controller Area Network (CAN) protocol is an efficient standard enabling communication among Electronic control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense featur...
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Multi-Agent Path Finding is a problem of finding the optimal set of paths for multiple agents from the starting position to the goal without conflict, which is essential to large-scale robotic systems. Imitation and r...
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ISBN:
(纸本)9781665481106
Multi-Agent Path Finding is a problem of finding the optimal set of paths for multiple agents from the starting position to the goal without conflict, which is essential to large-scale robotic systems. Imitation and reinforcement learning are applied to solve the MAPF problem and have achieved certain results, which provides a feasible solution for the path planning problem of large-scale robot systems. The current method improves the performance of distributed strategy-guided agent planning paths in complex environments by introducing the communication between graph neural networks and agents but dramatically reduces the system's robustness. This paper develops a novel imitation reinforcement learning framework by introducing Transformer, which enables algorithms to perform well in complex environments without relying on communication between agents. Compared with its counterparts, experiments show that the policy trained by our method guides the agent to drive from the initial position to the goal without collision and achieve better performance.
The exploration of Bird’s-Eye View (BEV) mapping technology has driven significant innovation in visualperceptiontechnology for autonomous driving. BEV mapping models need to be applied to the unlabeled real world,...
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The exploration of Bird’s-Eye View (BEV) mapping technology has driven significant innovation in visualperceptiontechnology for autonomous driving. BEV mapping models need to be applied to the unlabeled real world, making the study of unsupervised domain adaptation models an essential path. However, research on unsupervised domain adaptation for BEV mapping remains limited and cannot perfectly accommodate all BEV mapping tasks. To address this gap, this paper proposes HierDAMap, a universal and holistic BEV domain adaptation framework with hierarchical perspective priors. Unlike existing research that solely focuses on image-level learning using prior knowledge, this paper explores the guiding role of perspective prior knowledge across three distinct levels: global, sparse, and instance levels. With these priors, HierDA consists of three essential components, including Semantic-Guided Pseudo Supervision (SGPS), Dynamic-Aware Coherence Learning (DACL), and Cross-Domain Frustum Mixing (CDFM). SGPS constrains the cross-domain consistency of perspective feature distribution through pseudo labels generated by vision foundation models in 2D space. To mitigate feature distribution discrepancies caused by spatial variations, DACL employs uncertainty-aware predicted depth as an intermediary to derive dynamic BEV labels from perspective pseudo-labels, thereby constraining the coarse BEV features derived from corresponding perspective features. CDFM, on the other hand, leverages perspective masks of view frustum to mix multi-view perspective images from both domains, which guides cross-domain view transformation and encoding learning through mixed BEV labels. Furthermore, this paper introduces intra-domain feature exchange data augmentation to enhance the efficiency of domain adaptation learning. The proposed method is verified on multiple BEV mapping tasks, such as BEV semantic segmentation, high-definition semantic, and vectorized mapping. It demonstrates competitive perfo
Interactive Image Segmentation (IIS) has emerged as a promising technique for decreasing annotation time. Substantial progress has been made in pre- and post-processing for IIS, but the critical issue of interaction a...
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Navigating robots through dynamic multi-robot environments, avoiding collisions with both other robots and obstacles, has emerged as a central challenge in robotics. The existing approaches fall short in allowing the ...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Navigating robots through dynamic multi-robot environments, avoiding collisions with both other robots and obstacles, has emerged as a central challenge in robotics. The existing approaches fall short in allowing the policy network to effectively capture spatial-temporal reciprocal collision avoidance in multi-robot environments, comprising both static and dynamic obstacles, resulting in inadequate safety and efficiency in directing robot movement. In this study, we introduce a novel policy neural network called Spatial-Temporal RetNet (STR), designed to encode reciprocal collision avoidance states between robots in spatial and temporal dimensions. The goal is to improve the safety and efficacy of the policy neural network in directing robots to complete assigned tasks. The spatial state encoder module is built upon a parallel RetNet structure, which strengthens the neural network's capacity in extracting reciprocal collision avoidance states between robots in spatial dimensions. This module addresses the limitations of position encoding in transformer-based multi-robot navigation policy neural networks. We design a temporal state encoder utilizing a recurrent RetNet structure. This innovation bolsters the multi-robot navigation policy neural network's capability to capture features in the temporal dimension of multi-robot movements. It addresses the limitations of transformer-based multi-robot navigation policy neural networks, particularly in recurrently inferring information across time dimensions. Simulation experiments were conducted to showcase the superior safety and effectiveness of our proposed method compared to previous state-of-the-art approaches in guiding robots to accomplish tasks.
Data centers are the key infrastructure for information services. The monitoring of the thermal environment of the data center computer room is an important work for its safe operation. This paper proposed a mobile ro...
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ISBN:
(纸本)9781665426480
Data centers are the key infrastructure for information services. The monitoring of the thermal environment of the data center computer room is an important work for its safe operation. This paper proposed a mobile robotic system for data center thermal environment measurement, which can freely sample the temperature and humidity data around the facilities within the computer room. On one hand, measuring the temperature and humidity in a mobile way can obtain far more environmental data than fixed-point sensors. On the other hand, the mobile robot can be used instead of manual inspection. In addition, a temperature field reconstruction method is provided based on the sampled temperature data. Theoretically, the temperature of locations that have not been measured can be evaluated by interpolating sample temperature around them. The reconstruction of the temperature field can present the variation of the temperature more clearly and help find the hot spots or locations with low cooling efficiency during the operation. Lastly, experiments are carried out to study the measurement error of the mobile robotic system and an error correction method is proposed. After that, the relationship between the temperature reconstruction error and the layout of sampling points is investigated.
People with visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centere...
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Transformer-based methods have demonstrated superior performance for monocular 3D object detection recently, which aims at predicting 3D attributes from a single 2D image. Most existing transformer-based methods lever...
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This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) an...
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ISBN:
(数字)9798350379228
ISBN:
(纸本)9798350390780
This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) and self-tuning mechanism. Different from existing methods, the proposed strategy is designed to explore optimal control performance by taking advantage of model-based self-tuning mechanism and deep reinforcement learning. Specifically, the self-tuning guided deep RL scheme is put forward for quadrotors, with superior learning efficiency and strong adaptability. Firstly, a novel self-tuning mechanism is constructed and some auxiliary variables are introduced to enhance the tracking performance. Then, based on the model-driven self-tuning design, the deep RL is proposed to achieve model-guided learning, where the tuning actions are adopted in the evaluation process during training, aiming at removing the bad explorations by the carefully designed parallel evaluation. Finally, the convergence is analyzed based on the proposed learning framework, which indicates the efficient cooperation of exploration and self-tuning mechanism. To verify the effectiveness of the proposed controller, the guided training and hardware experiments are implemented to show efficient cooperation and satisfactory high-speed trajectory tracking control of the proposed method.
For quadrotors, imposing multiple dynamic constraints on the state simultaneously to achieve safe control is a challenging problem. In this paper, a cascaded control archi-tecture based on quadratic programming method...
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ISBN:
(纸本)9781665481106
For quadrotors, imposing multiple dynamic constraints on the state simultaneously to achieve safe control is a challenging problem. In this paper, a cascaded control archi-tecture based on quadratic programming method is proposed to generate minimally-invasive and collision-free control actions. This architecture consists of exponential control barrier functions(ECBFs) to construct a non-conservative forward invariant safety region and geometric nonlinear PID attitude control with considering quadrotor dynamics to avoid the singularities of Euler-angles and the ambiguity of quaternions. The feasibility and the effectiveness of the proposed cascaded control architecture is demonstrated through numerical simulations.
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