Real-Time Collision Detection and Collision Avoidance

2021 ◽  
pp. 91-113
Author(s):  
Bernard Schmidt
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection

REAL-TIME SELF-COLLISION AVOIDANCE SYSTEM FOR ROBOTS USING ROBE

2004 ◽  
Vol 01 (03) ◽  
pp. 533-550 ◽  
Author(s):  
FUMI SETO ◽  
KAZUHIRO KOSUGE ◽  
YASUHISA HIRATA
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Reaction Force ◽  
Collision Avoidance System ◽  
Representation Method ◽  
Elastic Elements

In this paper, we propose a real-time self-collision avoidance system for robots which cooperate with a human/humans. First, the robot is represented by elastic elements. The representation method is referred to as RoBE (Representation of Body by Elastic elements). Elastic balls and cylinders are used as the elements to simplify collision detection, although elements of any shape could be used for RoBE. When two elements collide with each other, a reaction force is generated between them, and self-collision avoidance motion is generated by the reaction force. Experiments using the mobile robot with dual manipulators, referred to as MR Helper, illustrate the validity of the proposed system.

A Method for Searching Optimal Routes with Collision Avoidance on Raster Charts

2003 ◽  
Vol 56 (3) ◽  
pp. 371-384 ◽  
Author(s):  
Ki-Yin Chang ◽  
Gene Eu Jan ◽  
Ian Parberry
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Linear Time ◽  
Routing Algorithm ◽  
Time And Space ◽  
Navigation Safety

Collision avoidance is an intensive discussion issue for navigation safety. This article introduces a new routing algorithm for finding optimal routes with collision detection and avoidance on raster charts or planes. After the required data structure of the raster chart is initialized, the maze routing algorithm is applied to obtain the particular route of each ship. Those ships that have potential to collide will be detected by simulating the particular routes with ship domains. The collision avoidance scheme can be achieved by using the collision-area-marking method with collision avoidance rules at sea. The algorithm has linear time and space complexities, and is sufficiently fast to perform real-time routing on the raster charts.

scholarly journals Using Real-Time Dynamic Prediction to Implement IoV-Based Collision Avoidance

2019 ◽  
Vol 9 (24) ◽  
pp. 5370
Author(s):  
Che-Cheng Chang ◽  
Wei-Ming Lin ◽  
Chuan-An Lai
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Confidence Region ◽  
Dynamic Prediction ◽  
Internet Of Vehicles ◽  
Time Dynamic ◽  
The Future ◽  
Future Position

For some IoV-based collision-avoidance architectures, it is not necessary that all vehicles have communication abilities. Hence, they need some particular designs and extra components. In the literature, one of them uses a camera mounted onto the infrastructure at an intersection to realize collision detection. Consequently, technologies for real-time object detection and dynamic prediction are required for the purposes of collision avoidance. In this paper, we propose an interesting method to predict the future position of a vehicle based on a well-known, real-time object detection project, YOLOv3. Our algorithm utilizes the concept of vehicle dynamics and the confidence region to predict the future position on vehicles. This will help us to realize real-time dynamic prediction and Internet of Vehicles (IoV)-based collision detection. Lastly, in accordance with the experimental results, our design shows the performance for predicting the future position of a vehicle.

REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles

SIMULATION ◽  
2021 ◽  
pp. 003754972110047
Author(s):  
Muhammad A Butt ◽  
Faisal Riaz ◽  
Yasir Mehmood ◽  
Somyyia Akram
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Autonomous Vehicles ◽  
Affective Computing ◽  
Field Experiments ◽  
Road Traffic ◽  
Sensory System ◽  
Driving Mode ◽  
Human Driver

Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent), which is capable of rear-end collision avoidance using artificial human driver emotions. However, the architecture of the EEEC_Agent is based on an ultrasonic sensory system which follows three-state driving strategies without considering the neighbor vehicles types. To address these issues, in this paper we propose an extended version of the EEEC_Agent which contains human driver-inspired dynamic driving mode controls for autonomous vehicles. In addition, a novel end-to-end learning-based motion planner has been devised to perceive the surrounding environment and regulate driving tasks accordingly. The real-time in-field experiments performed using a prototype AV demonstrate the effectiveness of this proposed rear-end collision avoidance system.

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

2021 ◽  
pp. 095440702110240
Author(s):  
Ziyu Zhang ◽  
Chunyan Wang ◽  
Wanzhong Zhao ◽  
Jian Feng
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Decentralized Control ◽  
Control Strategy ◽  
Centralized Control ◽  
Lateral Control ◽  
Time Performance ◽  
Collision Avoidance Control ◽  
Avoidance Control ◽  
Control Accuracy

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

scholarly journals COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

2021 ◽  
Vol 9 (4) ◽  
pp. 405
Author(s):  
Raphael Zaccone
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Open Water ◽  
Optimal Path Planning ◽  
Guidance And Control ◽  
Real Time Applications ◽  
And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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