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Title Page

Contents

Abstract 12

Chapter 1. Introduction 14

Chapter 2. Related Work 20

Chapter 3. Semantic Knowledge Modeling for Multi-robot System 29

3.1. Semantic Knowledge for Advanced Robot System 29

3.2. Semantic Knowledge Modeling for Multi-robot System 32

3.2.1. Relation Knowledge Modeling 32

3.2.2. TOSM-based Environment Element Modeling 36

3.3. Reasoning Rule Modeling for Multi-robot System 41

Chapter 4. Semantic Knowledge-based Hierarchical Task Modeling 46

4.1. Hierarchy Task Modeling with Semantic Knowledge 46

4.1.1. Task Modeling for Hierarchical Planning Structure 46

4.1.2. PDDL Representation of Hierarchy Tasks 49

Chapter 5. Hierarchical Semantic AI Planning for Multi-robot Task Execution 60

5.1. Overview 60

5.2. Hierarchical Planning Approach for Multi-robot 65

5.2.1. Task Planning 65

5.2.2. Task Re-planning 73

5.2.3. TOSM-based Autonomous Navigation 76

Chapter 6. Experiment 89

6.1. Experimental Environments 90

6.2. Experimental Scenarios 91

6.2.1. Semantic Knowledge-based Multi-robot Planning 91

6.2.2. Multi-robot Mission Planning 94

6.3. Experimental Results 98

6.3.1. Semantic Knowledge Test 98

6.3.2. Multi-robot Mission Planning 110

6.4. Applications 128

6.4.1. Warehouse Scenario 129

6.4.2. Campus Scenario 133

6.4.3. ACS Scenario 141

Chapter 7. Conclusion 148

References 152

논문요약 157

List of Tables

Table 1. Comparison for AI planning based robot system. 22

Table 2. Object properties. 34

Table 3. Data properties. 37

Table 4. Example of the task list defined by the hierarchy. 48

Table 5. Representation of Delivery task with PDDL. 51

Table 6. Representation of Guidance task with PDDL. 51

Table 7. Representation of Move task with PDDL. 52

Table 8. Representation of MoveSame task with PDDL. 53

Table 9. Representation of Take task with PDDL. 55

Table 10. Representation of Give task with PDDL. 55

Table 11. Representation of GoToPlace task with PDDL. 57

Table 12. Representation of PickUp task with PDDL. 57

Table 13. Feature matching algorithm pseudo code. 83

Table 14. Connection zone detecting algorithm pseudo code. 87

Table 15. Experimental cases of multi-robot delivery mission. 96

Table 16. Comparison of task planning results with and without knowledge. 108

Table 17. Experimental results of multi-robot task planning. 117

Table 18. Experimental results of multi-robot task re-planning. 124

Table 19. Re-planned behavior sequence with the centralized system. 126

Table 20. Re-planned behavior sequence with the proposed system. 127

Table 21. Planned behavior sequence for logistic scenario. 130

Table 22. Designed specifications of virtual robots. 135

Table 23. Case of designed delivery tasks. 136

Table 24. Representation of task allocation action with PDDL. 143

Table 25. Representation of Charging task with PDDL. 143

Table 26. Representation of Unload coarse-level task with PDDL. 144

Table 27. Representation of Unload fine-level task with PDDL. 144

List of Figures

Figure 1. Triplet Ontological Semantic Model (TOSM) 29

Figure 2. Classification of environments. 31

Figure 3. Comparison of when object properties activate and deactivate during the navigation. (a)When "isLocatedAt" property is activated/deactivated by... 35

Figure 4. Hierarchical relationships of Place in the semantic database. 39

Figure 5. Place & Task Hierarchy Structure. 47

Figure 6. Extending PDDL actions from the prime action "GoTo" 58

Figure 7. Semantic Knowledge-based Multi-robot System Architecture. 60

Figure 8. Semantic Navigation Framework for Multi-robot System 62

Figure 9. Schematic diagram of task planning structure. (a) Centralized Planning Structure. (b) Proposed hybrid structure. 63

Figure 10. Semantic Knowledge-based Hierarchical Task Planning Structure. 65

Figure 11. Example of the multi-robot working environment. 66

Figure 12. Semantic domain-based coarse goal generating step. 67

Figure 13. Coarse-level task-based multi-robot coarse planning step to perform coarse goal lists. 68

Figure 14. Plan restructuring step to group duplicate robots working in the same place at the same time. 70

Figure 15. Fine planning steps to create detailed behavioral sequences for each robot group. 71

Figure 16. Visualization of fine planning results for Group1. 72

Figure 17. Re-planning method in centralized planning approach. 73

Figure 18. Case of re-planning with specific robot group in proposed planning approach. 74

Figure 19. Case of re-planning with all robots in proposed planning approach. 75

Figure 20. System flow diagram of robot. 76

Figure 21. Predicting motion based on robot speed. 79

Figure 22. Differential type robot. 80

Figure 23. Tricycle type robot. 80

Figure 24. Omnidirectional type robot. 81

Figure 25. Simulation environments. (a) Whole driving place. (b) Cross-shaped stem-place "area" . (c) Square-shaped leaf-place "corridor" . (d) Robot... 90

