A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor †
Abstract
:1. Introduction
- (1)
- The presented collision-avoidance method only uses a depth sensor for collision-avoidance and does not need any information about the geometry of the obstacles.
- (2)
- By using the novel Euclidean distance field-mapping and collision-detection approach, the computing time of the collision-avoidance maneuver can be reduced to a range of 10.4 ms and 40.4 ms that is less than the computing time of the state-of-the-art algorithms.
- (3)
- In the simulation experiments, the proposed collision-avoidance strategy can fly the robot at 2.4 m/s in challenging dynamic environments with an 82% success rate across 100 repetitive tests. The algorithm can also achieve a 95% success rate of 100 repetitive tests in a challenging static environment with a flight speed of 1.96 m/s.
2. Problem Statement
3. Depth-Based Euclidean Distance Field Construction
3.1. Building the Obstacle Field Using the Depth Sensor
3.2. Transforming the Obstacle Field to the Euclidean Distance Field
4. Collision-Avoidance Using Euclidean Distance Fields and Rapidly-Exploring Random Tree
4.1. Euclidean Distance Field-Based Collision Detection
4.2. Collision-Avoidance Planning and Replanning
5. Algorithm Complexity
6. Experiments and Results
6.1. Experimental Setup
6.2. Simulation Experimental Results
6.3. Real Flight Experimental Results
7. Discussions
7.1. Robustness of the Proposed Collision-Avoidance Algorithm
7.2. Computing Efficiency of the Proposed Collision-Avoidance Algorithm
7.3. Flight Speeds in Real Flights vs. Flight Speeds in Simulations
7.4. Fail Cases in Simulation Experiments
8. Conclusions and Future Works
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Acronym | Definition |
---|---|
MAV | Micro Aerial Vehicle |
EDF | Euclidean Distance Field |
OFM | Only Forward Maneuver |
IMU | Inertial Measurement Unit |
EDT | Euclidean Distance Transform |
RRT | Rapid Random Tree |
ROS | Robot Operating System |
DDPG | Deep Deterministic Policy Gradient |
NAF | Normalized Advantage Function |
Algorithm | PL (m) | TG (s) | AV (m/s) | MV (m/s) | SR (%) |
---|---|---|---|---|---|
OFM_HIGH | 10.20 ± 1.85 | 16.03 ± 5.81 | 0.62 ± 0.02 | 2.44 ± 0.19 | 82 |
OFM_LOW | 10.44 ± 2.22 | 17.53 ± 3.50 | 0.59 ± 0.01 | 1.72 ± 0.21 | 85 |
DDPG [1] | 6.88 ± 0.81 | 16.3 ± 3.49 | 0.42 ± 0.007 | 1.06 ± 0.20 | 81 |
NAF [28] | 7.57 ± 0.77 | 15.75 ± 2.88 | 0.48 ± 0.05 | 1.07 ± 0.06 | 87 |
Algorithm | PL (m) | TG (s) | AV (m/s) | MV (m/s) | SR (%) |
---|---|---|---|---|---|
OFM | 4.8731 ± 1.1098 | 18.53 ± 11.78 | 0.28 ± 0.02 | 1.96 ± 0.05 | 95 |
Authors | Comp. Time (ms) | Authors | Comp. Time (ms) |
---|---|---|---|
Liu et al. [13] | >160 | Burri et al. [14] | >40 |
Chen et al. [15] | >34 | Tordesillas et al. [16] | >25 |
Chen et al. [17] | ≥18 | Ryll et al. [18] | ≈30 |
This paper | 25.4 ± 15 (>10.4) |
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Lu, L.; Carrio, A.; Sampedro, C.; Campoy, P. A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor. Remote Sens. 2021, 13, 1796. https://doi.org/10.3390/rs13091796
Lu L, Carrio A, Sampedro C, Campoy P. A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor. Remote Sensing. 2021; 13(9):1796. https://doi.org/10.3390/rs13091796
Chicago/Turabian StyleLu, Liang, Adrian Carrio, Carlos Sampedro, and Pascual Campoy. 2021. "A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor" Remote Sensing 13, no. 9: 1796. https://doi.org/10.3390/rs13091796