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Active Inference for Reactive Temporal Logic Motion Planning

Active Inference for Reactive Temporal Logic Motion Planning

引用

IEEE international conference on robotics and automation (ICRA)

作者： Ziyang Chen Zhangli Zhou Lin Li Zhen Kan Department of Automation University of Science and Technology of China Hefei China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Reactive planning enables the robots to deal with dynamic events in uncertain environments. However, existing methods heavily rely on the predefined hard-coded robot behaviors, e.g, a pre-coded temporal logic formula that specifies how robot should react. Little attention has been paid for autonomous generation of reactive tasks specifications during the runtime. As a first attempt towards this goal, this work develops a real-time decision-making and motion planning framework. It allows the robot to follow a global task planned offline while taking proactive decisions and generating temporal logic specifications for local reactive tasks when encountering dynamic events. Specifically, inspired by the causal knowledge graph, a proposition graph is developed, based on which the decision module encode the environment and the task as the Boolean logic and linear temporal logic (LTL), respectively. Based on the established proposition graph and perceived environment, the agent can autonomously generate an LTL formula to realize the local temporary task. A joint sampling algorithm is then developed, in which the automaton states of local and global task are jointly considered to generate a feasible planning that satisfies both global and local tasks. Experiments demonstrate the effectiveness of the proposed decision-making and motion planning.

关键词： Runtime Decision making Dynamics Automata Knowledge graphs Real-time systems Inference algorithms

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Accessibility Framework for Determining Collisions and Coverage for Radiation Scanning 25th

Accessibility Framework for Determining Collisions and Cov...

引用

25th Annual conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Bettles, Joshua West, Andrew Andrew, Jeremy Darby, Iain Lennox, Barry Manchester Centre for Robotics and AI The University of Manchester ManchesterM13 9PL United Kingdom Nuclear Restoration Services Ltd. Dounreay Thurso CaithnessKW14 7TZ United Kingdom National Nuclear Laboratory Chadwick House Birchwood Park Warrington CheshireWA3 6AE United Kingdom School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783031720581

Radioactive contamination monitoring is an important part of radiological protection. automation of surface monitoring poses difficulties, with a major challenge being determining the coverage of a radiation probe over an object in close proximity without collision. We propose a new accessibility framework to determine if radiation probes, modelled as convex hulls, collide with 3D point clouds representing the objects. We explain how to structure and analyze point clouds to extract properties such as the surface normal for each point. Our method for approximating radiation probes is demonstrated using the BP4 probe. This approach models both the probe and the sensor’s effective scan volume with geometric primitives, providing a computationally efficient way to detect collisions with flat surfaces. The accessibility assessment builds on common methods within computer science for determining intersections. A laptop in various positions was used to demonstrate that the framework can efficiently categorise points as accessible or inaccessible, identifying unscannable regions. The output of this framework can then be used to plan collision-free paths over objects and will be the foundation of a robotic survey assistant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Probes

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Adaptive Robust Optimization Based on Data-driven Uncertainties of Source and Load for Microgrid Operation 1

Adaptive Robust Optimization Based on Data-driven Uncertaint...

引用

1st international conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2022

作者： Li, Zibin Tan, Mao Ren, Yuling Jiang, Hongwei Xiangtan University School of Automation and Electronic Information Xiangtan China Xiangtan University Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Xiangtan China Xiangtan University Hunan National Center for Applied Mathematics Xiangtan China

ISBN: (纸本)9781665452151

The strong uncertainty of renewable energy output and load demand makes the stable operation of microgrids a challenging and important issue. However, the scheduling methods based on deterministic models cannot accurately describe the influence of uncertainties on operation of microgrids. To address this problem, this paper proposes a two-stage adaptive robust optimal scheduling model (TSARO) that considers both source and load uncertainties. The model first adopts the dirichlet process mixture model (DPMM) to perform cluster analysis and parameter estimation on massive historical data, and constructs a data-driven uncertainty set of source and load. Then, based on the uncertainty set, the TSARO model aiming at minimizing the microgrid operation cost is developed under the worst-case scenario. Finally, this paper solves the optimization model using column constraint generation algorithm (C&CG) to obtain the day-ahead power dispatching plan. Simulation results show that the proposed model in this paper has better economic benefits compared with several classical optimization models. © 2023 IEEE.

