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Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving

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arXiv 2023年

作者： Chowdhury, Jayabrata Veerendranath, Vishruth Sundaram, Suresh Sundararajan, Narasimhan Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Science Bangalore India Artificial Intelligence and Robotics Lab Dept. of Aerospace Engineering Indian Institute of Science Bangalore India PES university Bangalore India

This paper presents a Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) model for maneuver planning. Traditional rule-based maneuver planning approaches often have to improve their abilities to handle the variabilities of real-world driving scenarios. By learning from its experience, a Reinforcement Learning (RL)-based driving agent can adapt to changing driving conditions and improve its performance over time. Our proposed approach combines a predictive model and an RL agent to plan for comfortable and safe maneuvers. The predictive model is trained using historical driving data to predict the future positions of other surrounding vehicles. The surrounding vehicles’ past and predicted future positions are embedded in context-aware grid maps. At the same time, the RL agent learns to make maneuvers based on this spatio-temporal context information. Performance evaluation of PMP-DRL has been carried out using simulated environments generated from publicly available NGSIM US101 and I80 datasets. The training sequence shows the continuous improvement in the driving experiences. It shows that proposed PMP-DRL can learn the trade-off between safety and comfortability. The decisions generated by the recent imitation learning-based model are compared with the proposed PMP-DRL for unseen scenarios. The results clearly show that PMP-DRL can handle complex real-world scenarios and make better comfortable and safe maneuver decisions than rule-based and imitative models. © 2023, CC BY.

关键词： Autonomous vehicles

Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks

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arXiv 2024年

作者： Shokry, Ahmed Gomaa, Walid Zaenker, Tobias Dawood, Murad Menon, Rohit Maged, Shady A. Awad, Mohammed I. Bennewitz, Maren Humanoid Robots Lab Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany Cyber Physical Systems Lab Egypt Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt Mechatronics Department Ain Shams University Cairo Egypt

Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown environments is still challenging due to uncertainty in task parameters, such as the hole position and orientation, resulting from sensor noise. Although context-based meta reinforcement learning (RL) methods have been previously presented to adapt to unknown task parameters in PiH assembly tasks, the performance depends on a sample-inefficient procedure or human demonstrations. Thus, to enhance the applicability of meta RL in real-world PiH assembly tasks, we propose to train the agent to use information from the robot’s forward kinematics and an uncalibrated camera. Furthermore, we improve the performance by efficiently adapting the meta-trained agent to use data from force/torque sensor. Finally, we propose an adaptation procedure for out-of-distribution tasks whose parameters are different from the training tasks. Experiments on simulated and real robots prove that our modifications enhance the sample efficiency during meta training, real-world adaptation performance, and generalization of the context-based meta RL agent in PiH assembly tasks compared to previous approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Feasibility of Utilizing Passive BCI for Assistance Evaluation: A Case Study on a Knee Exoskeleton

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Feasibility of Utilizing Passive BCI for Assistance Evaluati...

IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Asghar Mahmoudi Morteza Khosrotabar Jannis Kuhmann Martin Grimmer Stephan Rinderknecht Maziar A. Sharbafi Faculty of Mechanical Engineering Institute for Mechatronic Systems Technical University Darmstadt Germany Lauflabor Locomotion Lab. Institute of Sports Science Technical Uni-versity Darmstadt Darmstadt Germany Department of Electrical En-gineering and Information Technology Control and Cyber-Physical Systems Technical University Darmstadt Darmstadt Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

This study explored the feasibility of incorpo-rating a passive Brain-Computer Interface (BCI) into the individualization process of wearable assistive devices, with a specific focus on lower-limb exoskeletons. These devices can greatly improve mobility and quality of life for those with lower limb impairments. However, their optimal performance relies on personalized adjustments. This study offered a unique approach to user feedback without intentional control. A one-degree-of-freedom knee exoskeleton was utilized, introducing different levels of impedance to create distinct assistance levels. The analysis identified specific brain clusters, notably the right premotor and supplementary motor cortex, exhibiting significant differences in activity between the easiest and the most challenging conditions. This critical proof-of-concept step demonstrated that through decoding the user's brain activity, passive BCI could support the individualization of assistive technology. This approach indicated the potential to enhance the performance and user experience of wearable assistive devices.

