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检索条件"任意字段=International Symposium on Computational Intelligence in Robotics and Automation"

共 3099 条记录，以下是441-450 订阅

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Gradient-Based Online Trajectory Planning for Quadrotor Transportation Systems

Gradient-Based Online Trajectory Planning for Quadrotor Tran...

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IEEE international Conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Tengfei Pei Ying Jin Hai Yu Yongchun Fang Jianda Han Xiao Liang Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin China

Trajectory planning has been an important research area for quadrotor transportation systems for a long time. However, generating feasible trajectories in real-time that allow quadrotor transportation systems to navigate safely in complex environments remains a challenging issue. This article proposes an efficient gradient-based safety constraint that helps keep the system away from obstacles. Furthermore, this paper combines safety constraint with a novel B-spline-based optimization method that utilizes an improved hybrid-state A* algorithm for path searching to generate desired trajectory. Strategies to speed up path searching and unconstrained optimization problem constructed in B-spline optimization guarantee the computational efficiency. The feasibility of the resulting trajectory has been verified through simulation.

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Exploration of Unknown Environment using Deep Reinforcement Learning

Exploration of Unknown Environment using Deep Reinforcement ...

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international Conference on robotics and Artificial intelligence (ICRAI)

作者： Asad Ali Sarah Gul Tallat Mahmood Anayat Ullah Control Automotive and Robotics Lab National Centre of Robotics and Automation (NCRA) Rawalpindi Pakistan Department of Electrical Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

Exploring the unknown environment is a very crucial task where human life is at risks like search and rescue operations, abandoned nuclear plants, covert operations and more. Autonomous robots could serve this task efficiently. The existing methods use uncertainty models for localization and map building to explore the unknown areas requiring high onboard computation and time. We propose to use Deep Reinforcement Learning (DRL) for the autonomous exploration of unknown environments. In DRL, the agent interacts with the environment and learns based on experiences (feedback/reward). We propose extrinsic and curiosity-driven reward functions to explore the environment. The curiosity-based reward function motivates the agent to explore unseen areas by predicting future states, while the extrinsic reward function avoids collisions. We train the differential drive robot in one environment and evaluate its performance in another unknown environment. We observe curiosity-driven reward function outperformed the extrinsic reward by exploring more areas in the unknown environment. The test results show the generalization capability to explore unknown environments with the proposed methods.

关键词： Deep learning Location awareness Industries Uncertainty Service robots computational modeling Reinforcement learning

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An Accumulated Error Based Self-Triggered MPC with Adaptive Prediction Horizon for Cyber Physical Systems

An Accumulated Error Based Self-Triggered MPC with Adaptive ...

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robotics, Intelligent Control and Artificial intelligence (RICAI), international Conference on

作者： Ning He Wenqian Li Hong Zhou Zhongxian Xu School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology Xi'an China Shaanxi Building Materials Technology Group Co. Ltd Xi'an China School of Automation Xi'an University of Post and Telecommunications Xi'an China

An accumulated error based self-triggered model predictive control (AST-MPC) algorithm with an adaptive prediction horizon is proposed for cyber physical systems in this paper. Rather than executing the controller update periodically, the next time of solving the MPC optimization problem is determined according to a new accumulated error based triggering mechanism. Meanwhile, the corresponding prediction horizon is shortened as the state approaches the target regions based on which the communication times and computational complexity of the optimization problem can be reduced simultaneously. Given such a new MPC algorithm, the stability of the considered cyber physical system is analyzed theoretically to guarantee its applicability. Finally, the simulation verification results are provided to illustrate the effectiveness of the algorithm.

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Open Issues in Agri-food Robot Standardization-the Red Meat Sector 15

Open Issues in Agri-food Robot Standardization-the Red Meat ...

