Reliability-Based Multi-Vehicle Path Planning Under Uncertainty Using a Bio-Inspired Approach

2021 ◽  
pp. 1-44
Author(s):  
Yixuan Liu ◽  
Chen Jiang ◽  
Xiaoge Zhang ◽  
Zissimos P. Mourelatos ◽  
Dakota Barthlow ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Dynamics Simulation ◽  
Computational Time ◽  
Soil Conditions ◽  
Planning Under Uncertainty ◽  
Uncertain Environments ◽  
Multiple Vehicles ◽  
Single Vehicle ◽  
Vehicle Path

Abstract Identifying a reliable path in uncertain environments is essential for designing reliable off-road autonomous ground vehicles (AGV) considering post-design operations. This paper presents a novel bio-inspired approach for model-based multi-vehicle mission planning under uncertainty for off-road AGVs subjected to mobility reliability constraints in dynamic environments. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility (i.e., maximum attainable speed) for any given terrain and soil conditions. Based on physics-based simulations, the vehicle state mobility reliability in operation is then analyzed using an adaptive surrogate modeling method to overcome the computational challenges in mobility reliability analysis by adaptively constructing a surrogate. Subsequently, a bio-inspired approach called Physarum-based algorithm is used in conjunction with a navigation mesh to identify an optimal path satisfying a specific mobility reliability requirement. The developed Physarum-based framework is applied to reliability-based path planning for both a single-vehicle and multiple-vehicle scenarios. A case study is used to demonstrate the efficacy of the proposed methods and algorithms. The results show that the proposed framework can effectively identify optimal paths for both scenarios of a single and multiple vehicles. The required computational time is less than the widely used Dijkstra-based method.

scholarly journals Time-efficient path planning using two virtual robots

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988674
Author(s):  
Jonghoek Kim
Keyword(s):  
Path Planning ◽  
Finite Time ◽  
Free Space ◽  
Path Length ◽  
Optimal Path ◽  
Three Dimensional ◽  
Computational Time ◽  
Topological Map ◽  
Planning Algorithms ◽  
Safe Path

This article introduces time-efficient path planning algorithms handling both path length and safety within a reasonable computational time. The path is planned considering the robot’s size so that as the robot traverses the constructed path, it doesn’t collide with an obstacle boundary. This article introduces two virtual robots deploying virtual nodes which discretize the obstacle-free space into a topological map. Using the topological map, the planner generates a safe and near-optimal path within a reasonable computational time. It is proved that our planner finds a safe path to the goal in finite time. Using MATLAB simulations, we verify the effectiveness of our path planning algorithms by comparing it with the rapidly-exploring random tree (RRT)-star algorithm in three-dimensional environments.

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 Proposed Smooth-STC Algorithm for Enhanced Coverage Path Planning Performance in Mobile Robot Applications

Robotics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 44 ◽  
Author(s):  
Hai Van Pham ◽  
Philip Moore ◽  
Dinh Xuan Truong
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Forest Monitoring ◽  
Planning Problem ◽  
Operational Environment ◽  
Uncertain Environments ◽  
Coverage Path Planning ◽  
Agricultural Robots ◽  
Cleaning Robots ◽  
Path Planning Problem

Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement. The aim of path planning is to optimise the efficacy of robotic movement in a defined operational environment. For example, robots have been employed in many domains including: Cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, and agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.

Research on Optimal Vehicle Path in Intelligent Transportation

2014 ◽  
Vol 644-650 ◽  
pp. 2615-2618
Author(s):  
Wen Ming Yu
Keyword(s):  
Data Storage ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Rapid Development ◽  
Optimal Path ◽  
Intelligent Transportation ◽  
Proposed Model ◽  
Planning Algorithm ◽  
Single Vehicle ◽  
Vehicle Path

The rapid development of economy and science and technology is quite obvious, which makes cars becoming more and more widely into the general family life. With the rapid development of economics and technology; the number of vehicles has largely increased. In this paper, there is the basic organizational framework for intelligent transportation, intelligent transportation network proposed model and its data storage structure, and the important influence on optimal path trajectory intelligent transportation planning. This paper analyzes intersection road network in the distribution based on computer graphics language, type and grade roads. The dynamic mathematical model of single vehicle is used vehicle planning algorithm based on period, effectively avoided the traffic road, reduce vehicle travel cost, improve the real-time effect and accuracy of the vehicle dynamic path. We hope the results and researches could combine with reality in order to reduce traffic congestion.

scholarly journals A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 945 ◽  
Author(s):  
Iram Noreen ◽  
Amna Khan ◽  
Khurshid Asghar ◽  
Zulfiqar Habib
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Execution Time ◽  
Path Length ◽  
Optimal Path ◽  
Performance Comparison ◽  
Computational Time ◽  
And Performance ◽  
Commercial Applications ◽  
Space Requirements

With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the total number of turns. MEA* is an efficient variation of A* for optimal path-planning of mobile robots. RRT*-AB is a sampling-based planner with rapid convergence rate, and improved time and space requirements than other sampling-based methods such as RRT*. The purpose of this paper is the review and performance comparison of these planners based on metrics, i.e., path length, execution time, and memory requirements. All planners are tested in structured and complex unstructured environments cluttered with obstacles. Performance plots and statistical analysis have shown that MEA* requires less memory and computational time than other planners. These advantages of MEA* make it suitable for off-line applications using small robots with constrained power and memory resources. Moreover, performance plots of path length of MEA* is comparable to RRT*-AB with less execution time in the 2D environment. However, RRT*-AB will outperform MEA* in high-dimensional problems because of its inherited suitability for complex problems.

