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.

scholarly journals An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

2018 ◽  
Vol 2018 ◽  
pp. 1-27 ◽  
Author(s):  
Ben Beklisi Kwame Ayawli ◽  
Ryad Chellali ◽  
Albert Yaw Appiah ◽  
Frimpong Kyeremeh
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Mobile Robot Navigation ◽  
Planning Problem ◽  
Ground Vehicles ◽  
Robot Path Planning ◽  
Autonomous Ground Vehicles ◽  
Planning Methods ◽  
Robot Path

Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.

Utility Function Derived Off-Road Vehicle Path Planning

2020 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Christopher Slon ◽  
Calahan Mollan ◽  
Dakota Barthlow ◽  
David Gorsich ◽  
...  
Keyword(s):  
Path Planning ◽  
Cost Function ◽  
Utility Function ◽  
Optimal Path ◽  
United States Geological Survey ◽  
Ground Vehicles ◽  
Multiattribute Utility ◽  
End Points ◽  
Vehicle Path ◽  
Physics Based Simulation

Abstract Optimal navigation of ground vehicles in an off-road setting is a challenging task. One must accurately model the properties of the terrain and reconcile it with vehicle capabilities, while simultaneously addressing mission requirements. An important part of navigation is path planning, the selection of the route a vehicle takes between the start and end points. It is often seen that, given the starting and end points for a vehicle, the optimal path that the vehicle should take varies considerably with the mission requirements. While most commonly used algorithms use a local cost function, mission requirements are typically defined over the entire run of the vehicle. Utility theoretic methods provide a normative tool to model tradeoffs over attributes (mission requirements) that the operator cares about. It is critical therefore, that preferences embedded in the utility function influence the local cost functions used. In this paper, we provide a framework for a feedback-based method to update the parameters of the local cost-function. We do so by using a geodesic-based method for path planning given the terrain inputs, followed by a physics-based simulation of a vehicle to evaluate the attributes. These attributes are then combined into a multiattribute utility function. An optimization-based approach is used to find the parameters of the cost function that maximizes this multiattribute utility. We present our approach on a vehicle navigation example over a terrain acquired from United States Geological Survey data.

scholarly journals An Intelligent Gain based Ant Colony Optimisation Method for Path Planning of Unmanned Ground Vehicles

2019 ◽  
Vol 69 (2) ◽  
pp. 167-172 ◽  
Author(s):  
Sangeetha Viswanathan ◽  
K. S. Ravichandran ◽  
Anand M. Tapas ◽  
Sellammal Shekhar
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Computation Time ◽  
Ant Colony ◽  
Ant Colony Optimisation ◽  
Ground Vehicles ◽  
Optimal Path Planning ◽  
Military Applications ◽  
The Military ◽  
Reduction Cost

 In many of the military applications, path planning is one of the crucial decision-making strategies in an unmanned autonomous system. Many intelligent approaches to pathfinding and generation have been derived in the past decade. Energy reduction (cost and time) during pathfinding is a herculean task. Optimal path planning not only means the shortest path but also finding one in the minimised cost and time. In this paper, an intelligent gain based ant colony optimisation and gain based green-ant (GG-Ant) have been proposed with an efficient path and least computation time than the recent state-of-the-art intelligent techniques. Simulation has been done under different conditions and results outperform the existing ant colony optimisation (ACO) and green-ant techniques with respect to the computation time and path length.

A New Algorithm of Path Planning Based on Local Data

2012 ◽  
Vol 531-532 ◽  
pp. 741-745
Author(s):  
Zi Wei Zhou ◽  
Xin Yu O Yang ◽  
Wang Bao Xu ◽  
Nai Dong Cui
Keyword(s):  
Path Planning ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Sensor Data ◽  
Target Point ◽  
Local Data ◽  
Practical Applications ◽  
Set Up ◽  
Moving Obstacle ◽  
Robot Path

A novel new algorithm of path planning is proposed for the robot path planning based on the local data map in the robot autonomous navigation. The algorithm is used to search for the local optimal path from the current position of the robot to the target according to the known sensor data. If the robot cannot reach the target directly, the temporary target point which the robot can reach will be set up according to the optimal path. The algorithm is effective under the complicated unknown environment and moving obstacle situation which fast searching speed, it can be adapted into the practical applications and the multi-robot harmony easily. The simulation result shows that this method can provide a better planning path by comparing with the traditional planning algorithms such as artificial potential field and wall-following

Sign in / Sign up

Export Citation Format

Share Document