Efficient UAV Exploration for Large-Scale 3D Environments Using Low-Memory Map
Abstract
:1. Introduction
- A novel representation of the environment is presented to provide the essential information for exploration: only obstacle and frontier information are maintained, requiring less onboard memory. And a method is designed to update the representation immediately whenever new environmental data are received.
- A hierarchical exploration approach incorporating the proposed environmental representation is developed to enable fast planning and generate high-quality exploration paths, allowing for quick responses to environmental changes and improving overall exploration efficiency.
- Comprehensive simulations and real-world experiments demonstrate that the proposed environmental representation is memory-efficient, and the proposed exploration method is able to promptly plan efficient exploration paths in large-scale environments.
2. Related Work
2.1. Autonomous Exploration Methods
2.2. Environmental Representation
3. System Overview
4. A 3D Environmental Representation for Exploration
4.1. Representation of FAOmap
4.2. Data Structure of FAOmap
4.3. Updating of FAOmap
- (1)
- After the UAV moves, the local 3D grid map is centered at the UAV’s current position, and all grids in G are reset to the empty state (the real state has yet to be determined).
- (2)
- Based on the coordinates, the frontiers and the occupied grids in the range of are retrieved from FAOmap and stored in their corresponding grids in , as shown in Figure 4b. And then, they are erased in FAOmap.
- (3)
- The grids in are clustered according to their current states (frontier, occupied, and empty). The possible occupancy states of a grid in are frontier, free, occupied, and unknown, as shown in Figure 2c. Since the frontiers and occupied grids are identified in step 2, the empty grids are free or unknown. The real states of empty grids in the same cluster are determined by the indicators of the frontiers or the occupied grids adjacent to . For example, as shown in Figure 4c–e, supposing that along the positive direction of the x-coordinate, there is a frontier v adjacent to the grid b, which is in cluster . After querying the indicator of grid v, whose value is 1, it can be inferred that the state of grid b is known, according to Figure 2d. However, there are only two types of known grids: free and occupied. But occupied grids have already been identified in step (2), so the real state of grid b is free. Then the real states of all grids in are free. Similarly, the real states of all empty grids in other clusters can be determined.
- (4)
- The occupancy states of all grids in are updated based on newly received data using a ray-cast approach, as in Octomap [38].
- (5)
- All new frontiers and occupied grids in are extracted and added to FAOmap.
5. Hierarchical Exploration Approach
5.1. Global Planning
5.1.1. Fast Incremental Frontier Clustering
- (1)
- Find all clusters that currently overlap with the local 3D grid map G. If a cluster falls inside the range of G, then delete it (as with the orange cluster in Figure 5a).
- (2)
- For clusters that intersect with the boundary of G, some frontiers in these clusters may no longer be the frontiers after G is updated by newly perceived data. If the number of remaining frontiers in a cluster is less than the threshold , delete the cluster (as with the green cluster in Figure 5); otherwise, retain clusters that consist of remaining frontiers (as with the blue cluster in Figure 5).
- (3)
- Cluster all frontiers that are not yet clustered, including the new frontiers that appear after G is updated and some frontiers from the deleted clusters.
