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作者： Buckingham, David Tufts University

学位级别：Ph.D., Doctor of Philosophy

In Epistemic planning, an automated system decides what goal-oriented actions to perform in a task-environment where the mental perspectives (beliefs and knowledge) of multiple agents are of prime importance. This work presents three contributions to Epistemic planning. First, three action languages improve upon prior languages to expand the class of planning domains that can be expressed with a natural-language-like syntax. Second, a new planning algorithm generalizes prior techniques by relaxing assumptions about how states and actions are represented. This approach also incorporates the predicted behavior of independent agents (beyond the planner's control), allowing the flexibility to plan for cooperative, competitive, or neutral (self-interested) multi-agent domains. Finally, we present and evaluate the Multi-Agent Cooperation-Agnostic Planner, which implements these methods.

关键词： Task-environment Natural-language-like syntax planning algorithm Multi-agent domains

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Research on Airline Routs Network plannings based on Multi-objectives Optimization 24

Research on Airline Routs Network Plannings based on Multi-o...

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3rd International Conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Chen, Yudong Han, Junduo Zheng, Ruikang China Airport Planning & Design Inst Co Ltd Northwest Branch Xian Peoples R China CCCC First Highway Consultants Co Ltd Xian Peoples R China

ISBN: (纸本)9798400716959

At present, civil aviation transportation has rapidly increased and formed the air routes network, which can influence the travel cost of passengers. Route network planning not only has a great impact on the existing business activities of airlines, but also directly affects the company's future development and profitability. However, existing routes network is relatively scattered, and although the route coverage is wide, the depth is insufficient, which leaks the optimization method after the establishment of network. In this work, we use an innovative multi-objective optimization framework that combines a genetic algorithm and a simulated annealing algorithm to find the best trade-offs between multiple targets. Our approach takes into account not only the direct operating costs of the route, but also indirect costs such as the costs associated with flight delays and environmental taxes. Through the experimental analysis of different routes, we demonstrate the effectiveness of our method. The results show that our optimization framework can effectively improve the overall efficiency of the route network while reducing the environmental impact. In addition, the framework has good flexibility to adapt to changing market conditions and passenger needs.

关键词： Airline Routs Network planning algorithm Muti-objectives Optimization Genetic algorithm

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planning algorithm Based on Airborne Sensor for UAV to Track and Intercept Moving Target in Dynamic Environment

Planning Algorithm Based on Airborne Sensor for UAV to Track...

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2014中国制导、导航与控制学术会议

作者： Yun Sang Zhihao Cai Qing Lin Yingxun Wang School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191 China School of Automation Science and Electrical Engineering and with Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing100191 China

ISBN: (纸本)9781479946983

This paper describes a planning algorithm based on airborne for UAV to track and intercept moving target in the presence of dynamic obstacles and considering the maneuvering characteristics of the Unmanned Aerial Vehicle (UAV).In the context of ensuring airborne sensor (camera) to keep track of the target,the approach can generate time-optimal speed commands for UAV in dynamic environment to intercept the target after discovering *** filtering the velocity instruction and considering the acceleration performance to generate reachable velocity *** of this method is that the coordinates of the UAV and the coordinates of moving targets and obstacles in the range of airborne sensors can be obtained in real *** simulation and experimental result show that this method can effectively ensure the moving target tracking and interception in dynamic environment.

关键词： autonomous aerial vehicles cameras image sensors path planning target tracking UAV acceleration performance airborne sensor camera dynamic environment dynamic obstacles moving target interception moving target tracking planning algorithm time-optimal speed commands unmanned aerial vehicle maneuvering characteristics velocity instruction filtering Aircraft Aircraft manufacture Collision avoidance planning Robot sensing systems Target tracking

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Comparison of path planning algorithms for an unmanned aerial vehicle deployment under threats

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IFAC-PapersOnLine 2019年第13期52卷 1978-1983页

作者： Danancier, Kevin Ruvio, Delphine Sung, Inkyung Nielsen, Peter Department of Industrial Engineering Polytech Engineering School of Aix-Marseille University France Operations Research Group Department of Materials and Production Aalborg University Denmark

