The Right Path: Comprehensive Path Planning for Lunar Exploration Rovers

Sutoh, Masataku; Otsuki, Masatsugu; Wakabayashi, Shigeo; Hoshino, Tetsuya; Hashimoto, Tatsuaki

doi:10.1109/mra.2014.2381359

Cited by 48 publications

(22 citation statements)

References 12 publications

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“…At present, the research on the global path planning of lunar rover mostly considers the influence of terrain conditions, illumination conditions, communication conditions and the constraints of rover body, and generates the global path with specified resolution in the specified area according to the optimal cost strategy. Masataku et al [ 7 ] present a comprehensive path-planning method for lunar rover, the constraints of lunar rover’s internal and external elements were considered respectively, and the influence of their different weights on the path was analyzed. According to the different motion characteristics of tracked and wheeled lunar rover, the planned path was evaluated.…”

Section: Related Workmentioning

confidence: 99%

“…

where

and

are the real-time pitch and roll attitude angles of the lunar rover,

and

are the maximum safe attitude angle of the lunar rover which is limited to ensure the safety of the autonomous exploration of the lunar rover.

is the lunar rover slip rate, and

is the lunar rover slip angle, which can be defined as a function of the attitude angle of the lunar rover according to the literature [ 7 ], as shown in Equation (6):

where A , B , C , D is a constant determined by the motion behavior of the lunar rover on a given terrain. According to the experimental data in the literature [ 7 ], for the wheeled lunar rover, the constants are as follows: A = 0.07, B = 0.10, C = 1.32, D = 0.16.…”

Section: Deep Reinforcement Learningmentioning

confidence: 99%

“…), lighting conditions, communication conditions and rover body constraints, and meets the set global optimization index (such as the shortest path, the least energy, etc.) [ 7 , 8 , 9 ]. The current global planning method has a high computational complexity to find the optimal solution, and due to the limitation of the resolution of the existing lunar map, it can only provide global guidance for the lunar rover.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…

where

and

are the real-time pitch and roll attitude angles of the lunar rover,

and

are the maximum safe attitude angle of the lunar rover which is limited to ensure the safety of the autonomous exploration of the lunar rover.

is the lunar rover slip rate, and

is the lunar rover slip angle, which can be defined as a function of the attitude angle of the lunar rover according to the literature [ 7 ], as shown in Equation (6):

Section: Deep Reinforcement Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhang

2021

Sensors

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show abstract

“…In addition, the solar position changes over a cycle of 29.5 days and differs according to the latitude and longitude [31][32][33]. Thus, the solar position can be estimated using the solar elevation angle α s and azimuth angle β s , and is derived as a function of the latitude ϕ, declination δ, and hour angle ω.…”

Section: Power Acquisition Modelmentioning

confidence: 99%

Multidisciplinary Design Optimization for a Solar-Powered Exploration Rover Considering the Restricted Power Requirement

Kim

2020

Energies

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The energy requirements of a solar-powered exploration rover constrain the mission duration, traversability, and tractive capability under the given limited usable power. Thus, exploration rover design, more specifically, rover wheel design (related to considerable energy consumption in driving), plays a significant role in the success of exploration missions. This paper describes the modeling of an operational environment and a multi-body dynamics (MBD) simulation tool based on wheel-terrain interaction model to predict the dynamic behavior on a digital elevation model (DEM) map. With these simulation environments, a multidisciplinary optimal wheel design methodology, integrating the MBD simulation tool and non-dominated sorting genetic algorithm-II (NSGA-II), is developed. Design parameters are chosen through sensitivity analysis. These multi-objective optimizations in dynamic states are conducted to obtain the optimal wheel dimension that meet the limited power condition with maximal tractive capability under the given operational environment. Furthermore, numerical and experimental verification using a single wheel testbed on lunar simulant are conducted to convincingly validate the derived optimization results. Finally, these results reveal that the proposed design methodology is an effective approach to deciding the best design parameter among a large variety of candidate design points considering the restricted power requirement.

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“…The difficulty of path tracking lies in the complicated working environment and the agricultural machinery can difficulty exercise sound judgment. At present, the autonomous driving of small agricultural transport vehicles has made important progress in the indoor field [1][2][3][4][5][6][7][8] . However, affected by natural environment (slope, ground obstacle, surface condition, climate condition) and other factors, outdoor agricultural transport vehicles are difficult to ensure the accuracy and robustness of trajectory tracking [9] .…”

Section: Introduction mentioning

confidence: 99%

Trajectory tracking control of agricultural vehicles based on disturbance test

Liu

Zheng

Wang

et al. 2020

International Journal of Agricultural and Biological Engineering

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To improve the trajectory tracking robust stability of agricultural vehicles, a path tracking control method combined with the characteristics of agricultural vehicles and nonlinear model predictive control was presented. Through the proposed method, the path tracking problem can be divided into two problems with speed and steering angle constraints: the trajectory planning problem, and the trajectory tracking optimization problem. Firstly, the nonlinear kinematics model of the agricultural vehicle was discretized, then the derived model was inferred and regarded as the prediction function plant for the designed controller. Second, the objective function characterizing the tracking performance was put forward based on system variables and control inputs. Therefore, the objective function optimization problem, based on the proposed prediction equation plant, can be regarded as the nonlinear constrained optimization problem. What's more, to enhance the robust stability of the system, a real-time feedback and rolling adjustment strategy was adopted to achieve optimal control. To validate the theoretical analysis before, the Matlab simulation was performed to investigate the path tracking performance. The simulation results show that the controller can realize effective trajectory tracking and possesses good robust stability. Meanwhile, the corresponding experiments were conducted. When the test vehicle tracked the reference track with a speed of 3 m/s, the maximum lateral deviation was 13.36 cm, and the maximum longitudinal deviation was 34.61 cm. When the added horizontal deviation disturbance Yr was less than 1.5 m, the controller could adjust the vehicle quickly to make the test car return to the reference track and continue to drive. Finally, to better highlight the controller proposed in this paper, a comparison experiment with a linear model predictive controller was performed. Compared to the conventional linear model predictive controller, the horizontal off-track distance reduced by 36.8% and the longitudinal deviation reduced by 32.98% when performing circular path tracking at a speed of 3 m/s.

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The Right Path: Comprehensive Path Planning for Lunar Exploration Rovers

Cited by 48 publications

References 12 publications

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

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

Multidisciplinary Design Optimization for a Solar-Powered Exploration Rover Considering the Restricted Power Requirement

Trajectory tracking control of agricultural vehicles based on disturbance test

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