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Titlebook: Robot Intelligence Technology and Applications 7; Results from the 10t Jun Jo,Han-Lim Choi,Bela Stantic Conference proceedings 2023 The Edi

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書(shū)目名稱(chēng)Robot Intelligence Technology and Applications 7
副標(biāo)題Results from the 10t
編輯Jun Jo,Han-Lim Choi,Bela Stantic
視頻videohttp://file.papertrans.cn/832/831051/831051.mp4
概述Presents recent research on Robot Intelligence Technology and Applications.Discusses the results from the 10th International Conference on Robot Intelligence Technology and Applications.Written by exp
叢書(shū)名稱(chēng)Lecture Notes in Networks and Systems
圖書(shū)封面Titlebook: Robot Intelligence Technology and Applications 7; Results from the 10t Jun Jo,Han-Lim Choi,Bela Stantic Conference proceedings 2023 The Edi
描述.We are starting to enter a post-COVID-19 life. While this pandemic has made everyone’s life challenging, it also expedited the transition of our everyday lives into a new form, often called “The New Normal.” Although many people often use the terminology, perhaps we still do not have consensus about what it is and what it should be like. However, one thing that is clear namely that robotics and artificial intelligence technologies are playing a critical role in this transition phase of our everyday lives. We saw the emergence of last-mile delivery robots on the street, AI-embedded service robots in restaurants, uninhabited shops, non-face-to-face medical services, conferences and talks in metaverses, and AI-based online education programs...This book is an edition that aims at serving researchers and practitioners in related fields with a timely dissemination of the recent progress in the areas of robotics and artificial intelligence. This book is based on a collection of papers presented at the 10th International Conference on Robot Intelligence Technology and Applications (RiTA), held at Griffith University in the Gold Coast, Queensland, Australia. The conference was held in a h
出版日期Conference proceedings 2023
關(guān)鍵詞Intelligent Systems; Robot Intelligence Technology; Robot Intelligence; Robot Intelligence Applications
版次1
doihttps://doi.org/10.1007/978-3-031-26889-2
isbn_softcover978-3-031-26888-5
isbn_ebook978-3-031-26889-2Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Nonlinear Autoregressive with?Gaussian Process Regression-Based Path-Following Guidance for?UAV Undebances such as wind. We propose an adaptive guidance algorithm consisting of two parts: a baseline path-following guidance law with optimal error dynamics and a nonlinear autoregressive with Gaussian process regression (GP-NAR). Firstly, a baseline path-following guidance law is constructed by adopt
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CEO-MLCPP: Control-Efficient and?Obstacle-Aware Multi-Layer Coverage Path Planner for?3D ReconstructAV). The proposed method rapidly calculates initial viewpoints considering the ground sampling distance (GSD) by partitioning a structure by height. Then, optimal viewpoints are selected by checking the collision and calculating overlaps and coverage. Next, the newly developed collision-aware Travel
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Sampling-Based Path Planning Algorithm Considering Moving Obstacles Based on?RRT* obstacles, we need to also consider moving obstacles in a dynamic environment. In multi-agent systems, these obstacles often have known trajectory that must be avoided. Related works in sampling-based algorithms proposed a re-planning mechanism to consider the moving obstacle. However, those algori
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Path Following Algorithm with?Small Error for?Guide Robotonment, and the robot moves along the waypoints. The conventional path-following methods, such as the pure pursuit and the parallelogram, overshoot when the waypoint is not straight. The proposed method has three states depending on the robot’s position, angle, and path for reducing the trajectory d
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MDP Formulation for?Multi-UAVs Mission Planning with?Refueling Constraintsation of multi-agent mission planning. Using the MDP formulation can make persistent mission planning with refueling constraints. The state space of MDP formulation consists of agents’ locations and the uncertainty. In order to avoid an enormous computation, refueling constraint is excluded for a st
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A*-Based Path Planning for?Legged Robots environmental conditions while making the most of the ability to move between non-adjacent terrains. This paper presents an A*-based path-planning algorithm for legged robots. The heuristic functions in A* is constructed mainly based on preference term for long-distance travel and logarithmic barri
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