关键词： Cluster analysis

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Path Planning in Uncertain Ocean Currents using Ensemble Forecasts

Path Planning in Uncertain Ocean Currents using Ensemble For...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yoo, Chanyeol Lee, James Ju Heon Anstee, Stuart Fitch, Robert Univ Technol Sydney Sydney NSW Australia Dept Defence Def Sci & Technol Grp Canberra ACT Australia

ISBN: (纸本)9781728190778

We present a path planning framework for marine robots subject to uncertain ocean currents that exploits data from ensemble forecasting, which is a technique for current prediction used in oceanography. Ensemble forecasts represent a distribution of predicted currents as a set of flow fields that are considered to be equally likely. We show that the typical approach of computing the vector-wise mean and variance over this set can yield meaningless results, and propose an alternative approach that considers each flow field in the ensemble simultaneously. Our framework finds a sequence of vehicle controls that minimises the root-mean-square error distance (RMSE) over the full set of ensemble-induced trajectories. The key to achieving computational efficiency in this approach is our use of Monte Carlo tree search (MCTS) with a specialised heuristic that improves convergence rate while preserving asymptotic optimality and the anytime property. We demonstrate our results using real ensemble forecasts provided by the Australian Bureau of Meteorology, and provide comparisons with the deterministic mean-based approach where we observe RMSE reductions of 92% and 43% in two example scenarios. Further, we argue that the framework can be used in a plan-as-you-go manner where ensemble forecasts change over time. These results help to introduce ensemble forecasts as a viable source of data to improve path planning in marine robotics.

关键词： Monte Carlo methods Oceans Weather forecasting Estimation Probabilistic logic Forecast uncertainty Robustness

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Handling Object Symmetries in CNN-based Pose Estimation

Handling Object Symmetries in CNN-based Pose Estimation

引用

IEEE international conference on robotics and automation (ICRA)

作者： Richter-Klug, Jesse Frese, Udo Univ Bremen Fac Math & Comp Sci D-283569 Bremen Germany

ISBN: (纸本)9781728190778

In this paper, we investigate the problems that Convolutional Neural Networks (CNN)-based pose estimators have with symmetric objects. We considered the value of the CNN's output representation when continuously rotating the object and found that it has to form a closed loop after each step of symmetry. Otherwise, the CNN (which is itself a continuous function) has to replicate an uncontinuous function. On a 1-DOF toy example we show that commonly used representations do not fulfill this demand and analyze the problems caused thereby. In particular, we find that the popular min-oversymmetries approach for creating a symmetry-aware loss tends not to work well with gradient-based optimization, i.e. deep learning. We propose a representation called "closed symmetry loop" (csl) from these insights, where the angle of relevant vectors is multiplied by the symmetry order and then generalize it to 6-DOE The representation extends our algorithm from [1] including a method to disambiguate symmetric equivalents during the final pose estimation. The algorithm handles continuous rotational symmetry (e.g. a bottle) and discrete rotational symmetry (e.g. a 4-fold symmetric box). It is evaluated on the T-LESS dataset, where it reaches state-of-the-art for unrefining RGB-based methods.

关键词： automation conferences Pose estimation Toy manufacturing industry Convolutional neural networks Optimization

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Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input

Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation fro...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yiye Chen Yunzhi Lin Ruinian Xu Patricio A. Vela School of Electrical and Computer Engineering Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA

The success of 6-DoF grasp learning with point cloud input is tempered by the computational costs resulting from their unordered nature and pre-processing needs for reducing the point cloud to a manageable size. These properties lead to failure on small objects with low point cloud cardinality. Instead of point clouds, this manuscript explores grasp generation directly from the RGB-D image input. The approach, called Keypoint-GraspNet (KGN), operates in perception space by detecting projected gripper keypoints in the image, then recovering their SE(3) poses with a $\mathrm{P}n\mathrm{P}$ algorithm. Training of the network involves a synthetic dataset derived from primitive shape objects with known continuous grasp families. Trained with only single-object synthetic data, Keypoint-GraspNet achieves superior result on our single-object dataset, comparable performance with state-of-art baselines on a multi-object test set, and outperforms the most competitive baseline on small objects. Keypoint-GraspNet is more than 3x faster than tested point cloud methods. Robot experiments show high success rate, demonstrating KGN's practical potential.

