As a rigid body, the nonholonomic mobile robot contains both states of position and orientation. In order to plan these states simultaneously, this paper investigates the full-state planning problem of nonholonomic mo...
As a rigid body, the nonholonomic mobile robot contains both states of position and orientation. In order to plan these states simultaneously, this paper investigates the full-state planning problem of nonholonomic mobile robots, in the sense that the robots should reach the specified positions and meanwhile point to the desired orientations at the terminal time. To this end, we propose a velocity vector field which guides the mobile robots to the goal points. Particularly, the dynamics of the robot orientation is brought into the vector field, so that the attitude angle of the robot can converge to the specified value following the orientation dynamics. Furthermore, we study the obstacle avoidance and mutual-robot-collision avoidance by proposing another velocity vector field, which guides the robots moving along the tangential direction of the dangerous areas. Finally, several numerical simulation examples are provided to support the theoretical results.
Multi-agent systems outperform single agent in complex collaborative tasks. However, in large-scale scenarios, ensuring timely information exchange during decentralized task execution remains a challenge. This work pr...
Multi-agent systems outperform single agent in complex collaborative tasks. However, in large-scale scenarios, ensuring timely information exchange during decentralized task execution remains a challenge. This work presents an online decentralized coordination scheme for multi-agent systems under complex local tasks and intermittent communication constraints. Unlike existing strategies that enforce all-time or intermittent connectivity, our approach allows agents to join or leave communication networks at aperiodic intervals, as deemed optimal by their online task execution. This scheme concurrently determines local plans and refines the communication strategy, i.e., where and when to communicate as a team. A decentralized potential game is modeled among agents, for which a Nash equilibrium is generated iteratively through online local search. It guarantees local task completion and intermittent communication constraints. Extensive numerical simulations are conducted against several strong baselines.
Multi-agent systems outperform single agent in complex collaborative tasks. However, in large-scale scenarios, ensuring timely information exchange during decentralized task execution remains a challenge. This work pr...
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Researchers have always been popular with analyzing and controlling nonlinear systems, yet most methods are based on local linearized models. As an alternative, Koopman theory was introduced to solve global linearizat...
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Estimating the shape and motion state of the myocardium is essential in diagnosing cardiovascular diseases. However, cine magnetic resonance (CMR) imaging is dominated by 2D slices, whose large slice spacing challenge...
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Gaussian process regression has received considerable attention due to its performance in solving the problem of learning and predicting the dynamics of certain systems in the machine learning area. However, this data...
Gaussian process regression has received considerable attention due to its performance in solving the problem of learning and predicting the dynamics of certain systems in the machine learning area. However, this data-driven method ignores the prior physical information. A feasible method to tackle this problem is to embed prior dynamics into the Gaussian process regression. This naturally relies on numerical discretizations of continuous-time differential equations that describe the dynamics. However, conventional discretization schemes do not respect the intrinsic geometric structure of the system, which plays an important role when analyzing the properties of the mechanical system. In this work, we develop a physic-informed Gaussian process regression algorithm based on Hamel's formalism and its variational integrator. Computational properties are illustrated by the numerical experiment of learning and predicting the dynamics of a planar pendulum.
This paper studies the secure and accurate clock synchronization problem for sensor networks with time-varying delays and malicious attacks.A novel clock synchronization scheme based on the attack detection mechanism,...
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This paper studies the secure and accurate clock synchronization problem for sensor networks with time-varying delays and malicious attacks.A novel clock synchronization scheme based on the attack detection mechanism,attack compensation,and maximum consensus protocol is *** proposed scheme starts with the detection of the malicious attacks and the clock data under attacks is *** the basis,software clock parameters are updated so that all the nodes in the network can have the same software skew and offset,so the clock synchronization can be ***,it is theoretically proved that the proposed scheme can achieve the attack detection correctly,and further can guarantee a secure and accurate clock *** addition,extensive simulations are also conducted to validate the effectiveness of the proposed scheme.
Fuzzy logic is widely applied in various applications. However, verifying the correctness of fuzzy logic models can be difficult. This extended abstract presents our ongoing work on verifying fuzzy logic models. We tr...
Fuzzy logic is widely applied in various applications. However, verifying the correctness of fuzzy logic models can be difficult. This extended abstract presents our ongoing work on verifying fuzzy logic models. We treat a fuzzy logic model as a program and propose a verification method based on symbolic execution for fuzzy logic models. We have developed and implemented the environment models for the common functions and the inference rules in fuzzy logic models. Our preliminary evaluation shows the potential of our verification method.
Underwater robotic operation usually requires visual perception(e.g.,object detection and tracking),but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual...
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Underwater robotic operation usually requires visual perception(e.g.,object detection and tracking),but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual *** addition,detection continuity and stability are important for robotic perception,but the commonly used static accuracy based evaluation(i.e.,average precision)is insufficient to reflect detector performance across *** response to these two problems,we present a design for a novel robotic visual perception ***,we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection *** a result,although domain quality has an ignorable effect on within-domain detection accuracy,visual restoration is beneficial to detection in real sea scenarios by reducing the domain ***,non-reference assessments are proposed for detection continuity and stability based on object ***,online tracklet refinement is developed to improve the temporal performance of ***,combined with visual restoration,an accurate and stable underwater robotic visual perception framework is ***-overlap suppression is proposed to extend video object detection(VID)methods to a single-object tracking task,leading to the flexibility to switch between detection and *** experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed *** codes are available at https://***/yrqs/VisPerception.
This brief presents a novel method based on canonical correlation analysis (CCA) and particle filter (PF) for battery state of charge (SOC) estimation. More specifically, CCA is adopted to provide a universal way for ...
This brief presents a novel method based on canonical correlation analysis (CCA) and particle filter (PF) for battery state of charge (SOC) estimation. More specifically, CCA is adopted to provide a universal way for battery SOC estimation with PF for error correction. Technically, the input data are first mapped to polynomial terms before training to find the intrinsic features. Afterwards, ℓ 2 -norm regularization is further added to the loss function in order to prevent overfitting. Finally, the SOC estimation result from CCA training is combined with the Coulomb counting model and updated by PF for error correction. Experimental results using battery testing data at different testing profiles and temperatures show the high accuracy of estimation and robustness to wrong initial guess. In particular, compared with linear regression, support vector regression, and neural network, the error of the proposed method is reduced to 1.47%.
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