Flexible multi-unmanned ground vehicles (MUGVs) in intersection coordination based on ε-constraint probability collectives algorithm

Cooperative navigation (CN) is a widespread technique to have efficient navigation of intelligent vehicles. Nonetheless, the CN strategies need to be more consistent in estimating and managing in-road risks. This paper outlines a flexible CN scheme for multiple unmanned ground vehicles (MUGVs) syste...

Full description

Saved in:
Bibliographic Details
Published in:International journal of intelligent robotics and applications Online Vol. 5; no. 2; pp. 156 - 175
Main Authors: Zhu, Zhengze, Adouane, Lounis, Quilliot, Alain
Format: Journal Article
Language:English
Published: Singapore Springer Singapore 01.06.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Behavior
Collaboration
Collision avoidance
Computer networks
Computer Science
Control
Electronics and Microelectronics
Energy consumption
Instrumentation
Intelligent vehicles
Machines
Manufacturing
Mechatronics
Navigation
Navigation systems
Optimization
Probability distribution
Processes
Real time
Regular Paper
Risk management
Robotics
Roundabouts
Safety margins
Strategy
Traffic accidents & safety
Traffic intersections
Unmanned ground vehicles
User Interfaces and Human Computer Interaction
Vehicles
Probability collectives
Real-time constraints
Multi-vehicle coordination
Risk assessment and management
constraint PC
ISSN:2366-5971, 2366-598X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cooperative navigation (CN) is a widespread technique to have efficient navigation of intelligent vehicles. Nonetheless, the CN strategies need to be more consistent in estimating and managing in-road risks. This paper outlines a flexible CN scheme for multiple unmanned ground vehicles (MUGVs) system to deal with such critical cooperative system. With its relative low execution time, the probability collectives (PC) algorithm has succeeded at generating fast and feasible solutions to cross intersections and roundabouts (Philippe et al. 1928–1934, 2019). However, the PC is still sensitive to uncertainty in the navigation process, which highlights the need to adopt several safety margins. This work focuses on balancing between the high-quality cooperative optimization and acceptable computational speed. Thus, a reliable risk management strategy is proposed by introducing a novel ε -constraint PC method. A real-time communication mechanism is suggested for a distributed system to avoid invalid behavior due to inconsistency. The novel ε -PC based navigation strategy allows the vehicles to adapt their dynamics and react to unexpected events while respecting real-time constraints. One finding appears to be well substantiated by the typical common-yet-difficult scenarios in intensive simulations. The ε -PC method can ensure collision-free behaviors and reserve at least 1.5s of reaction time for vehicles’ safety insurance.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2366-5971
2366-598X
DOI:10.1007/s41315-021-00181-4