Comparative analysis of the equipment for the formation of navigation radio signals of the GLONASS system and features of group navigation radio signals

In this paper comparative analysis of methods for forming navigation radio signals of the GLONASS system at each stage is presented. This analysis gives a clear picture of the evolution of the methods for forming navigation radio signals, and provides insights into their advantages and disadvantages. Special attention is paid to group navigation signals of the GLONASS system, namely the effective power amplification of the group navigation radiosignal.

Comparative Analysis of Equipment for Shaping Navigation Radio Signals of the GLONASS system and Features of Group Navigation Radio Signals

This paper presents a comparative analysis of methods for shaping navigation radio signals of the GLONASS system at all stages of development. This analysis gives a clear picture of the evolution of methods for shaping navigation radio signals and provides insights into their advantages and disadvantages. Special attention is paid to group navigation signals of the GLONASS system, which appear in the next generation of GLONASS navigation spacecraft to optimize the antenna assembly. The peculiarities of shaping them are considered, taking account of ensuring code measurement accuracy and effective power amplification.

Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning

Compared with the single robot system, a multi-robot system has higher efficiency and fault tolerance. The multi-robot system has great potential in some application scenarios, such as the robot search, rescue and escort tasks, and so on. Deep reinforcement learning provides a potential framework for multi-robot formation and collaborative navigation. This paper mainly studies the collaborative formation and navigation of multi-robots by using the deep reinforcement learning algorithm. The proposed method improves the classical Deep Deterministic Policy Gradient (DDPG) to address the single robot mapless navigation task. We also extend the single-robot Deep Deterministic Policy Gradient algorithm to the multi-robot system, and obtain the Parallel Deep Deterministic Policy Gradient (PDDPG). By utilizing the 2D lidar sensor, the group of robots can accomplish the formation construction task and the collaborative formation navigation task. The experiment results in a Gazebo simulation platform illustrates that our method is capable of guiding mobile robots to construct the formation and keep the formation during group navigation, directly through raw lidar data inputs.