Intelligent Collision Avoidance Method for Motion Obstacles of Unmanned Vehicles

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Hongge Zhang
Keyword(s):  
Collision Avoidance ◽  
Road Safety ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Artificial Potential Field ◽  
High Detection Rate ◽  
Unmanned Vehicle ◽  
Future Behavior ◽  
The Future ◽  
Vehicle Motion

At present, the active technology of automobiles is becoming more and more mature and the emergence of driverless vehicles makes it a hotspot in the field of road safety. A new intelligent collision avoidance method for unmanned vehicle motion obstacles is proposed. The kinematics model of unmanned vehicles is established and linearized to obtain the kinematics linear tracking error model of unmanned vehicles and predict the future behavior of unmanned vehicles. The intelligent collision avoidance can be achieved by improving the artificial potential field model of the unmanned vehicle after understanding the future behavior and obstacle information of the unmanned vehicle. The experimental results show that the method has a high detection rate and success rate of obstacle avoidance and low total time-consuming in the process of behavior selection and path planning. It can quickly make collision avoidance responses and reduce the possibility of collision.

Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework

2018 ◽  
Vol 06 (04) ◽  
pp. 231-250 ◽  
Author(s):  
Willson Amalraj Arokiasami ◽  
Prahlad Vadakkepat ◽  
Kay Chen Tan ◽  
Dipti Srinivasan
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Path Generation ◽  
Ground Vehicles ◽  
Agent Architectures ◽  
Time Path ◽  
Multi Agent

Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service Oriented Interoperable Framework for Robot Autonomy (SOIFRA) proposed in this work is an interoperable multi-agent framework focused on generalizing platform independent algorithms for unmanned aerial and ground vehicles. SOIFRA is behavior-based, modular and interoperable across unmanned aerial and ground vehicles. SOIFRA provides collision avoidance, and, path-planning and tracking behaviors for unmanned aerial and ground vehicles. Vector Directed Path-Generation and Tracking (VDPGT), a vector-based algorithm for real-time path-generation and tracking, is proposed in this work. VDPGT dynamically adopts the shortest path to the destination while minimizing the tracking error. Collision avoidance is performed utilizing Hough transform, Canny contour, Lucas–Kanade sparse optical flow algorithm and expansion of object-based time-to-contact estimation. Simulation and experimental results from Turtlebot and AR Drone show that VDPGT can dynamically generate and track paths, and SOIFRA is interoperable across multiple robotic platforms.

scholarly journals Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments

2021 ◽  
Vol 11 (13) ◽  
pp. 5963
Author(s):  
Phuc Thanh-Thien Nguyen ◽  
Shao-Wei Yan ◽  
Jia-Fu Liao ◽  
Chung-Hsien Kuo
Keyword(s):  
Robot Navigation ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Mobile Robot Navigation ◽  
Selection Strategy ◽  
Localization And Mapping ◽  
Pure Pursuit ◽  
Stable Performance ◽  
Large Curve ◽  
Moving Speed

In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. Among trajectory-tracking algorithms for unmanned vehicles, the Pure Pursuit (PP) algorithm is prevalent in many real-world applications because of its simple and easy implementation. However, it is challenging to decelerate the AGV’s moving speed when turning on a large curve path. Moreover, this paper addresses the kidnapped-robot problem occurring in spare LiDAR environments. This paper proposes an improved Pure Pursuit algorithm so that the AGV can predict the trajectory and decelerate for turning, thus increasing the accuracy of the path tracking. To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system between Simultaneous Localization And Mapping (SLAM) method and Odometer method. As experimental results in practice, the improved Pure Pursuit algorithm can reduce the tracking error while performing more efficiently. Moreover, the learning-based localization selection strategy helps the robot navigation task achieve stable performance, with 36.25% in completion rate more than only using SLAM. The results demonstrate that the proposed method is feasible and reliable in actual conditions.

scholarly journals Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

2021 ◽  
Vol 11 (7) ◽  
pp. 3103
Author(s):  
Kyuman Lee ◽  
Daegyun Choi ◽  
Donghoon Kim
Keyword(s):  
Collision Avoidance ◽  
Search Algorithm ◽  
Unmanned Aircraft ◽  
Artificial Potential Field ◽  
Tree Search ◽  
Local Minima ◽  
Collision Risk ◽  
Motion Primitives ◽  
Multiple Uavs ◽  
Tree Search Algorithm

Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.

