-The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.
Q-learning (QL) approach is constantly used for mobile robot (MR) navigation in unknown dynamic environment because of its simplicity and well-developed theory. However, its salient downside is the curse of dimensionality problem, where it incurs a huge computational power and memory requirement. This problem is aggravated in complex environments. In this paper, a collision prediction based QL (CPQL) scheme is presented to MR navigation in a dynamic environment based on collision prediction between the robot and a group of static and dynamic obstacles. In the proposed scheme, a novel definition of environment states is presented to apply QL to unknown dynamic environments with compact state space, satisfactory robot turning angles, and adequate speed gradation. The key feature of the proposed CPQL scheme pertains to constructing a state-action pair based on two factors. The first factor is the region of predicting the position of collision between the robot and an obstacle, and the second is the region of the obstacle related to robot position. Simulation analysis and results show the superiority of CPQL in terms of learning convergence, obstacle avoidance, and smooth navigation path compared with state-of-the-art MR navigation schemes. Hence, CPQL proves its authenticity and suitability for real-time navigation in complex and dynamic environments.
The methods and algorithms used for recognition texts authorship analyzes in the paper. The applied features of recognition are based on n-grams with n = 1, and n = 2. The results of computer experiments to identify the authorship of the texts in the Azerbaijani are presented.
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