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IEEE 15th international symposium on Applied computational intelligence and Informatics (SACI)

作者： Takacs, Kristof Mason, Alex Cordova-Lopez, Luis Eduardo Haidegger, Tamas Obuda Univ Antal Bejczy Ctr Intelligent Robot EKIK Budapest Hungary Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Norwegian Univ Life Sci NMBU As Norway Obuda Univ John von Neumann Fac Informat Antal Bejczy Ctr Intelligent Robot EKIK Budapest Hungary Obuda Univ John von Neumann Fac Informat Budapest Hungary

ISBN: (纸本)9781728195438

Ensuring the safety of equipment, operator and the environment during robotic operation is paramount. Robotic systems are appearing in more and more professional service applications, while mechanic and control system components are evolving fast themselves, the legislation and standards regarding these topics are lagging behind. In connection with the RoBUTCHER project - which is a pioneer research effort employing industrial robots for completely automated slaughtering - it was revealed that there are no particular standards regulating directly robotics applied to the agri-food application domain. More specifically, the meat industry and the red meat sector within has only seen hygienic standards regarding machinery, not considering human-robot collaboration or safe autonomous robot operation in the abattoirs. The purpose of this paper is to provide a general overview of the relevant standards (and similar guiding documents) that could be used as pathfinders during the development of inherently safe robotic systems. Exploring the standard and legislation landscape should offer some instrumental help regarding the foreseen certification process of meat processing robots and robot cells in the near future.

关键词： robot meat processing standardization food robotics

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Fast Path Computation using Lattices in the Sensor-Space for Forest Navigation

Fast Path Computation using Lattices in the Sensor-Space for...

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IEEE international Conference on robotics and automation (ICRA)

作者： Junior, Bernardo Martinez R. Pereira, Guilherme A. S. West Virginia Univ Dept Mech & Aerosp Benjamin M Statler Coll Engn & Mineral Resources Morgantown WV 26506 USA

ISBN: (纸本)9781728190778

Fast autonomous motion in cluttered and unknown environments, such as forests, is highly dependent on low-latency obstacle avoidance strategies. In this context, this paper presents a motion planning strategy that relies on lattices for the fast computation of local paths that both avoid obstacles and follow a vector field that encodes the global robot task. Lattices are constructed in the sensor space and represent a set of search trees that can be quickly pruned in function of the detected obstacles. The remaining lattice trees are used to optimize a vector field-dependent functional, thus generating the best free local path that tracks the field. To illustrate the proposed approach, we present simulation and real-world experiments of a planar robot moving in a cluttered, forest-like environment.

关键词： Navigation Conferences computational modeling Lattices Forestry Robot sensing systems Planning

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MP-STSP: A Multi-Platform Steiner Traveling Salesman Problem Formulation for Precision Agriculture in Orchards

MP-STSP: A Multi-Platform Steiner Traveling Salesman Problem...

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IEEE international Conference on robotics and automation (ICRA)

作者： Carpio, Renzo Fabrizio Maiolini, Jacopo Potena, Ciro Garone, Emanuele Ulivi, Giovanni Gasparri, Andrea Roma Tre Univ I-00146 Rome Italy Univ Libre Bruxelles B-1050 Brussels Belgium

ISBN: (纸本)9781728190778

In this work, we propose a global planning strategy specifically designed for precision agriculture settings, where field activities may have different requirements ranging from a full orchard inspection to sparse targeted per-plant interventions. This global planning strategy is formulated as a novel Multi-Platform Steiner Traveling Salesman Problem (MP-STSP) where, in order to guarantee the exploitation of multiple moving platforms and the minimization of the overall operational time, the proposed formulation explicitly takes into account the time required to perform each task. By doing so, the computed itineraries attempt to balance the workload among the deployed platforms. Comparative simulations, inspired by the needs of the EU H2020 Project PANTHEON(1), are provided to numerically demonstrate the effectiveness of the proposed planning strategy for an orchard precision agriculture setting.

关键词： Conferences computational modeling Traveling salesman problems Inspection Minimization Agriculture Distance measurement

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Assembly Sequences Based on Multiple Criteria Against Products with Deformable Parts

Assembly Sequences Based on Multiple Criteria Against Produc...

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IEEE international Conference on robotics and automation (ICRA)

作者： Kiyokawa, Takuya Takamatsu, Jun Ogasawara, Tsukasa Nara Inst Sci & Technol NAIST Div Informat Sci Robot Lab Nara Japan

ISBN: (纸本)9781728190778

To generate assembly sequences that robots can easily handle, this study tackled assembly sequence generation (ASG) by considering two tradeoff objectives: (1) insertion conditions and (2) degrees of the constraints affecting the assembled parts. We propose a multiobjective genetic algorithm to balance these two objectives. Furthermore, we extend our previously proposed 3D computer-aided design (CAD)-based method for extracting three types of two-part relationship matrices from 3D models that include deformable parts. The interference between deformable and other parts can be determined using scaled part shapes. Our proposed ASG can produce Pareto-optimal sequences for multi-component models with deformable parts such as rubber bands, rubber belts, and roller chains. We further discuss the limitation and applicability of the generated sequences to robotic assembly.