Artificial intelligence in the digital twins: State of the art, challenges, and future research topics

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 12
Author(s):  
Zhihan Lv ◽  
Shuxuan Xie
Keyword(s):  
Artificial Intelligence ◽  
Smart City ◽  
Traffic Accidents ◽  
Intelligent Manufacturing ◽  
Unmanned Vehicles ◽  
Urban Traffic ◽  
Future Research ◽  
Test Accuracy ◽  
Digital Twins ◽  
The Future

Advanced computer technologies such as big data, Artificial Intelligence (AI), cloud computing, digital twins, and edge computing have been applied in various fields as digitalization has progressed. To study the status of the application of digital twins in the combination with AI, this paper classifies the applications and prospects of AI in digital twins by studying the research results of the current published literature. We discuss the application status of digital twins in the four areas of aerospace, intelligent manufacturing in production workshops, unmanned vehicles, and smart city transportation, and we review the current challenges and  topics that need to be looked forward to in the future. It was found that the integration of digital twins and AI has significant effects in aerospace flight detection simulation, failure warning, aircraft assembly, and even unmanned flight. In the virtual simulation test of automobile autonomous driving, it can save 80% of the time and cost, and the same road conditions reduce the parameter scale of the actual vehicle dynamics model and greatly improve the test accuracy. In the intelligent manufacturing of production workshops, the establishment of a virtual workplace environment can provide timely fault warning, extend the service life of the equipment, and ensure the overall workshop operational safety. In smart city traffic, the real road environment is simulated, and traffic accidents are restored, so that the traffic situation is clear and efficient, and urban traffic management can be carried out quickly and accurately. Finally, we looked forward to the future of digital twins and AI, hoping to provide a reference for future research in related fields.

Research on Management System of Automatic Driver Decision-Making Knowledge Base for Unmanned Vehicle

2019 ◽  
Vol 33 (04) ◽  
pp. 1959013 ◽  
Author(s):  
Zhaoxia Zhang ◽  
Qing Jiang ◽  
Rujing Wang ◽  
Liangtu Song ◽  
Zhengyong Zhang ◽  
...  
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Management System ◽  
Autonomous Driving ◽  
Unmanned Vehicles ◽  
Knowledge Model ◽  
Unmanned Vehicle ◽  
Base Management System ◽  
Decision Making System ◽  
Base Management

The acquisition, presentation and management of autonomous driving decision-making knowledge of unmanned vehicles are the key and difficult issues in the autonomous driving decision-making system of unmanned vehicles. This paper presents a knowledge model, which includes problem description layer and problem-solving knowledge layer. The automatic driving decision knowledge base of unmanned vehicle is composed of a set of knowledge models. Knowledge model supports knowledge representation and reasoning. Based on the WEB visualization knowledge modeling tool and visualization knowledge service tool, we construct the decision-making knowledge base management system for autonomous driving of unmanned vehicles and then construct the autonomous driving decision-making system of unmanned vehicles. The reasoning example shows that the knowledge base management system can effectively improve the knowledge acquisition, representation and maintenance efficiency of autonomous driving decision-making system, which is of great significance in enhancing the intelligence level of autonomous driving decision-making system.

scholarly journals Car Following Model and Algorithm Design based on Reinforcement Learning

2021 ◽  
Vol 2083 (3) ◽  
pp. 032008
Author(s):  
Jie Ren
Keyword(s):  
Reinforcement Learning ◽  
Algorithm Design ◽  
Unmanned Vehicles ◽  
Learning Technology ◽  
Car Following ◽  
Experimental Environment ◽  
The Future ◽  
Car Following Model

Abstract Based on reinforcement learning technology, this paper establishes a new driverless car following model. DQN algorithm and traffic simulator are mainly used to train the agent, and the following model is finally obtained. Under the precise and controllable experimental environment, the preset optimization targets can achieve the expected assumption and complete the following behavior. This study will contribute to the development of unmanned vehicles in the future.

scholarly journals Vehicles for Open-Pit Mining with Smart Scheduling System for Transportation Based on 5G

2021 ◽  
Vol 12 (5) ◽  
pp. 827-835
Author(s):  
Mohanad F Jwaid, Husam K Salih Juboori
Keyword(s):  
Artificial Intelligence ◽  
Unmanned Vehicles ◽  
Open Pit Mine ◽  
Open Pit ◽  
Open Pit Mining ◽  
Transport Costs ◽  
Time Data ◽  
Nsga Ii ◽  
Scheduling System ◽  
Unmanned Vehicle

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancements coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. In addition to four other models we are proposing a decomposition-based restricted genetic dominance (DBCDP-NSGA-II) algorithm, which retains viable and non-facilitating solutions in small areas in order to improve the convergence, distribution and diversity of traditional high-dimensional multi-objective fast-dominated genetic sorting Algorithms (NSGA-II).

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