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Learning Terminal State of the Trajectory Planner: Application for Collision Scenarios of Autonomous Vehicles

Learning Terminal State of the Trajectory Planner: Applicati...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Joonhee Lim Kibeom Lee Jangho Shin Dongsuk Kum Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Department of Mechanical Engineering Gachon University Seongnam Republic of Korea Research & Development Division Hyundai Motor Company Hwaseong Republic of Korea The Cho Chun Shik Graduate School of Mobility Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Collision Avoidance/Mitigation System (CAMS) for autonomous vehicles is a crucial technology that ensures the safety and reliability of autonomous driving systems. Conventional collision avoidance approaches struggle in complex and various scenarios by avoiding collisions based on rules for specific collision scenarios. This has led to learning-based methods using neural networks for adaptive collision avoidance. However, the approaches directly outputting control inputs through neural networks have drawbacks in interpretability and stability. To address these limitations, we propose a trajectory planning method for CAMS that combines deep reinforcement learning (DRL) and quintic polynomial (QP) trajectory planning. The proposed method determines the terminal state and confidence of the trajectory using DRL and plans a QP trajectory based on them. By utilizing the terminal state and confidence of the trajectory rather than direct control inputs as the output of the neural network, it generates a more realistic and continuous path. Moreover, this approach considers collision avoidance and mitigation in an integrated manner through the reward function of RL. Our experimental results demonstrate that the proposed method not only improves interpretability and stability compared to existing learning-based methods but also upholds performance in complex and various collision scenarios.

关键词： Learning systems Trajectory planning Neural networks Trajectory Cams Safety Reliability

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Design of Sliding Mode Linear Active Disturbance Rejection Quadcopter UAV Controller based on Particle Swarm Optimization

Design of Sliding Mode Linear Active Disturbance Rejection Q...

引用

robotics, Control and automation Engineering (RCAE), international conference on

作者： Qianqian Xu Yuehao Yan Qiang Han Bohao Wang Youhua Tian School of Automation and Information Engineering Sichuan University of Science and Engineering Yibin China School of Electronic and Electrical Engineering Civil Aviation Flight University of China Artificial Intelligence Key Laboratory of Sichuan Province DeYang China School of Automation and Information Engineering Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province Yibin China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

To address the nonlinear, underactuated, and strongly coupled dynamics of quadrotor UAV systems, this paper proposes a control strategy. The designed controller combines sliding mode control (SMC) with linear active disturbance rejection control (LADRC) to effectively handle significant external disturbances. The controller employs a linear extended state observer (LESO) to estimate model uncertainties and external disturbances, while SMC is utilized to address the reduction in control precision caused by the bandwidth limitations of LESO. To enhance the efficiency of parameter tuning, the particle swarm optimization (PSO) algorithm is introduced, overcoming the inefficiencies of traditional empirical methods. Simulation results demonstrate that the proposed method exhibits strong disturbance rejection and fast response in altitude and attitude control of quadrotor UAVs. Compared to standalone PID control and LADRC, this approach significantly improves the attitude control performance, stability, and robustness of the quadrotor UAV.

关键词： Robust control Simulation Bandwidth Autonomous aerial vehicles Stability analysis Robustness Particle swarm optimization Tuning Sliding mode control Quadrotors

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Online Connectivity-aware Dynamic Deployment for Heterogeneous Multi-Robot Systems

Online Connectivity-aware Dynamic Deployment for Heterogeneo...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lin, Chendi Luo, Wenhao Sycara, Katia Carnegie Mellon Univ Inst Robot Sch Comp Sci Pittsburgh PA 15213 USA

ISBN: (纸本)9781728190778

In this paper, we consider the dynamic multi-robot distribution problem where a heterogeneous group of networked robots is tasked to spread out and simultaneously move towards multiple moving task areas while maintaining connectivity. The heterogeneity of the system is characterized by various categories of units and each robot carries different numbers of units per category representing heterogeneous capabilities. Every task area with different importance demands a total number of units contributed by all of the robots within its area. Moreover, we assume the importance and the total number of units requested from each task area is initially unknown. The robots need first to explore, i.e., reach those areas, and then be allocated to the tasks so to fulfill the requirements. The multi-robot distribution problem is formulated as designing controllers to distribute the robots that maximize the overall task fulfillment while minimizing the traveling costs in presence of connectivity constraints. We propose a novel connectivity-aware multi-robot redistribution approach that accounts for dynamic task allocation and connectivity maintenance for a heterogeneous robot team. Such an approach could generate sub-optimal robot controllers so that the amount of total unfulfilled requirements of the tasks weighted by their importance is minimized and robots stay connected at all times. Simulation and numerical results are provided to demonstrate the effectiveness of the proposed approaches.

关键词： Greedy algorithms Costs conferences Maintenance engineering Dynamic scheduling Numerical models Resource management

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Learning Seed Placements and automation Policies for Polyculture Farming with Companion Plants

Learning Seed Placements and Automation Policies for Polycul...

