More than one homogenous or heterogenous type unmanned vehicle can work in a coordinated manner and perform large-scale swarm tasks (firefighting, search and rescue, mapping, and military operations, etc.) efficiently...
More than one homogenous or heterogenous type unmanned vehicle can work in a coordinated manner and perform large-scale swarm tasks (firefighting, search and rescue, mapping, and military operations, etc.) efficiently in a shorter time by sharing tasks. Collision of these vehicles is among the most significant problems encountered during their work. The crash of the vehicles causes the vehicles to be out of duty and, accordingly, to the mission’s failure. In this study, Quadrotor-type UAVs used as agents can go to any target point by receiving location, speed, and compass information with GPS and IMU sensors. In the application, the agents’ locations were kept and updated in a list in pairs, similar to the traversing process in the Optimized Bubble Sort Algorithm. The projections of the velocity vectors on the agents’ axis (local) on a single coordinate plane are taken to determine the collision situation between these two agents that are traveling instantaneously. These global velocity vectors, whose projections are taken, are re-projected to the edge formed by these two agents in the graph and then subtracted from each other. Suppose the size of the vector is greater than the distance between two agents obtained by any GPS distance algorithm (Pythagoras, Haversine, etc.). In this case, collision is detected, and the separation process is activated. Separation is the process of advancing one agent in the opposite direction of the other until a minimum safe distance is achieved, where one agent entered by the user will not affect the other. Once the separation is complete, the agent moves to the final destination point.
Adaptive task planning is fundamental to ensuring effective and seamless human-robot collaboration. This paper introduces a robot task planning framework that takes into account both human leading/following preference...
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This study aims to address key technical issues in the segmentation of Cerebral MicroBleeds (CMBs) based on Low-Resolution (LR) Magnetic Resonance Imaging (MRI) data. There are two challenges in this task. First, the ...
This study aims to address key technical issues in the segmentation of Cerebral MicroBleeds (CMBs) based on Low-Resolution (LR) Magnetic Resonance Imaging (MRI) data. There are two challenges in this task. First, the CMB lesions are typically small in size and easily confused with various mimics. Second, anisotropy becomes more prominent and adverse in LR MRI sequences than HR sequences. To address these issues, we propose a Progressive Learning based Knowledge Distillation method. This method progressively transfers knowledge from HR models to their LR counterparts, thereby minimizing the occurrence of false positives attributable to noise from Super-Resolution. To further eliminate the influence of anisotropy, an encoding-enhanced network, called E 2 U-Net, is proposed in this paper. It can effectively capture anisotropic information and mitigates potential feature loss. The experimental results on multiple publicly accessible CMBs datasets demonstrated the superiority of our proposed approach over existing deep-learning methods.
On-demand transit service has shown itself to have great prospects in terms of addressing the issues of long-time waiting, connectivity as well as operation efficiency. From the passengers’ perspectives, the service ...
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In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its poste...
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In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.
Lane-changing (LC) is a challenging scenario for connected and automated vehicles (CAVs) because of the complex dynamics and high uncertainty of the traffic environment. This challenge can be handled by deep reinforce...
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This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles' relative or global position and velocity measurements ...
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Current frameworks for training offensive penetration testing agents with deep reinforcement learning struggle to produce agents that perform well in real-world scenarios, due to the reality gap in simulation-based fr...
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The problem of identification over a discrete memoryless wiretap channel is examined under the criterion of semantic effective secrecy. This secrecy criterion guarantees both the requirement of semantic secrecy and of...
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Growing demands in today’s industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. No...
Growing demands in today’s industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is generally not applicable, despite of some recent promising advances. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.
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