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

Bai, Chengchao; Guo, Jifeng; Guo, Linli; Song, Junlin

doi:10.3390/s19143102

Cited by 38 publications

(20 citation statements)

References 45 publications

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“…Chengchao Bai [40] also addressed the issue of terrain identification based on multi-sensor fusion. The authors classified terrain into five types; brick, sand, flat, cement, and soil.…”

Section: Terrain Classification Without Visual Perceptionmentioning

confidence: 99%

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Nampoothiri

Sunny

et al. 2021

SN Appl. Sci.

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The work presents a review on ongoing researches in terrain-related challenges influencing the navigation of Autonomous Robots, specifically Unmanned Ground ones. The paper aims to highlight the recent developments in robot design and advanced computing techniques in terrain identification, classification, parameter estimation, and developing modern control strategies. The objective of our research is to familiarize the gaps and opportunities of the aforementioned areas to the researchers who are passionate to take up research in the field of autonomous robots. The paper brings recent works related to terrain strategies under a single platform focusing on the advancements in planetary rovers, rescue robots, military robots, agricultural robots, etc. Finally, this paper provides a comprehensive analysis of the related works which can bridge the AI techniques and advanced control strategies to improve navigation. The study focuses on various Deep Learning techniques and Fuzzy Logic Systems in detail. The work can be extended to develop new control schemes to improve multiple terrain navigation performance.

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“…Chengchao Bai [40] also addressed the issue of terrain identification based on multi-sensor fusion. The authors classified terrain into five types; brick, sand, flat, cement, and soil.…”

Section: Terrain Classification Without Visual Perceptionmentioning

confidence: 99%

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Nampoothiri

Sunny

et al. 2021

SN Appl. Sci.

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“…So local path planning is needed to deal with the dynamic unknown environment of the lunar surface. The local path planning is based on the on-board sensor system of the rover to perceive the surrounding lunar environment in real time, reconstruct the terrain and detect obstacles in the area, and then plan a safe obstacle avoidance trajectory that meets the dynamic constraints of the lunar rover [ 10 , 11 , 12 ]. However, local path planning requires precise reconstruction and sensing of the surrounding environment, and can only achieve very low-speed lunar rover real-time planning due to the processing power limitation of the lunar on-board computer.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints

Wang

Zhang

2021

Sensors

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Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a training environment integrating real lunar surface terrain data was built using the Gazebo simulation environment and a lunar rover simulator was created in it to simulate the real lunar surface environment and the lunar rover system. Then an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization. In addition, to improve the generalization ability to different lunar surface topography and different scale environments, a variety of training scenarios were set up to train the network model using the idea of curriculum learning. The simulation results show that the proposed planning algorithm can successfully achieve the end-to-end path planning of the lunar rover, and the path generated by the proposed algorithm has a higher safety guarantee compared with the classical path planning algorithm.

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“…Then, a long–short-term memory network based on one-dimensional convolution was proposed as a terrain classification method. Bai et al 19 proposed a terrain classification method based on multilayer perceptual deep neural networks, which has significantly improved accuracy compared with traditional backpropagation neural network classification. Concurrently, they also proposed that in different terrains, different robot speeds will affect the accuracy of terrain classification.…”

Section: Introductionmentioning

confidence: 99%

Adaptive online terrain classification method for mobile robot based on vibration signals

Wang

Sun

2021

International Journal of Advanced Robotic Systems

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To improve the accuracy of terrain classification during mobile robot operation, an adaptive online terrain classification method based on vibration signals is proposed. First, the time domain and the combined features of the time, frequency, and time–frequency domains in the original vibration signal are extracted. These are adopted as the input of the random forest algorithm to generate classification models with different dimensions. Then, by judging the relationship between the current speed of the mobile robot and its critical speed, the classification model of different dimensions is adaptively selected for online classification. Offline and online experiments are conducted for four different terrains. The experimental results show that the proposed method can effectively avoid the self-vibration interference caused by an increase in the robot’s moving speed and achieve higher terrain classification accuracy.

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

Cited by 38 publications

References 45 publications

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints

Adaptive online terrain classification method for mobile robot based on vibration signals

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