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IEEE international conference on robotics and automation (ICRA)

作者： Avigal, Yahav Deza, Anna Wong, William Oehme, Sebastian Presten, Mark Theis, Mark Chui, Jackson Shao, Paul Huang, Huang Kotani, Atsunobu Sharma, Satvik Parikh, Rishi Luo, Michael Mukherjee, Sandeep Carpin, Stefano Viers, Joshua H. Vougioukas, Stavros Goldberg, Ken Univ Calif Berkeley AUTOLab Berkeley CA USA Tech Univ Munich Dept Elect & Comp Engn Munich Germany UC Merced Sch Engn Merced CA 95343 USA UC Merced Sch Engn Environm Syst Merced CA USA Univ Calif Davis Biol & Agr Engn Davis CA USA

ISBN: (纸本)9781728190778

Polyculture farming is a sustainable farming technique based on synergistic interactions between differing plant types that make them more resistant to diseases and pests and better able to retain water. Reduced uniformity can reduce use of pesticides, fertilizer, and water, but is more labor intensive and more challenging to automate. We describe a scaled physical testbed (1.5m x3.0m) that uses a high resolution camera and soil sensors to monitor polyculture plants to facilitate tuning of plant growth, companion effects, and irrigation parameters for a first-order garden simulator. We use this simulator to develop a novel seed placement algorithm that increases coverage and diversity, and a learned pruning policy. In simulation experiments, the seed placement algorithm yields 60% more coverage and 10% more diversity than random seed placement and the learned pruning policy runs 1000X faster than a procedural lookahead policy to achieve high leaf coverage and plant diversity on adversarial gardens that include plant species with diverse growth rates. These models and policies provide the groundwork for a fully-automated system under development. Code, datasets and supplementary material can be found at https://***/Berkeleyautomation/AlphaGarden/.

关键词： Resistance Irrigation automation Stochastic processes Tools Soil Sensors

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Uncertainty-Aware Shape Estimation of a Surgical Continuum Manipulator in Constrained Environments using Fiber Bragg Grating Sensors

Uncertainty-Aware Shape Estimation of a Surgical Continuum M...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Alexander Schwarz Arian Mehrfard Golchehr Amirkhani Henry Phalen Justin H. Ma Robert B. Grupp Alejandro Martin Gomez Mehran Armand Biomechanical- and Image-Guided Surgical Systems (BIGSS) Lab LCSR at the Whiting School of Engineering Johns Hopkins University Baltimore MD USA Technical University of Munich Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Continuum Dexterous Manipulators (CDMs) are well-suited tools for minimally invasive surgery due to their inherent dexterity and reachability. Nonetheless, their flexible structure and non-linear curvature pose significant challenges for shape-based feedback control. The use of Fiber Bragg Grating (FBG) sensors for shape sensing has shown great potential in estimating the CDM’s tip position and subsequently reconstructing the shape using optimization algorithms. This optimization, however, is under-constrained and may be ill-posed for complex shapes, falling into local minima. In this work, we introduce a novel method capable of directly estimating a CDM’s shape from FBG sensor wavelengths using a deep neural network. In addition, we propose the integration of uncertainty estimation to address the critical issue of uncertainty in neural network predictions. Neural network predictions are unreliable when the input sample is outside the training distribution or corrupted by noise. Recognizing such deviations is crucial when integrating neural networks within surgical robotics, as inaccurate estimations can pose serious risks to the patient. We present a robust method that not only improves the precision upon existing techniques for FBG-based shape estimation but also incorporates a mechanism to quantify the models’ confidence through uncertainty estimation. We validate the uncertainty estimation through extensive experiments, demonstrating its effectiveness and reliability on out-of-distribution (OOD) data, adding an additional layer of safety and precision to minimally invasive surgical robotics.

关键词： Uncertainty Minimally invasive surgery Medical robotics Shape Estimation Fiber gratings Robot sensing systems

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Research on Intelligent Optimisation Decision Making of Production Process Based on Adaptive Sampling and Genetic Algorithm

Research on Intelligent Optimisation Decision Making of Prod...

引用

Electrical, automation and Computer Engineering (ICEACE), international conference on

作者： Bohan Zhang Beijing Jiaotong University Beijing China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper focuses on the application of optimisation algorithms for intelligent optimisation decisions in production processes. Focusing on adaptive sampling methods, 0-1 planning models and genetic algorithms construct mathematical models to optimise the decision-making of inspection and disassembly and maximise the revenue index. Firstly, the simulation generates spare parts data and compares the four sampling and detection methods, and finally identifies adaptive stepwise sampling as the best choice; secondly, the decision variables are analysed by the 0-1 planning model to establish a functional relationship between the revenue index and the cost and sales, and to identify the key influencing factors; and finally, the 0-1 integer planning model is optimised to search for the optimal decision combinations by using the global optimisation algorithm. Sampling errors are introduced to verify the stability of the adaptive stepwise sampling method in decision making and its effect on the revenue index.

关键词： Adaptation models Decision making Stability criteria Production Sampling methods Mathematical models Planning Indexes Optimization Genetic algorithms

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Flight Control of Aerial Robots for Wire-Hanging Under Wind Disturbances

Flight Control of Aerial Robots for Wire-Hanging Under Wind ...