关键词： Performance evaluation Brain Exoskeletons Motors User experience Brain-computer interfaces Decoding Assistive devices Impedance Robots

An efficient Deep Spatio-Temporal Context Aware decision Network (DST-CAN) for Predictive Manoeuvre Planning

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arXiv 2022年

作者： Chowdhury, Jayabrata Sundaram, Suresh Rao, Nishanth Sundararajan, Narasimhan Robert Bosch Centre for Cyber-Physical Systems Artificial Intelligence and Robotics Lab Dept. of Aerospace Engineering Indian Institute of Science Bangalore India The Artificial Intelligence and Robotics Lab Dept. of Aerospace Engineering Indian Institute of Science Bangalore India WIRIN Project Dept. of Aerospace Engineering Indian Institute of Science Bangalore India

The safety and efficiency of an Autonomous Vehicle (AV) manoeuvre planning heavily depend on the future trajectories of surrounding vehicles. If an AV can predict its surrounding vehicles’ future trajectories, it can make safe and efficient manoeuvre decisions. In this paper, we present a Deep Spatio-Temporal Context-Aware decision Network (DST-CAN) for predictive manoeuvre decisions for AVs on highways. DST-CAN has two main components, namely spatio-temporal context-aware map generator and predictive manoeuvre decisions engine. DST-CAN employ a memory neuron network to predict the future trajectories of its surrounding vehicles. Using look-ahead prediction and past actual trajectories, a spatio-temporal context-aware probability occupancy map is generated. These context-aware maps as input to a decision engine generate a safe and efficient manoeuvre decision. Here, CNN helps extract feature space, and two fully connected network generates longitudinal and lateral manoeuvre decisions. Performance evaluation of DST-CAN has been carried out using two publicly available NGSIM US-101 and I-80 highway datasets. A traffic rule is defined to generate ground truths for these datasets in addition to human decisions. Two DST-CAN models are trained using imitation learning with human driving decisions from actual traffic data and rule-based ground truth decisions. The performances of the DST-CAN models are compared with the state-of-the-art Convolutional Social-LSTM (CS-LSTM) models for manoeuvre prediction. The results clearly indicate that the context-aware maps help DST-CAN to predict the decision accurately over CS-LSTM. Further, an ablation study has been carried out to understand the effect of prediction horizons of performance and a robustness study to understand the near collision scenarios over actual traffic observations. The context-aware map with a 3 second prediction horizon is most suitable and robust against near collision. © 2022, CC BY.

关键词： Autonomous vehicles

A Review on Collaborative Robots in Industrial and Service Sectors

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A Review on Collaborative Robots in Industrial and Service S...

Siberian Conference on control and Communications (SIBCON)

作者： Ramir Sultanov Shifa Sulaiman Hongbing Li Roman Meshcheryakov Evgeni Magid Intelligent Robotics Department Kazan Federal University Kazan Russia Department of Instrument Science and Engineering Shanghai Jiao Tong University Shanghai China Cyber-physical Lab V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia HSE University Moscow Russia

ISBN: (纸本)9781665476294

Today, a collaborative robotics is a rapidly developing area due to its applications in a broad variety of sectors. Collaborative robots (cobots) are robots, which are designed specifically to work along with humans in a common workspace. This article discusses modern cobots employed in industrial and service sectors. Research activities associated with cobots used in virtual environments and real world experiments are described. The ISO/TS 15066:2016 technical specifications followed by cobots for ensuring safety to human operators are emphasized.

关键词： Learning systems Service robots Collaboration Virtual environments Process control Human-robot interaction Production

Modelling mobile robot navigation in 3D environments: camera-based stairs recognition in Gazebo

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Modelling mobile robot navigation in 3D environments: camera...

Moscow Workshop on Electronic and Networking Technologies (MWENT)

作者： Maksim Mustafin Tatyana Tsoy Edgar A. Martí nez-Garcí a Roman Meshcheryakov Evgeni Magid Intelligent Robotics Department Kazan Federal University Kazan Russia Industrial Engineering and Manufacturing Dept. Autonomous University of Ciudad Juarez Chihuahua Mexico Cyber-physical Lab V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia HSE University Moscow Russia

The task of sensory-based autonomous navigation of mobile robots requires data fusion from multiple sources in order to properly detect and recognize environmental obstacles. One of important issues mobile robots deal in a typical multi-level indoor environment is a stair well detection and negotiation. This paper presents ROS-based stairs detection implementation using onboard cameras of the Russian mobile crawler robot Servosila Engineer. Virtual experiments were performed in Gazebo environment with a single camera and a stereo camera.

