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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

Engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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Finding Optimal Nash Equilibria in Multiplayer Games via Correlation Plans 22

Finding Optimal Nash Equilibria in Multiplayer Games via Cor...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Zhang, Youzhi An, Bo Subrahmanian, V.S. Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation CAS Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science Northwestern University Evanston United States

Designing efficient algorithms to compute a Nash Equilibrium (NE) in multiplayer games is still an open challenge. In this paper, we focus on computing an NE that optimizes a given objective function. Finding an optimal NE in multiplayer games can be formulated as a mixed-integer bilinear program by introducing auxiliary variables to represent bilinear terms, leading to a huge number of bilinear terms, making it hard to solve. To overcome this challenge, we propose a novel algorithm called CRM based on a novel mixed-integer bilinear program with correlation plans for some subsets of players, which uses Correlation plans with their Relations to strictly reduce the feasible solution space after the convex relaxation of bilinear terms while Minimizing the number of correlation plans to significantly reduce the number of bilinear terms. Experimental results show that our algorithm can be several orders of magnitude faster than the state-of-the-art baseline. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Computation theory

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Robot-Assisted Wide-Area Photoacoustic System

Robot-Assisted Wide-Area Photoacoustic System

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Gao, Shang Ma, Xihan Zhang, Haichong K. Worcester Polytechnic Institute Department of Robotics Engineering Worcester United States Worcester Polytechnic Institute Department of Biomedical Engineering Worcester United States Worcester Polytechnic Institute Department of Computer Science Worcester United States

ISBN: (纸本)9798350346459

Liver ischemia and reperfusion injury (IRI) is a vascular complication that can lead to hepatic functional impairment. IRI is relevant in liver transplantation and during liver surgery performed with intermittent vascular inflow occlusion. Detecting intraoperative liver oxygenation impairment and ischemia could help prevent IRI, which is challenging partially due to the multiple hepatic vascular inflows. We propose a robot-assisted wide-area photoacoustic (PA) imaging system to monitor the liver oxygenation level intraoperatively. PA imaging has been widely used for oxygenation detection in preclinical research. We used a robotic arm to actuate the 2D PA probe and scan across the liver surface. An accurate volumetric tomography can be reconstructed via registering 2D images to the recorded robot trajectory. An ex vivo porcine liver sample was scanned using the proposed system. The result suggested that the robot-assisted wide-area PA tomography has the potential to be incorporated into clinical practice to evaluate liver SO2 intraoperatively. © 2023 IEEE.

关键词： Photoacoustic effect

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Risk-Aware Decentralized Safe Control via Dynamic Responsibility Allocation 37

Risk-Aware Decentralized Safe Control via Dynamic Responsibi...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Lyu, Yiwei Luo, Wenhao Dolan, John M. Department of Electrical and Computer Engineering Carnegie Mellon University United States Department of Computer Science University of North Carolina at Charlotte United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9781577358800

In this work, we present a novel risk-aware decentralized Control Barrier Function (CBF)-based controller for multi-agent systems. The proposed decentralized controller is composed based on pairwise agent responsibility shares (a percentage), calculated from the risk evaluation of each individual agent faces in a multi-agent interaction environment. With our proposed CBF-inspired risk evaluation framework, the responsibility portions between pairwise agents are dynamically updated based on the relative risk they face. Our method allows agents with lower risk to enjoy a higher level of freedom in terms of a wider action space, and the agents exposed to higher risk are constrained more tightly on action spaces, and are therefore forced to proceed with caution. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi agent systems

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Intersection Coordination with Priority-Based Search for Autonomous Vehicles 37

Intersection Coordination with Priority-Based Search for Aut...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Li, Jiaoyang Hoang, The Anh Lin, Eugene Vu, Hai L. Koenig, Sven Robotics Institute Carnegie Mellon University United States Department of Civil Engineering Monash University Australia Computer Science Department University of Southern California United States

ISBN: (纸本)9781577358800

The development of connected and autonomous vehicles opens an opportunity to manage intersections without signals. One promising approach is to use a central autonomous intersection manager to optimize the movement of the vehicles in the intersection. Existing work uses Mixed Integer Linear Programming (MILP) to find optimal solutions for this problem but is time-consuming and cannot be applied in real-time. On the other hand, the coordination of the vehicles is essentially a Multi-Agent Path Finding (MAPF) problem, for which dozens of efficient algorithms have been proposed in recent years. Inspired by these MAPF algorithms, we propose a three-level algorithm called PSL to solve the intersection coordination problem. Theoretically, PSL is complete and polynomial-time in the number of vehicles. Empirically, PSL runs significantly faster with only a slight compromise in the solution quality than the optimal MILP method. It also generates significantly better solutions with a slightly larger runtime than the traditional First-Come-First-Served strategy. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Autonomous vehicles

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Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning 38

Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Rep...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Paolino, Raffaele Maskey, Sohir Welke, Pascal Kutyniok, Gitta Department of Mathematics LMU Munich Germany Germany Faculty of Computer Science TU Wien Austria Institute for Robotics and Mechatronics DLR-German Aerospace Center Germany Department of Physics and Technology University of Tromsø Norway

We introduce r-loopy Weisfeiler-Leman (r-`WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, r-`MPNN, that can count cycles up to length r+2. Most notably, we show that r-`WL can count homomorphisms of cactus graphs. This extends 1-WL, which can only count homomorphisms of trees and, in fact, we prove that r-`WL is incomparable to k-WL for any fixed k. We empirically validate the expressive and counting power of r-`MPNN on several synthetic datasets and demonstrate the scalability and strong performance on various real-world datasets, particularly on sparse graphs. Our code is available on GitHub. © 2024 Neural information processing systems foundation. All rights reserved.

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Vision-Based Object Sorting in Dynamic Environments using YOLO for RoboCup ARM Challenge 2023 7

Vision-Based Object Sorting in Dynamic Environments using YO...

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7th IEEE Conference on Information and Communication Technology, CICT 2023

作者： Sahu, Yash Kumar Mittal, Radhika Patel, Deep Maiti, Chayan Sreekumar, M. Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Mechanical Engineering Kancheepuram Chennai India Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Computer Science Kancheepuram Chennai India

ISBN: (纸本)9798350305173

In the intersection of Artificial Intelligence (AI) and robotics, autonomous object manipulation has emerged as a pivotal research domain, finding applications in sectors as diverse as manufacturing and healthcare. While significant strides have been made in object identification, trajectory planning, and manipulation tasks, these advancements often fall short in real-world, dynamic settings, revealing a critical research gap in algorithmic adaptability and computational efficiency. This paper addresses the gap by applying YOLOv4, a state-of-the-art object detection AI algorithm, within the context of the RoboCup Autonomous Robot Manipulation (ARM) Challenge - a competitive platform designed to simulate real-world complexities in autonomous systems. The research objectives are twofold: firstly, the performance of YOLOv4 in object detection and classification under dynamically changing conditions is rigorously evaluated, employing varied training strategies in both simulated and real-world scenarios. Secondly, the robustness and adaptability of the algorithm in object manipulation tasks are scrutinized and facilitated by the real-world dynamics inherent to the competition. Empirical results demonstrate the algorithm's high accuracy, efficiency, and robustness in dynamic environments, substantiating its broader applicability in the development of autonomous systems. © 2023 IEEE.

关键词： Object detection

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Continuous query-based data trading 24

Continuous query-based data trading

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2024 ACM SIGMETRICS/IFIP Performance Conference on Measurement and Modeling of computer Systems, SIGMETRICS/PERFORMANCE 2024

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400706240

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Copyright held by the owner/author(s).

关键词： Integer programming

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Performance Evaluation of Machine Learning Classifiers for Sudden Cardiac Arrest Detection

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Journal of The Institution of Engineers (India): Series B 2022年 1-7页

作者： Banerjee, Prakash Bhattacherjee, Saptak Dasgupta, Kousik Sen, Shibaprasad Department of Electrical and Electronics Engineering University of Engineering and Management Kolkata India School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Shibpur Howrah India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

Sudden cardiac arrest (SCA) is a type of cardiovascular disease which attacks a person so promptly that it gives minimum time for hospitalization and may also lead to the patient death. In order to save the patient from death, rapid detection of SCA just after the onset of symptoms with a high accuracy is very critical. Although numerous approaches of SCA detection based on electrocardiogram have been carried out, there is still need of standard method for SCA detection with high precession and fast detection. Hence, the present work proposes the use of four machine learning classifiers for detection of SCA based on temporal features extracted from ECG. Performance analysis of the classifiers used in this study on 19 SCA and 20 normal, i.e., healthy subjects, yields classification accuracies of 97.4%, 94.8%, 97.4% and 97.4% for SVM, decision tree, Naïve Bayes and KNN, respectively. Experimental results thus obtained demonstrate the potential of the proposed method as a promising alternative for SCA detection. © 2022, The Institution of Engineers (India).

关键词： Electrocardiograms

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Intelligent Accident Recognition and Notification System using Hybrid Deep Learning and IoT 4

Intelligent Accident Recognition and Notification System usi...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sathish Kumar, P. Singh Rawat, Ramesh Shrimali, Manish Parikh, Swapnil Patil, Harshal Maranan, Ramya United Institute of Technology Department of Robotics & Automation Tamil Nadu Coimbatore641020 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Computer Science and IT Rajasthan Udaipur313 003 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Symbiosis Institute of Technology Symbiosis International Deemed University Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331515720

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-Accident data were collected. The model is evaluated using three metrics: Accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety. © 2024 IEEE.

关键词： Graphical user interfaces

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