Optimal path planning for intelligent automated wheelchair using DDSRPSO

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kalaipriyan Thirugnanasambandam ◽  
Raghav R.S. ◽  
Jayakumar Loganathan ◽  
Ankur Dumka ◽  
Dhilipkumar V.
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Swarm Intelligence ◽  
Cooperative Behavior ◽  
Optimal Path ◽  
Particle Swarm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Optimal Path Planning

Purpose This paper aims to find the optimal path using directionally driven self-regulating particle swarm optimization (DDSRPSO) with high accuracy and minimal response time. Design/methodology/approach This paper encompasses optimal path planning for automated wheelchair design using swarm intelligence algorithm DDSRPSO. Swarm intelligence is incorporated in optimization due to the cooperative behavior in it. Findings The proposed work has been evaluated in three different regions and the comparison has been made with particle swarm optimization and self-regulating particle swarm optimization and proved that the optimal path with robustness is from the proposed algorithm. Originality/value The performance metrics used for evaluation includes computational time, success rate and distance traveled.

Water wave optimization algorithm for autonomous underwater vehicle path planning problem

2021 ◽  
pp. 1-15
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jia Zeng ◽  
Jialing Tang
Keyword(s):  
Path Planning ◽  
Water Wave ◽  
Autonomous Underwater Vehicle ◽  
Optimal Path ◽  
Wave Theory ◽  
Underwater Vehicle ◽  
Local Optimum ◽  
Planning Problem ◽  
Vehicle Path ◽  
Path Planning Problem

In this paper, a water wave optimization (WWO) algorithm is proposed to solve the autonomous underwater vehicle (AUV) path planning problem to obtain an optimal or near-optimal path in the marine environment. Path planning is a prerequisite for the realization of submarine reconnaissance, surveillance, combat and other underwater tasks. The WWO algorithm based on shallow wave theory is a novel evolutionary algorithm that mimics wave motions containing propagation, refraction and breaking to obtain the global optimization solution. The WWO algorithm not only avoids jumps out of the local optimum and premature convergence but also has a faster convergence speed and higher calculation accuracy. To verify the effectiveness and feasibility, the WWO algorithm is applied to solve the randomly generated threat areas and generated fixed threat areas. Compared with other algorithms, the WWO algorithm can effectively balance exploration and exploitation to avoid threat areas and reach the intended target with minimum fuel costs. The experimental results demonstrate that the WWO algorithm has better optimization performance and is robust.

scholarly journals Vehicle path planning for complete field coverage using genetic algorithms

2021 ◽  
Author(s):  
A. E. F. Ryerson ◽  
Q. Zhang
Keyword(s):  
Genetic Algorithms ◽  
Path Planning ◽  
Optimal Path ◽  
Agricultural Equipment ◽  
Automated Method ◽  
Complete Field ◽  
Simulation Results ◽  
Vehicle Path ◽  
The Cost ◽  
Further Development

In farming operations, one of the fundamental issues facing farmer is the cost of running the farm. If the equipment the farmer is using can be made more efficient, the cost of farming will be reduced. One way of making agricultural equipment more efficient is to develop automated or autonomous functions for the equipment. One of the fundamental tasks for autonomous equipment is to plan the path for the equipment to travel. This paper reports the research on the feasibility of creating an automated method of path planning for autonomous agricultural equipment. Genetic algorithms were chosen to plan the paths with a primary goal of creating an optimal path guiding the equipment to completely cover a field while avoiding all known obstacles. Two example fields were designed for evaluating the feasibility of this concept on simple problems. While simulation results verified the feasibility of this conceptual path planning method, they also indicated that further development would be required before the algorithm could actually be implemented on agricultural equipment for real-world field applications. Keywords: Automonous equipment, genetic algorithms, off-road vehicle, path planning

Simulation-Based Multi-Vehicle Path Planning Under Uncertainty Using Physarum-Based Approach

2022 ◽  
Author(s):  
Yixuan Liu ◽  
Chen Jiang ◽  
Xiaoge Zhang ◽  
Zissimos P. Mourelatos ◽  
Zhen Hu ◽  
...  
Keyword(s):  
Path Planning ◽  
Planning Under Uncertainty ◽  
Simulation Based ◽  
Vehicle Path
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