The task of keeping a specific geometric configuration while following a designated path is common in various fields that involve autonomous robots. When done correctly, this approach can result in several advantages, including a reduction in system costs and an increase in system reliability and efficiency, while maintaining the adaptability and flexibility of the system. The concept of maintaining a particular geometric pattern during motion is frequently utilized in different scenarios. For example, unmanned military vehicles must maintain a particular formation to explore and cover terrain during missions. Another example is in “Smart Highways,” where the traffic system's capacity can be enhanced significantly by having vehicles move in groups at the same speed while maintaining a certain distance between them. This research presents an algorithm that allows a group of self-governing mobile robots to move while maintaining a specific geometric structure. A behavior-based control algorithm is advanced to regulate the movement of these systems. The coordination of the robots within the system is accomplished through the implementation of a leader-follower technique, with virtual leader.

Maintaining a specific geometric pattern is essential in various applications where groups of autonomous robots must follow a given path. Proper organization of the geometric pattern can lead to several benefits such as cost reduction, increased system reliability, and efficiency while providing a reconfigurable and flexible structure of the system. Military missions and traffic systems are examples where maintaining certain geometric patterns are widely used. However, little is known about how to develop an effective algorithm that guarantees collision avoidance and obstacle avoidance while maintaining the geometric pattern. This paper presents an algorithm for movement with a certain geometric structure of a group of autonomous mobile robots that maintains the required geometric pattern and ensures the avoidance of collisions and obstacles. The proposed algorithm is behavior-based and utilizes a set of rules that allow the robots to navigate around obstacles and avoid collisions. The algorithm's performance is demonstrated through simulations in a variety of scenarios with different numbers of robots and geometric patterns. The algorithm proposed in this paper provides an effective solution for controlling a group of autonomous mobile robots to maintain a certain geometric pattern. The proposed algorithm has the potential to be utilized in numerous applications where multiple robots must work together to achieve a common goal while maintaining a specific formation. The use of behavior-based approach and obstacle avoidance rules ensures that the robots avoid collisions and obstacles while maintaining the required pattern

This study investigates the automatic transformation of Kazakh text into sign language glosses (Text-to-Gloss) using multilingual transformer-based models with emphasis on preserving morphological structure in a low-resource agglutinative language framework. Given the scarcity of high-quality intermediate representations for Kazakh Sign Language, a methodology for corpus formation was developed, resulting in a specialized dataset of 11 190 unique text−gloss pairs sourced from educational materials. To assess the effectiveness of transfer learning under low-resource constraints, we fine-tuned mT5-small and mT5-base models on this dataset. Experimental results indicate that the mT5-base architecture consistently outperforms the smaller variant across all metrics, achieving a 2.61-point improvement in BLEU (87.51 → 90.12) and a 2.47-point increase in ROUGE-1 F1 (89.61% → 92.08%), alongside gains in exact match (74.96% → 80.68%) and chrF (91.6 → 94.1). Statistical significance testing ( p < 0.001) confirms the stability of this improvement. Error analysis reveals that larger model capacity particularly improves morphological handling and token coverage, which is critical for agglutinative languages, while maintaining semantic accuracy. These findings demonstrate that pretrained multilingual models effectively address TTG transformation for low-resource agglutinative languages through transfer learning, with practical applications for Kazakh Sign Language assistive systems and education.