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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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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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Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection

Viewer-Centred Surface Completion for Unsupervised Domain Ad...

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IEEE international conference on robotics and automation (ICRA)

作者： Darren Tsai Julie Stephany Berrio Mao Shan Eduardo Nebot Stewart Worrall Australian Centre for Field Robotics (ACFR) at the University of Sydney NSW Australia

Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are trained on one dataset and tested on another. We observe that lidar scan pattern differences form a large component of this reduction in performance. We address this in our approach, SEE-VCN, by designing a novel viewer-centred surface completion network (VCN) to complete the surfaces of objects of interest within an unsupervised domain adaptation framework, SEE [1]. With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns. By adopting a domain-invariant representation, SEE-VCN can be classed as a multi-target domain adaptation approach where no annotations or re-training is required to obtain 3D detections for new scan patterns. Through extensive experiments, we show that our approach outperforms previous domain adaptation methods in multiple domain adaptation settings. Our code and data are available at https://***/darrenjkt/SEE-VCN.

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A Framework for Learning Behavior Trees in Collaborative Robotic Applications

A Framework for Learning Behavior Trees in Collaborative Rob...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Matteo Iovino Jonathan Styrud Pietro Falco Christian Smith ABB Corporate Research Västerås Sweden Division of Robotics Perception and Learning KTH - Royal Institute of Technology Stockholm Sweden ABB Robotics Västerås Sweden

In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behavior Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot.

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Research on the Development Status and Future Trends of Foreign Unmanned Ground Equipment

Research on the Development Status and Future Trends of Fore...

引用

Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Mingming Shi Xin He Haoyuan Li Hao Zhang Peng Lu Ji Ma Wuyi Liu Wenchao Wei Unit No 32322 PLA Urumqi China School of Information Systems Engineering Strategic Support Force Information Engineering University Zhengzhou China Department of UAV Engineering Army Engineering University Shijiazhuang China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In recent years, it has been observed that the army's intelligent and unmanned weapons and equipment have played a remarkable role in close combat during local wars, armed conflicts, and counter-terrorism operations. Furthermore, their utilization has evolved from individual soldiers and sporadic usage to cluster deployment. Combining with the operational situation, this paper divided the development of foreign unmanned ground equipment (UGE) into three stages (application exploring, gradual maturity and dominant battle), and analyzed the main characteristics of UGE's overall development (top-level design for traction, supporting construction for foundation and technology advantage for guidance). At the same time, the classification methods of foreign UGE were discussed in detail according to the classification of function (reconnaissance and surveillance equipment, fire strike equipment and comprehensive support equipment), and the development trends of foreign UGE were summarized as intelligent, modularization, swarm, flexibility, stealth and autonomy. The aim of this paper is to provide a theoretical reference for advancing the construction of the armyUGE.

关键词： Control engineering automation Service robots Weapons Surveillance Robot kinematics Standardization Reconnaissance Market research Sustainable development

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CLIPPER: A Graph-Theoretic Framework for Robust Data Association

CLIPPER: A Graph-Theoretic Framework for Robust Data Associa...

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IEEE international conference on robotics and automation (ICRA)

作者： Lusk, Parker C. Fathian, Kaveh How, Jonathan P. MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

ISBN: (纸本)9781728190778

We present CLIPPER (Consistent Linking, Pruning, and Pairwise Error Rectification), a framework for robust data association in the presence of noise and outliers. We formulate the problem in a graph-theoretic framework using the notion of geometric consistency. State-of-the-art techniques that use this framework utilize either combinatorial optimization techniques that do not scale well to large-sized problems, or use heuristic approximations that yield low accuracy in high-noise, high-outlier regimes. In contrast, CLIPPER uses a relaxation of the combinatorial problem and returns solutions that are guaranteed to correspond to the optima of the original problem. Low time complexity is achieved with an efficient projected gradient ascent approach. Experiments indicate that CLIPPER maintains a consistently low runtime of 15 ms where exact methods can require up to 24 s at their peak, even on small-sized problems with 200 associations. When evaluated on noisy point cloud registration problems, CLIPPER achieves 100% precision and 98% recall in 90% outlier regimes while competing algorithms begin degrading by 70% outliers. In an instance of associating noisy points of the Stanford Bunny with 990 outlier associations and only 10 inlier associations, CLIPPER successfully returns 8 inlier associations with 100% precision in 138 ms. Code is available at https://***/clipper.

关键词： Runtime Codes automation conferences Noise measurement Time complexity Optimization

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

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

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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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Galaxy Morphology Classification with DenseNet 4

Galaxy Morphology Classification with DenseNet

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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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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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