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Distributed formation trajectory planning for multi-vehicle systems

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Distributed formation trajectory planning for multi-vehicle ...

American control Conference (ACC)

作者： Binh Nguyen Truong Nghiem Linh Nguyen Tung Nguyen Hung La Mehdi Sookhak Thang Nguyen Department of Engineering Texas A&M University–Corpus Christi Corpus Christi TX USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA School of Engineering Information Technology and Physical Sciences Federation University Australia Churchill VIC Australia Department of Information Technology Uppsala University Uppsala Sweden Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA Department of Computing Sciences Texas A&M University–Corpus Christi Corpus Christi TX USA

This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system.

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A survey on security and privacy issues of UAVs

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arXiv 2021年

作者： Mekdad, Yassine Aris, Ahmet Babun, Leonardo Fergougui, Abdeslam E.L. Conti, Mauro Lazzeretti, Riccardo Uluagac, A. Selcuk Laboratory of Computer Networks and Systems Moulay Ismail University of Meknes Zitoune Meknes11201 Morocco Cyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Department of Mathematics University of Padua Padua35121 Italy Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome00185 Italy

In the 21st century, the industry of drones, also known as Unmanned Aerial Vehicles (UAVs), has witnessed a rapid increase with its large number of airspace users. The tremendous benefits of this technology in civilian applications such as hostage rescue and parcel delivery will integrate smart cities in the future. Nowadays, the affordability of commercial drones expands its usage at a large scale. However, the development of drone technology is associated with vulnerabilities and threats due to the lack of efficient security implementations. Moreover, the complexity of UAVs in software and hardware triggers potential security and privacy issues. Thus, posing significant challenges for the industry, academia, and governments. In this paper, we extensively survey the security and privacy issues of UAVs by providing a systematic classification at four levels: Hardware-level, Software-level, Communication-level, and Sensor-level. In particular, for each level, we thoroughly investigate (1) common vulnerabilities affecting UAVs for potential attacks from malicious actors, (2) existing threats that are jeopardizing the civilian application of UAVs, (3) active and passive attacks performed by the adversaries to compromise the security and privacy of UAVs, (4) possible countermeasures and mitigation techniques to protect UAVs from such malicious activities. In addition, we summarize the takeaways that highlight lessons learned about UAVs’ security and privacy issues. Finally, we conclude our survey by presenting the critical pitfalls and suggesting promising future research directions for security and privacy of UAVs. © 2021, CC BY.

关键词： Drones

Plug-and-Play Supervisory control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection 14th

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Plug-and-Play Supervisory Control Using Muscle and Brain Sig...

14th robotics: Science and systems, RSS 2018

作者： Del Preto, Joseph Salazar-Gomez, Andres F. Gil, Stephanie Hasani, Ramin M. Guenther, Frank H. Rus, Daniela MIT Distributed Robotics Lab CambridgeMA02139 United States Vienna University of Technology Cyber-Physical Systems Group Austria Boston University Guenther Lab BostonMA02215 United States

ISBN: (纸本)9780992374747

control of robots in safety-critical tasks and situations where costly errors may occur is paramount for realizing the vision of pervasive human-robot collaborations. For these cases, the ability to use human cognition in the loop can be key for recuperating safe robot operation. This paper combines two streams of human biosignals, electrical muscle and brain activity via EMG and EEG, respectively, to achieve fast and accurate human intervention in a supervisory control task. In particular, this paper presents an end-to-end system for continuous rolling-window classification of gestures that allows the human to actively correct the robot on demand, discrete classification of Error-Related Potential signals (unconsciously produced by the human supervisor’s brain when observing a robot error), and a framework that integrates these two classification streams for fast and effective human intervention. The system also allows "plug-and-play" operation, demonstrating accurate performance even with new users whose biosignals have not been used for training the classifiers. The resulting hybrid control system for safety-critical situations is evaluated with 7 untrained human subjects in a supervisory control scenario where an autonomous robot performs a multi-target selection task. © 2018, MIT Press Journals. All rights reserved.

关键词： Safety engineering