5.1.2. Super Points for Frontier Clusters
5.1.3. Exploration Routes Finding
5.1.4. Road Map for Path Searching
5.2. Local Refinement
5.2.1. Viewpoint Generation
5.2.2. Observation Path Determination
6. Trajectory Optimization
7. Simulation Experiments
7.1. FAOmap Evaluation
7.1.1. Memory Consumption
7.1.2. Update Efficiency
7.2. Exploration Planning Method Evaluation
7.2.1. Computational Efficiency
7.2.2. Exploration Efficiency
8. Real-World Experiments
9. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Scene | Indoor | Mountain | Village | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Resolution r (m) | 0.1 | 0.3 | 0.5 | 0.1 | 0.3 | 0.5 | 0.1 | 0.3 | 0.5 | ||||||||||
Sensing Range (m) | 5 | 9 | 12 | 5 | 9 | 12 | 5 | 9 | 12 | ||||||||||
metric | total memory usage (MB) | average update time (ms) | total memory usage (MB) | average update time (ms) | total memory usage (MB) | average update time (ms) | total memory usage (MB) | average update time (ms) | total memory usage (MB) | average update time (ms) | total memory usage (MB) | average update time (ms) | total memory usage (MB) | average update time (ms) | total memory usage (MB) | average update time (ms) | total memory usage (MB) | average update time (ms) | |
method | |||||||||||||||||||
Octomap [38] | 1038.9 | 1238 | 18.7 | 107 | 5.0 | 58 | 824.8 | 1176 | 31.8 | 122 | 7.2 | 54 | 1353.8 | 1793 | 44.9 | 184 | 12.5 | 74 | |
Voxblox [39] | 1021.7 | 345 | 69.1 | 143 | 20.8 | 124 | 857.4 | 344 | 105.1 | 160 | 25.2 | 119 | 1754.1 | 332 | 125.3 | 202 | 40.8 | 115 | |
FIESTA [40] | 7695.0 | 261 | 480.9 | 49 | 120.2 | 8 | 7695.0 | 232 | 480.9 | 36 | 240.5 | 11 | 7695.0 | 97 | 961.9 | 38 | 240.5 | 8 | |
UFOmap [41] | 344.0 | 51 | 24.6 | 20 | 5.5 | 14 | 353.9 | 47 | 30.9 | 27 | 8.0 | 15 | 448.2 | 53 | 41.8 | 32 | 12.5 | 14 | |
FAOmap | 64.8 | 112 | 6.9 | 28 | 2.1 | 17 | 76.5 | 117 | 8.0 | 28 | 2.6 | 17 | 120.7 | 114 | 12.1 | 30 | 4.7 | 17 |
FAOmap | Global Planning | Local Planning | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Frontier Clustering | Road Map Updating | Path Planning | Viewpoint Generation | Path Determining | |||||||||
param | r | R | n | l | |||||||||
value | 0.5 m | 10 | 5 | 8 m | 2 m | 6 | 6 m | 100 | 0.1 | 1.0 m | 3 | 3 | 10 |
Scene & Bounding Box For Exploration & Observable Volume | Method | Planning Computation Time (ms) | Total Exploration Time (s) | Total Movement Distance (m) | Average Speed (m/s) | ||
---|---|---|---|---|---|---|---|
avg | std | avg | std | ||||
Indoor | FUEL [31] | 42 | 384.2 | 18.6 | 561.9 | 24.4 | 1.463 |
105 m × 60 m × 8 m | TARE [32] | 694 | 336.7 | 46.0 | 728.1 | 98.4 | 2.162 |
47,084 m3 | proposed | 29 | 205.9 | 16.2 | 494.4 | 35.4 | 2.401 |
Mountain | FUEL [31] | 74 | 412.9 | 44.6 | 579.9 | 67.1 | 1.404 |
100 m × 100 m × 8 m | TARE [32] | 656 | 312.7 | 56.3 | 685.3 | 122.0 | 2.192 |
56,341 m3 | proposed | 20 | 196.8 | 22.2 | 487.2 | 49.2 | 2.476 |
Village | FUEL [31] | 139 | 726.8 | 57.9 | 996.7 | 70.8 | 1.371 |
150 m × 100 m × 8 m | TARE [32] | 749 | 659.2 | 105.6 | 1387.6 | 220.3 | 2.105 |
94,463 m3 | proposed | 24 | 383.9 | 29.5 | 923.2 | 69.9 | 2.405 |
Scene | Explored Time (s) | Movement Distance (m) | Explored Volume in Bounding Box (m3) | FAOmap Memory Usage (KB) | Average Planning Time (ms) |
---|---|---|---|---|---|
garage | 159 | 171 | 4924 | 173 | 19.7 |
forest | 189 | 210 | 17,229 | 610 | 53.5 |
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Huang, J.; Fan, Z.; Yan, Z.; Duan, P.; Mei, R.; Cheng, H. Efficient UAV Exploration for Large-Scale 3D Environments Using Low-Memory Map. Drones 2024, 8, 443. https://doi.org/10.3390/drones8090443
Huang J, Fan Z, Yan Z, Duan P, Mei R, Cheng H. Efficient UAV Exploration for Large-Scale 3D Environments Using Low-Memory Map. Drones. 2024; 8(9):443. https://doi.org/10.3390/drones8090443
Chicago/Turabian StyleHuang, Junlong, Zhengping Fan, Zhewen Yan, Peiming Duan, Ruidong Mei, and Hui Cheng. 2024. "Efficient UAV Exploration for Large-Scale 3D Environments Using Low-Memory Map" Drones 8, no. 9: 443. https://doi.org/10.3390/drones8090443