Current Issue : April-June Volume : 2022 Issue Number : 2 Articles : 5 Articles
There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path....
Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. (is paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. (e code of the proposed model and the virtual indoor navigation environment will be released....
*e accurate design of ship routing plans in arctic areas is not easy, considering that navigation conditions (e.g., weather, visibility, and ice thickness) may change frequently. A ship’s crew identifies sea ice in arctic channels with the help of radar echoes, and ship maneuvering decisions are made to avoid navigation interference. Ship officials must manually and consistently change the ship’s route of travel, which is time-consuming and tedious. To address this issue, we propose a near-field route optimization model for the purpose of automatically selecting an optimal route with the help of radar echo images. *e ship near-field route optimization model uses a multiobjective optimal strategy considering factors of minimum navigation risk and steaming distance. We verified the model’s performance with the support of the Xuelong voyage dataset. *is research finding can help a ship’s crew to design more reasonable navigation routes in polar channels....
To improve the accuracy and reliability of the on-board navigation system positioning, the positioning control algorithm of vehicle navigation system based on wireless tracking technology is proposed. By using modern information fusion technology, the accurate positioning of vehicle integrated navigation is realized, and the design goal of omnidirectional, all weather, and self-contained positioning function is realized. Finally, the test shows that the accuracy and reliability of the positioning control algorithm of vehicle navigation system based on wireless tracking technology are improved than existing point system, speed measurement accuracy can reach 0.02 m/s, and positioning accuracy is about 18 meters. The vehicle operation efficiency and safety are greatly improved, and the traffic capacity is improved. And the traffic congestion is effectively alleviated, which provides reliable guarantee for the realization of traffic management automation and intelligent vehicle driving....
As deep reinforcement learning methods have made great progress in the visual navigation field, metalearning-based algorithms are gaining more attention since they greatly improve the expansibility of moving agents. According to metatraining mechanism, typically an initial model is trained as a metalearner by existing navigation tasks and becomes well performed in new scenes through relatively few recursive trials. However, if a metalearner is overtrained on the former tasks, it may hardly achieve generalization on navigating in unfamiliar environments as the initial model turns out to be quite biased towards former ambient configuration. In order to train an impartial navigation model and enhance its generalization capability, we propose an Unbiased Model-Agnostic Metalearning (UMAML) algorithm towards target-driven visual navigation. Inspired by entropy-based methods, maximizing the uncertainty over output labels in classification tasks, we adopt inequality measures used in Economics as a concise metric to calculate the loss deviation across unfamiliar tasks. With succinctly minimizing the inequality of task losses, an unbiased navigation model without overperforming in particular scene types can be learnt based on Model-Agnostic Metalearning mechanism. *e exploring agent complies with a more balanced update rule, able to gather navigation experience from training environments. Several experiments have been conducted, and results demonstrate that our approach outperforms other state-ofthe- art metalearning navigation methods in generalization ability....
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