Uncertainty-Based Vibration/Gyro Composite Planetary Terrain Mapping

Bai, Chengchao; Jifeng, Guo

doi:10.3390/s19122681

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…On the one hand, it can directly obtain information on the state of the unknown environment. On the other hand, it can provide rich environmental information for the planning system and provide support for the path planning and obstacle avoidance of the rover [2,3,4]. Among them, how to effectively classify and identify the terrain is particularly important, as it directly affects the path selection of the rover, and thus determines whether the detection task can be carried out smoothly.…”

Section: Introductionmentioning

confidence: 99%

Deep Multi-Layer Perception Based Terrain Classification for Planetary Exploration Rovers

Bai

Guo

et al. 2019

Sensors

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Accurate classification and identification of the detected terrain is the basis for the long-distance patrol mission of the planetary rover. But terrain measurement based on vision and radar is subject to conditions such as light changes and dust storms. In this paper, under the premise of not increasing the sensor load of the existing rover, a terrain classification and recognition method based on vibration is proposed. Firstly, the time-frequency domain transformation of vibration information is realized by fast Fourier transform (FFT), and the characteristic representation of vibration information is given. Secondly, a deep neural network based on multi-layer perception is designed to realize classification of different terrains. Finally, combined with the Jackal unmanned vehicle platform, the XQ unmanned vehicle platform, and the vibration sensor, the terrain classification comparison test based on five different terrains was completed. The results show that the proposed algorithm has higher classification accuracy, and different platforms and running speeds have certain influence on the terrain classification at the same time, which provides support for subsequent practical applications.

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Section: Introductionmentioning

confidence: 99%

Deep Multi-Layer Perception Based Terrain Classification for Planetary Exploration Rovers

Bai

Guo

et al. 2019

Sensors

Self Cite

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“…The safe and efficient navigation of an unmanned ground vehicle requires the terrain information, which can be exploited to predict the future pose of the vehicle and assess the traversability. The sensors used to obtain the terrain information include visual sensors [1,2,3,4,5], LiDARs [6,7,8,9,10,11,12,13], and so on. Visual sensors can provide various kinds of terrain information, but processing visual data often requires a long computation time.…”

Section: Introductionmentioning

confidence: 99%

Traversability Assessment and Trajectory Planning of Unmanned Ground Vehicles with Suspension Systems on Rough Terrain

Zhang

Yang

et al. 2019

Sensors

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This paper presents a traversability assessment method and a trajectory planning method. They are key features for the navigation of an unmanned ground vehicle (UGV) in a non-planar environment. In this work, a 3D light detection and ranging (LiDAR) sensor is used to obtain the geometric information about a rough terrain surface. For a given SE(2) pose of the vehicle and a specific vehicle model, the SE(3) pose of the vehicle is estimated based on LiDAR points, and then a traversability is computed. The traversability tells the vehicle the effects of its interaction with the rough terrain. Note that the traversability is computed on demand during trajectory planning, so there is not any explicit terrain discretization. The proposed trajectory planner finds an initial path through the non-holonomic A*, which is a modified form of the conventional A* planner. A path is a sequence of poses without timestamps. Then, the initial path is optimized in terms of the traversability, using the method of Lagrange multipliers. The optimization accounts for the model of the vehicle’s suspension system. Therefore, the optimized trajectory is dynamically feasible, and the trajectory tracking error is small. The proposed methods were tested in both the simulation and the real-world experiments. The simulation experiments were conducted in a simulator called Gazebo, which uses a physics engine to compute the vehicle motion. The experiments were conducted in various non-planar experiments. The results indicate that the proposed methods could accurately estimate the SE(3) pose of the vehicle. Besides, the trajectory cost of the proposed planner was lower than the trajectory costs of other state-of-the-art trajectory planners.

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Adaptive cooperative detection method for unmanned planetary vehicles based on deep reinforcement learning

Peng

Xiao

Guo

et al. 2019

2019 IEEE International Conference on Unmanned Systems (ICUS)

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Uncertainty-Based Vibration/Gyro Composite Planetary Terrain Mapping

Cited by 5 publications

References 26 publications

Deep Multi-Layer Perception Based Terrain Classification for Planetary Exploration Rovers

Deep Multi-Layer Perception Based Terrain Classification for Planetary Exploration Rovers

Traversability Assessment and Trajectory Planning of Unmanned Ground Vehicles with Suspension Systems on Rough Terrain

Adaptive cooperative detection method for unmanned planetary vehicles based on deep reinforcement learning

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