关键词： Deformable models Robotic assembly Robot motion Solid modeling Three-dimensional displays Shape computational modeling

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Fault Diagnosis of Quadruped Robot Joint Modules Based on TDIC and Random Forest

Fault Diagnosis of Quadruped Robot Joint Modules Based on TD...

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Sensing, Measurement & Data Analytics in the era of Artificial intelligence (ICSMD), international Conference on

作者： Yufan Chen Beibei Fan Xin Xiong Mapping Shi School of Mechatronic Engineering and Automation Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

As robotics technology advances, quadruped robots are increasingly instrumental in diverse applications, including rescue operations and healthcare. However, the intricate operating conditions expose the components within the joint modules of these robots to alternating and impact loads. Consequently, research into signal processing and fault diagnosis techniques for planetary gearboxes in joint modules holds significant engineering importance. Given the challenges associated with fault signal extraction from planetary gearboxes, this study proposes a novel fault feature extraction method based on Time-Dependent Intrinsic Correlation (TDIC), integrated with a random forest classifier for fault classification. This approach leverages signal frequency analysis to adaptively extract segments characterized by high correlation among different physical field signals as fault features. The random forest model is utilized for fault classification and its performance is compared against CNN, KNN, and XGBoost models. Experimental results validate the superior fault classification efficacy of the proposed method.

关键词： Fault diagnosis Adaptation models Correlation Accuracy computational modeling Feature extraction Real-time systems Quadrupedal robots Random forests Load modeling

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Model-Dependent Prosthesis Control with Interaction Force Estimation

Model-Dependent Prosthesis Control with Interaction Force Es...

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IEEE international Conference on robotics and automation (ICRA)

作者： Gehlhar, Rachel Ames, Aaron D. CALTECH Dept Mech & Civil Engn Pasadena CA 91125 USA

ISBN: (纸本)9781728190778

Current lower-limb prosthesis control methods are primarily model-independent - lacking formal guarantees of stability, relying largely on heuristic tuning parameters for good performance, and neglecting use of the natural dynamics of the system. Model-dependence for prosthesis controllers is difficult to achieve due to the unknown human dynamics. We build upon previous work which synthesized provably stable prosthesis walking through the use of rapidly exponentially stabilizing control Lyapunov functions (RES-CLFs). This paper utilizes RES-CLFs together with force estimation to construct model-based optimization-based controllers for the prosthesis. These are experimentally realized on hardware with onboard sensing and computation. This hardware demonstration has formal guarantees of stability, utilizes the natural dynamics of the system, and achieves superior tracking to other prosthesis trajectory tracking control methods.

关键词： Trajectory tracking computational modeling Dynamics Force Estimation Stability analysis Hardware

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DDPG with Meta-Learning-Based Experience Replay Separation for Robot Trajectory Planning 7

DDPG with Meta-Learning-Based Experience Replay Separation f...

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7th international Conference on Control, automation and robotics (ICCAR)

作者： Yang, Jin Peng, Gang Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9781665449861

Prioritized experience replay (PER) chooses the experience data based on the value of Temporal-Difference (TD) error, it can improve the utilization of experience in deep reinforcement learning based methods. But since the value of TD error needs to be calculated when experience data sampled at each time, so the computational complexity of PER is high. And the hyperparameters in PER also affect the learning process. The hyperparameters need to be adjusted carefully. To cope above problems, we propose meta-learning-based experience replay buffer separation (MSER) in this paper. Firstly, the original experience replay buffer is divided into a successful experience buffer and a failure experience buffer, and the experience data of successful exploration or failure exploration are stored respectively. Secondly, the experience data is sampled randomly from two experience replay buffers according to a ratio, and the ratio is learned through the neural network designed by us. Finally, we conduct experiments in a simulation environment for trajectory planning of robot manipulator in V-REP. Extensive experiments show that the MSER proposed is capable of improving convergence rate by up to 9.1% in DDPG, compared to DDPG with PER. The convergence mean reward rises by up to 3.3% and the standard deviation reduce by 16.9%. In the evaluation, the success rate of trajectory planning for robot manipulator is up to 99%.

关键词： experience replay robot manipulator meta learning deep reinforcement learning

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