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Galaxy Morphology Classification with DenseNet 4

Galaxy Morphology Classification with DenseNet

引用

2022 4th international Symposium on robotics and Intelligent Manufacturing Technology, ISRIMT 2022

作者： Hui, Wuyu Robert Jia, Zheng Li, Hansheng Wang, Zijian School of Electronic and Automation Anhui University Hefei China College of Liberal Arts Computer Science University of Minnesota MN United States Division of Science and Technology Data Science BNU-HKBU United International College Zhuhai China School of Mathematical Sciences Capital Normal University Beijing China

Galaxy classification is crucial in astronomy, as galaxy types reveal information on how the galaxy was formed and evolved. While manually conducting the classification task requires extensive background knowledge and is time-consuming, deep learning algorithms provide a time-efficient and expedient way of accomplishing this task. Hence, this paper utilizes transfer learning from pre-trained CNN models and compares their performances on the Galaxy10 DECals Dataset. This paper applies opening operation, data augmentation, class weights, and learning rate decay to further improve the models' performance. In our experiments, DenseNet121 outperforms the other models and achieved approximately 89% test-set accuracy within 30 minutes. The second best-performing model, EfficientNetV2S, takes double the time achieving 2.43% lower test set accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Galaxies

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A Vision-Based Two-Stage Fruit Outer Diameter Detection Method

A Vision-Based Two-Stage Fruit Outer Diameter Detection Meth...

引用

IEEE international conference on robotics and Biomimetics

作者： Yifei Hao Shuaizheng Yan Zhengxing Wu Bingwei He Chenghua Li Junzhi Yu Department of Mechanical Engineering Fuzhou University Fuzhou China Laboratory of Cognitive and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Guangzhou Shenghui Cleaning Service Co. Ltd. Guangzhou China Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Regarding the mobile monitoring system in agricultural automation, the outer diameter monitoring of greenhouse vegetables has significant implications for accurately regulating fruit growth and cultivation plans. In this paper, a novel vision-based two-stage outer diameter detection method is proposed, including the segmentation and outer diameter calculation of greenhouse tomato images. To address the issues of multiple tomatoes in a single image and the similarity in color between tomatoes and the background, a YOLOv8 network embedded with convolutional block attention module is constructed for detection and segmentation in the first stage. Additionally, to solve the problem of inaccurate outer diameter detection caused by different tomato poses and pedicles, a defined central axis method is proposed, which can adaptively filter out the maximum outer diameter candidates and calculate accurate values utilizing depth information in the world coordinate system. The backbone network is fine-tuned on the pre-trained YOLOv8 model with 1000 tomato images. Extensive measurement experiments, involving 215 tomatoes in 80 images, is conducted to validate the effectiveness of our proposed approach. Compared to the ground truth, we obtain a measurement error of 4.27 mm, the coefficient of determination value R2 is 0.9349.

关键词： Image segmentation Measurement errors Adaptation models automation Image color analysis Robot kinematics Biomimetics Adaptive filters Information filters Monitoring

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Disturbance Observation and Control of Operational Unmanned Aerial Vehicles

Disturbance Observation and Control of Operational Unmanned ...

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IEEE international conference on Unmanned Systems (ICUS)

作者： Guanyu Yao Liying Yang Yanchun Chang Yuqing He School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

UAVs are used widely in all works and life, and their operation forms and application scenarios are more complex than ever before. In order to deal with various disturbances faced by operational UAVs in application scenarios and achieve stable control, a disturbance observation and compensation structure combining linear extended state observer and sliding mode controller is proposed to achieve stable control of operational UAVs. Firstly, the typical operational UAV is taken as the research object, and the kinematics and dynamics models are established. Then, a linear extended state observer is designed to estimate the environment and body disturbances, and the sliding mode control method is combined to control the system. Finally, the effectiveness of the designed control method is verified through simulation.

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Safe POMDP Online Planning via Shielding

Safe POMDP Online Planning via Shielding

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IEEE international conference on robotics and automation (ICRA)

作者： Shili Sheng David Parker Lu Feng School of Engineering and Applied Science University of Virginia Charlottesville VA USA Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Partially observable Markov decision processes (POMDPs) have been widely used in many robotic applications for sequential decision-making under uncertainty. POMDP online planning algorithms such as Partially Observable Monte-Carlo Planning (POMCP) can solve very large POMDPs with the goal of maximizing the expected return. But the resulting policies cannot provide safety guarantees which are imperative for real-world safety-critical tasks (e.g., autonomous driving). In this work, we consider safety requirements represented as almost-sure reach-avoid specifications (i.e., the probability to reach a set of goal states is one and the probability to reach a set of unsafe states is zero). We compute shields that restrict unsafe actions which would violate the almost-sure reach-avoid specifications. We then integrate these shields into the POMCP algorithm for safe POMDP online planning. We propose four distinct shielding methods, differing in how the shields are computed and integrated, including factored variants designed to improve scalability. Experimental results on a set of benchmark domains demonstrate that the proposed shielding methods successfully guarantee safety (unlike the baseline POMCP without shielding) on large POMDPs, with negligible impact on the runtime for online planning.

关键词： Runtime Uncertainty Monte Carlo methods Scalability Benchmark testing Planning Safety

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