) | ||
#1 or #2 UGV arrives at customer address | According to the inference result, unload the package | |
Effect | Each vehicle completes a designed task and reports to the cloud control center | — |
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