引用

robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Dazhi Wang Zhong Yang Xiantan Wang Jiahui He College of Automation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

To enhance the efficiency of power transmission line inspections, this paper designs a novel aerial robot capable of hanging line operations and proposes an automatic hanging line flight control method. The robot's structure is designed, and its mathematical model is established using the Newton-Euler method, along with a wind field model for accurate wind disturbance simulation. To address nonlinear coupling and wind disturbances, a position controller based on Linear Active Disturbance Rejection Control (LADRC) is developed, and system stability is proven. Simulation results demonstrate that the LADRC controller ensures stable and robust position control, enabling the aerial robot to perform hanging line tasks effectively under wind disturbances.

关键词： Robust control Wind speed Simulation Power system stability Autonomous aerial vehicles Mathematical models Stability analysis Robots Tuning Aerospace control

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Experience Consistency Distillation Continual Reinforcement Learning for Robotic Manipulation Tasks

Experience Consistency Distillation Continual Reinforcement ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Chao Zhao Jie Xu Ru Peng Xingyu Chen Kuizhi Mei Xuguang Lan Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Continual reinforcement learning, which aims to help robots acquire skills without catastrophic forgetting, obviating the need to re-learn all tasks from scratch. In order to enable lifelong acquisition of skills in robots, replay-based continual reinforcement learning has emerged as a promising research direction. These techniques replay data from previous tasks to mitigate forgetting when learning new skills. However, existing replay-based methods store poor representative experience, and the experience utilization of old tasks is inefficient. To address these issues, we propose an experience consistency distillation method for robot continual reinforcement learning to improve the data efficiency of the experience. Specifically, the experience of old tasks are distilled to obtain Markov Decision Process (MDP) data with high compression ratio and information content. To ensure consistent data distributions before and after distillation, we further utilize a Fréchet Inception Distance (FID) loss as a regularization constraint. In order to improve experience utilization efficiency, the policy is then trained using both the distilled data and current task data, with policy distillation performed based on uncertainty metrics. Our method is validated in the continual reinforcement learning simulation platform and real scene with a UR5e robot arm. Experimental results indicate that our method achieves higher success and lower buffer size requirement compared to other methods.

关键词： Measurement Uncertainty Markov decision processes Reinforcement learning Manipulators Task analysis Robots

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First Order Logic for Command Understanding

First Order Logic for Command Understanding

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international conference on Autonomous Robots and Agents, ICARA

作者： Adrian Groza Department of Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

When given requests in natural language, ideally, the robot should have a single interpretation of that command. However, the more generic the request, the more potential execution pathways it may entail. To address this challenge, we introduce a two-step method designed to diminish the multitude of conceivable interpretations for natural language commands directed at a robot. In the first step, the request is translated into First Order Logic (FOL), subsequently generating interpretations that align with the agent's existing knowledge base. The interpretations are derived using MACE4, a tool for identifying finite models of FOL theories. In the second step, the agent systematically reduces the pool of interpretations hrough the formulation of informative queries directed towards the human agent. The effectiveness of these queries is assessed based on the information gain metric, adapted to suit finite interpretation models associated with FOL theories. We present a practical demonstration within the kitchen domain, wherein our approach is exemplified through the utilization of AbeSim, a robot simulation tool. Our research contribution primarily entails the utilization of the information gain metric to prioritize and rank queries based on their effectiveness in minimizing the multitude of interpretation models. This innovative methodology offers a promising solution for mitigating ambiguity within natural language commands issued to robotic systems.

关键词： Measurement Adaptation models automation Natural languages Knowledge based systems Logic Robots

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Collaborative Learning of Multiple-Discontinuous-Image Saliency Prediction for Drone Exploration

Collaborative Learning of Multiple-Discontinuous-Image Salie...

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IEEE international conference on robotics and automation (ICRA)

作者： Chu, Ting-Tsan Chen, Po-Heng Huang, Pin-Jie Chen, Kuan-Wen Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu 300 Taiwan Natl Chiao Tung Univ Dept Comp Sci Hsinchu 300 Taiwan

ISBN: (纸本)9781728190778

Most of the existing saliency prediction research focuses on either single images or videos (or more precisely multiple images in sequence). However, to apply saliency prediction to drone exploration that has to consider multiple images from different view angles or localizations to determine the direction to explore, saliency prediction over multiple discontinuous images is required. In this paper, we propose a deep relative saliency model (MS-Net) for such an application. MS-Net starts with a single-image saliency feature extraction network for each image separately and then integrate these images by using a GCN-based mechanism called multi-image saliency fusion that learns relative saliency information among all the images. Finally, it predicts the saliency of each image by considering the relative information. Because there are no existing saliency prediction datasets with such multiple discontinuous images, we randomly cropped a large number of sub-images from 360 degrees images of the existing 360 degrees image saliency datasets to build our own dataset for both training and evaluation. Experimental results showed that the proposed MS-Net considerably outperformed both single-image and video saliency prediction methods and could achieve comparative performance to that of 360 degrees image saliency prediction even with only limited field-of-views, i.e., five sub-images, considered.

关键词： Training Location awareness automation conferences Prediction methods Feature extraction Collaborative work

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