Following the massive interests in unmanned aerial vehicles (UAVs), various optimization algorithms have been proposed for a path planning problem that allow the units to navigate in a region filled with threats such as a radar detection in air defence systems. Among the algorithms, we address Dijkstra's algorithm and a heuristic algorithm for the path planning of a UAV. The algorithms are compared under various configurations of a region to navigate with respect to the optimality and the computational complexity of the algorithms. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Intelligent vehicle highway systems Air navigation Aircraft detection Antennas Computational complexity Heuristic algorithms Navigation Operations research Optimization Scheduling Tracking radar Unmanned aerial vehicles (UAV) Air defence systems Dijkstra's algorithms Optimality Optimization algorithms Optimization and control Path planning problems Path planning algorithm Radar detection

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An Effective Way to Large-Scale Robot-Path-planning Using a Hybrid Approach of Pre-Clustering and Greedy Heuristic

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APPLIED ARTIFICIAL INTELLIGENCE 2020年第14期34卷 1159-1175页

作者： Wang, W. C. Chen, R. Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 30013 Taiwan

Robot-path-planning seeks the shortest path to optimize the motion cost for robots. In robot-path-planning, the computational time will significantly increase if the moving targets rise largely, also known as the large-scale TSP. Hence, the current algorithms for the shortest path planning may be ineffective in the large-scale TSP. Aimed at the real-time applications that a robot must achieve as many goals as possible within limited time and the computational time of a robot has to be short enough to provide the next moving signal in time. Otherwise, the robot will be trapped into the idle status. This work proposes a hybrid approach, called the pre-clustering greedy heuristic, to tackle the reduction of computational time cost and achieve the near-optimal solutions. The proposed algorithm demonstrates how to lower the computational time cost drastically via smaller data of a sub-group, divided byk-means clustering, and the intra-cluster path planning. An algorithm is also developed to construct the nearest connections between any two unconnected clusters, ensuring the inter-cluster tour is the shortest. As a result, by utilizing the proposed heuristic, the computational time is significantly reduced and the path length is more efficient than the benchmark algorithms, while the input data grow up to a large scale. In applications, the proposed work can be applied practically to the path planning with large-scale moving targets, for example, the employment for the ball-collecting robot in a court.

关键词： Motion planning K-means clustering planning algorithm Robot

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Vehicle Safety planning Control Method Based on Variable Gauss Safety Field

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第11期13卷 203页

作者： Zhu, Zixuan Teng, Chenglong Cai, Yingfeng Chen, Long Lian, Yubo Wang, Hai Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China BYD Auto Ind Co Ltd Shenzhen 518118 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China

The existing intelligent vehicle trajectory-planning methods have limitations in terms of efficiency and safety. To overcome these limitations, this paper proposes an automatic driving trajectory-planning method based on a variable Gaussian safety field. Firstly, the time series bird's-eye view is used as the input state quantity of the network, which improves the effectiveness of the trajectory planning policy network in extracting the features of the surrounding traffic environment. Then, the policy gradient algorithm is used to generate the planned trajectory of the autonomous vehicle, which improves the planning efficiency. The variable Gaussian safety field is used as the reward function of the trajectory planning part and the evaluation index of the control part, which improves the safety of the reinforcement learning vehicle tracking algorithm. The proposed algorithm is verified using the simulator. The obtained results show that the proposed algorithm has excellent trajectory planning ability in the highway scene and can achieve high safety and high precision tracking control.

关键词： autonomous driving planning algorithm variable Gaussian safety field reinforcement learning policy gradient

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A Scalable Real-Time Multiagent Decision Making algorithm with Cost 26

A Scalable Real-Time Multiagent Decision Making Algorithm wi...

