Model-based analysis is a common technique to identify incorrect behavioral composition of complex, safety-critical systems, such as robotics systems. However, creating structural and behavioral models for hundreds of...
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ISBN:
(纸本)9798400705021
Model-based analysis is a common technique to identify incorrect behavioral composition of complex, safety-critical systems, such as robotics systems. However, creating structural and behavioral models for hundreds of software components manually is often a labor-intensive and error-prone process. I propose an approach to infer behavioral models for components of systems based on the Robot Operating System (ROS), the most popular framework for robotics systems, using a combination of static and dynamic analysis by exploiting assumptions about the usage of the ROS framework. This work is a contribution towards making well-proven and powerful but infrequently used methods of model-based analysis more accessible and economical in practice to make robotics systems more reliable and safer.
Pose estimation is a critical task in computer vision with a wide range of applications from activity monitoring to human-robot interaction. However, most of the existing methods are computationally expensive or have ...
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"Data is the new oil" has become a popular catch-phrase in the world of technology, emphasizing the immense value of data in today's digital age. Most services and platforms rely on data, but collecting ...
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ISBN:
(纸本)9798350322514
"Data is the new oil" has become a popular catch-phrase in the world of technology, emphasizing the immense value of data in today's digital age. Most services and platforms rely on data, but collecting this data can be challenging and costly. To address this issue, we leverage a novel distributed crowdsourcing framework - termed Swarm Contracts - that utilizes blockchain and is applied to robotics technologies. The framework encourages an incentivized crowdsourcing model through open-source robots and a secure, decentralized, and transparent blockchain-based incentive system. As a demonstration of the framework's capabilities, we use it to collect Google Street View((R)) map data, which can be a resource-intensive task to keep up to date using traditional centralized methods. Our Swarm Contract framework uses Google Street View((R)) Publish API, which allows for the contribution of street view data to Google Maps((R)) to implement the incentive-based crowdsourcing of street view images. By incorporating a swarm contract-powered framework with the Google Street View((R)) Publish API, we show that the incentivized crowdsourcing of street view data can be a practical solution to maintain accurate and up-to-date Google Street View((R)) maps.
3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a ...
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ISBN:
(纸本)9798350323658
3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require an explicit view transformation between BEV and front view, which itself is still a challenging problem. In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step. Specifically, we formulate 3D lane detection as a curve propagation problem by using curve queries. A 3D lane query is represented by a dynamic and ordered anchor point set. In this way, queries with curve representation in Transformer decoder iteratively refine the 3D lane detection results. Moreover, a curve cross-attention module is introduced to compute the similarities between curve queries and image features. Additionally, a context sampling module that can capture more relative image features of a curve query is provided to further boost the 3D lane detection performance. We evaluate our method for 3D lane detection on both synthetic and real-world datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art approaches. The effectiveness of each component is validated via ablation studies as well.
With their high-fidelity scene representation capability, the attention of SLAM field is deeply attracted by the Neural Radiation Field (NeRF) and 3D Gaussian Splatting (3DGS). Recently, there has been a surge in NeRF...
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Data-driven simulators promise high dataefficiency for driving policy learning. When used for modelling interactions, this data-efficiency becomes a bottleneck: small underlying datasets often lack interesting and cha...
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ISBN:
(纸本)9781728196817
Data-driven simulators promise high dataefficiency for driving policy learning. When used for modelling interactions, this data-efficiency becomes a bottleneck: small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We address this challenge by proposing a data-driven simulation engineA that uses inpainted ado vehicles for learning robust driving policies. Thus, our approach can be used to learn policies that involve multiagent interactions and allows for training via state-of-theart policy learning methods. We evaluate the approach for learning standard interaction scenarios in driving. In extensive experiments, our work demonstrates that the resulting policies can be directly transferred to a full-scale autonomous vehicle without making use of any traditional sim-to-real transfer techniques such as domain randomization.
Soft robotics promises new opportunities for solving problems that were limited by rigid robots due to their compliant physical structure. Pneumatic Soft Actuators (PSA) are a class of soft robots that have gained pop...
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Human motion prediction is key to understand social environments, with direct applications in robotics, surveillance, etc. We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians&...
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ISBN:
(纸本)9781728196817
Human motion prediction is key to understand social environments, with direct applications in robotics, surveillance, etc. We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians' positions prediction in urban-like environments conditioned by the environment: map and surround agents. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion, allowing to capture the important features in the environment that improve prediction. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models, which makes our approach easily extendable and configurable, depending on the data available. We report results performing similarly with SoTA models on publicly available and extensible-used datasets with uni-modal prediction metrics ADE and FDE.
Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by app...
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ISBN:
(纸本)9781728196817
Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by applying learning from demonstration (LfD) to trajectories observed by cars already on the road. However, existing LfD methods are typically insufficient, yielding policies that frequently collide or drive off the road. To address this problem, we propose Symphony, which greatly improves realism by combining conventional policies with a parallel beam search. The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator. However, it can also harm diversity, i.e., how well the agents cover the entire distribution of realistic behaviour, as pruning can encourage mode collapse. Symphony addresses this issue with a hierarchical approach, factoring agent behaviour into goal generation and goal conditioning. The use of such goals ensures that agent diversity neither disappears during adversarial training nor is pruned away by the beam search. Experiments on both proprietary and open Waymo datasets confirm that Symphony agents learn more realistic and diverse behaviour than several baselines.
This paper mainly focuses on various two-point methods of identifying the First Order Plus Time Delay (FOPTD) model, and their impact on control quality. The following criteria were used to match the identification me...
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ISBN:
(数字)9781665468589
ISBN:
(纸本)9781665468596;9781665468589
This paper mainly focuses on various two-point methods of identifying the First Order Plus Time Delay (FOPTD) model, and their impact on control quality. The following criteria were used to match the identification method: ISE between the response of the real system and the model and the normalized delay time. The work used two different tuning techniques: QDR and pidtune. A three-tank interacting and non-interacting system was used as a model for the tests. All tests were simulated in Matlab environment, ISE and settling time were used to assess the quality of control.
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