Project 5.18 Probability of Mobility for Mission Planning of Autonomous Ground Vehicles at “High Stress” Environments

2021 ◽  
Author(s):  
Zissimos Mourelatos ◽  
Zhen Hu
Keyword(s):  
High Stress ◽  
Mission Planning ◽  
Ground Vehicles ◽  
Autonomous Ground Vehicles ◽  
Stress Environments

Reliability-Based Mission Planning of Off-Road Autonomous Ground Vehicles Using an Outcrossing Approach

2021 ◽  
Author(s):  
Chen Jiang ◽  
Yixuan Liu ◽  
Zhen Hu ◽  
Zissimos P. Mourelatos ◽  
David Gorsich ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Gaussian Random Field ◽  
Mobility Model ◽  
Outcrossing Rate ◽  
Mission Planning ◽  
Ground Vehicles ◽  
Practical Applications ◽  
Autonomous Ground Vehicles ◽  
Uncertainty Sources

Abstract Reliability-based mission planning aims to identify an optimal path for off-road autonomous ground vehicles (AGVs) under uncertain terrain environment, while satisfying specific mission mobility reliability (MMR) constraints. The evaluation of MMR during path planning poses computational challenges for practical applications. This paper presents an efficient reliability-based mission planning using an outcrossing approach that has the same computational complexity as deterministic mission planning. A Gaussian random field is employed to represent the spatially dependent uncertainty sources in the terrain environment. The latter are then used in conjunction with a vehicle mobility model to generate a stochastic mobility map. Based on the stochastic mobility map, outcrossing rate maps are generated using the outcrossing concept which is widely used in time-dependent reliability. Integration of the outcrossing rate map with a rapidly-exploring random tree (RRT*) algorithm, allows for efficient path planning of AGVs subject to MMR constraints. A reliable RRT* algorithm using the outcrossing approach (RRT*-OC) is developed to implement the proposed efficient reliability-based mission planning. Results of a case study verify the accuracy and efficiency of the proposed algorithm.

Efficient Reliability-Based Mission Planning of Off-Road Autonomous Ground Vehicles Using an Outcrossing Approach

2021 ◽  
pp. 1-45
Author(s):  
Chen Jiang ◽  
Yixuan Liu ◽  
Zissimos P. Mourelatos ◽  
David Gorsich ◽  
Yan Fu ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Gaussian Random Field ◽  
Mobility Model ◽  
Outcrossing Rate ◽  
Mission Planning ◽  
Ground Vehicles ◽  
Practical Applications ◽  
Autonomous Ground Vehicles ◽  
Uncertainty Sources

Abstract Reliability-based mission planning aims to identify an optimal path for off-road autonomous ground vehicles (AGVs) under uncertain terrain environment, while satisfying specific mission mobility reliability (MMR) constraints. The evaluation of MMR during path planning poses computational challenges for practical applications. This paper presents an efficient reliability-based mission planning using an outcrossing approach that has the same computational complexity as deterministic mission planning. A Gaussian random field is employed to represent the spatially dependent uncertainty sources in the terrain environment. The latter are then used in conjunction with a vehicle mobility model to generate a stochastic mobility map. Based on the stochastic mobility map, outcrossing rate maps are generated using the outcrossing concept which is widely used in time-dependent reliability. Integration of the outcrossing rate map with a rapidly-exploring random tree (RRT*) algorithm, allows for efficient path planning of AGVs subject to MMR constraints. A reliable RRT* algorithm using the outcrossing approach (RRT*-OC) is developed to implement the proposed efficient reliability-based mission planning. Results of a case study with two scenarios verify the accuracy and efficiency of the proposed algorithm.

Fuzzy Control for Autonomous Ground Vehicles

2000 ◽  
Author(s):  
Jeffrey S. Wit ◽  
Carl D. Crane ◽  
Armstrong III ◽  
II David G.
Keyword(s):  
Fuzzy Control ◽  
Ground Vehicles ◽  
Autonomous Ground Vehicles

scholarly journals Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hajira Saleem ◽  
Faisal Riaz ◽  
Leonardo Mostarda ◽  
Muaz A. Niazi ◽  
Ammar Rafiq ◽  
...  
Keyword(s):  
Ground Vehicles ◽  
Steering Angle ◽  
Autonomous Ground Vehicles ◽  
Prediction Techniques

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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