Figure 26. Semantic knowledge test scenario. (a) Crossover situation. (b) Multiple agents charging situation. (c) Different boxes delivery scenario. 92

Figure 27. Example environment case of delivery task. 94

Figure 28. Task execution failure case. (a) Robot path blocking case (b) Robot breakdown case. 97

Figure 29. Planning result without semantic knowledge. (a) Multi-robot task planning result. (b) Task implementing result with multi-robot. 99

Figure 30. Multi-robot planning results with knowledge of "occupancy" . (a) Multi-robot task planning result. (b) Task implementing result with multi-robot. 101

Figure 31. Planned behavior sequences without knowledge of "usage" . (a) Planned sequence with short charging time. (b) Planned sequence with long... 102

Figure 32. Planned behavior sequences with knowledge of "usage" . (a) Planned sequence with short charging time. (b) Planned sequence with long... 104

Figure 33. Planned behavior sequence without task knowledge. 105

Figure 34. Planned behavior sequence with task knowledge. (a) When a specific item is over the robot's payload limit. (b) When all items within the robot's... 106

Figure 35. Operation of the multi-robot system for delivery missions. (a) Execution of delivery missions. (b) Completion of delivery missions. 111

Figure 36. Planned behavior sequence with the centralized structure. 112

Figure 37. Coarse planning result with the proposed structure. 114

Figure 38. Planned task sequences of each group with the proposed structure. (a) Group1 planning result. (b) Group2 planning result. 115

Figure 39. Graph of used place instance quantity for multi-robot task planning (a) Test results with 2 robots. (b) Test results with 3 robots. (c) Test results... 119

Figure 40. Graph of required time for multi-robot task planning. (a) Test results with 2 robots. (b) Test results with 3 robots. (c) Test results with 4 robots. 120

Figure 41. Re-planning in the path blocking case. (a) Path-blocking situation. (b) Driving a detour route. (c) Completing the delivery mission. 121

Figure 42. Re-planning in the robot breakdown case. (a) When a robot breakdown occurred. (b) Re-placing another robot to accomplish the mission.... 122

Figure 43. Logistics scenario environment. (a) Identified areas of the environment (b) Visualized semantic map. 129

Figure 44. The part of planned behavior sequence for logistic scenario. 132

Figure 45. Logistic task execution based on proposed multi-robot system. 132

Figure 46. Test environment with various things and complex places. 134

Figure 47. Semantic map of campus. 135

Figure 48. Semantic TOSM instances in the campus environments. 136

Figure 49. Generated semantic relationship knowledge of each delivery task. (a) Generated "canPickup" knowledge for the Case1. (b) Generated... 137

Figure 50. Delivery task planning results. (a) Planned behavior sequences in the Case1. (b) Planned behavior sequences in the Case2. 138

Figure 51. Semantic knowledge-based multi-robot delivery task execution. (a) Navigating the robot for each task assigned in the Case 1. (b) Pick up a box... 140

Figure 52. ACS scenario environment. (a) Defined places for robots to operate. (b) Grouped areas based on where the robot is working. 142

Figure 53. Planned coarse-level tasks in ACS scenario. 146

Figure 54. Planned fine-level tasks in ACS scenario. 147

초록보기

 여러 로봇을 병렬로 배치하여 작업을 할당하고 협업을 통해 임무를 수행하는 멀티 로봇 시스템이 다양한 분야에서 점점 더 많이 활용되고 있습니다. 이러한 시스템은 임무 계획 시 개별 로봇의 움직임과 전체 로봇의 영향을 동시에 고려해야 하는 복잡한 문제를 해결해야 합니다. 이러한 과제를 해결하기 위해 연구자들은 다중 로봇 작업을 모델링하고 계획하기 위한 다양한 시스템을 제안했습니다. 특히 환경 요인 및 도메인 규칙과 같은 시맨틱 지식의 도입으로 고수준 임무 계획을 위한 고도화된 방식이 제시되기도 했습니다.

본 논문에서는 시맨틱 지식 기반 고수준의 동작을 수행하는 다중 로봇 시스템을 제안합니다. 환경 요소 간의 영향과 상호작용을 고려하여 다중 로봇 시스템의 의미론적 지식을 정의합니다. 여러 로봇이 작동하는 환경에서 중복을 피하기 위해 공간 점유 및 객체 소유권과 같은 환경 요소 간의 관계에 대한 지식을 표현하고 이 지식을 포괄하는 작업과 작업을 모델링합니다. 또한 각 공간의 계층적 정보를 표현하기 위해 지식 속성을 정의합니다. 작업 플래너는 제안된 시맨틱 지식과 규칙을 활용하여 공간 계층적 지식을 활용하고 로봇을 그룹화하여 각 그룹에 대한 최적의 작업 계획을 생성합니다. 이러한 접근 방식을 통해 여러 로봇 간의 중복 및 교착 문제를 해결하면서 복잡한 임무를 효율적으로 계획할 수 있습니다. 실험을 통해 제안한 시맨틱 지식의 타당성을 검증하고 시뮬레이션 환경에서 태스크 플래너가 계획 시간을 단축할 수 있음을 입증했습니다. 마지막으로 제안하는 시스템을 응용하여 다중 로봇의 고수준 미션을 수행하여 제안하는 의미지식 기반 다중로봇 시스템의 실용성을 검증했습니다.

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