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26th IEEE Symposium on Computers and Communications (IEEE ISCC)

作者： Cotae, Paul Kang, Myong Velazquez, Alexander Univ Dist Columbia Dept Elect & Comp Engn Washington DC 20008 USA Naval Res Lab Ctr High Assurance Comp Syst Washington DC 20375 USA

ISBN: (纸本)9781665427449

We focus on a real-time multiagent decision making problem in a collaborative setting including a cost factor for the planning and execution of actions. We present the centralized coordination of a multiagent system in which the team must make a collaborative decision to maximize the global payoff. We used the framework of Coordination Graphs, which exploit dependencies among agents to decompose the global payoff function value as the sum of local terms. We revise the centralized Max-Plus algorithm by presenting a new Cost Max-Plus algorithm for planning and acting by including the cost in the local interactions of agents. We propose a two-step planning and acting algorithm called Factored Value-MCTS-Cost-Max-Plus algorithm that is online, anytime, and scalable in terms of the number of agents and their local interactions.

关键词： MCTS Max-Plus algorithm Multiagent System Decision Making planning algorithm

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Vision-based control architecture for human–robot hand-over applications

Vision-based control architecture for human–robot hand-over...

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Asian Journal of Control

作者： Melchiorre, Matteo Scimmi, Leonardo Sabatino Mauro, Stefano Pastorelli, Stefano Paolo Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy

Human–robot collaboration will be an essential part of the production processes in the factories of tomorrow. In this paper, a human–robot hand-over control strategy is presented. Human and robot can be both giver and receiver. A path-planning algorithm drives the robotic manipulator towards the hand of the human and permits to adapt the pose of the tool center point of the robot to the pose of the hand of the human worker. The movements of the operator are acquired with a multi 3D-sensors setup so to avoid any possible occlusion related to the presence of the robot or other dynamic obstacles. Estimation of the predicted position of the hand is performed to reduce the waiting time of the operator during the hand-over task. The hardware setup is described, and the results of experimental tests, conducted to verify the effectiveness of the control strategy, are presented and discussed. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Visual servoing End effectors Industrial manipulators Manipulators Robot programming Robotic arms Social robots Control strategies Dynamic obstacles Experimental test Path planning algorithm Production process Robotic manipulators Tool center points Vision based control

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Path planning inside Multi-Storey Buildings: an algorithm for Autonomous Mobile Robot

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IFAC-PapersOnLine 2018年第22期51卷 518-523页

作者： Baltashov, Ilia Semakova, Anna Russian State Scientific Center for Robotics and Technical Cybernetics Saint Petersburg Russia Russian State Scientific Center for Robotics and Technical Cybernetics Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia

A mobile robot travels inside a multi-storey building. The robot does not know an environment a priory; it explores each floor via lidar and SLAM method. As a result, it has access to the map of the entire building. The robot can also determine staircases and move along them using data from RGB-D camera. The objective is to reach a given location, which is specified by the operator based on the resulting map. We present a navigation and guidance strategy under which the robot autonomously reaches the desired location combining 2D path planning algorithm with staircase overpassing. Effectiveness of such strategy is also confirmed by real-world experiments with tracked robotic platform. © 2018

关键词： Motion planning Control engineering Mobile robots Navigation Robot programming Robotics Stairs Autonomous Mobile Robot Guidance strategy Localization Map Building Multistorey buildings Path planning algorithm Real world experiment Robotic platforms

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A Kansei Model of One-Day Circular Tour Considering Satisfaction and Tired Condition of Transportation 19

A Kansei Model of One-Day Circular Tour Considering Satisfac...

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19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT)

作者： Li, Shuang Sasaki, Jun Iwate Prefectural Univ Grad Sch Software & Informat Sci Takizawa Japan

ISBN: (纸本)9781643681153;9781643681146

The number of Foreign Independent Tour (FIT) is increasing rapidly in the world. The FIT tourists usually leave from one hotel as a base, travel around multiple sightseeing areas and return to the same hotel by using some public transportation methods in one day (we call it "one-day circular tour (OCT)"). However, due to the restriction on the public transport schedule, it is difficult to travel around multiple sightseeing areas efficiently with high satisfaction within a limited time. In our previous research, we proposed a planning algorithm for OCT to obtain high satisfaction using such public transportations. But, the satisfaction and tired condition of transportation was not considered there. In this paper, we propose a Kansei model for OCT including the satisfaction and tired condition of transportation and shows the evaluation results in a case study using persona method on travelers in Iwate Prefecture, Japan.

关键词： planning algorithm Kansei model Tourism recommendation system Decision making

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