Algorithms of Real-Time Navigation and Control of Autonomous Unmanned Vehicles

TL;DR

This paper reviews algorithms for real-time navigation and control of autonomous vehicles, focusing on obstacle avoidance and trajectory planning in 2D and 3D environments, with applications to cars and drones.

Contribution

It introduces new algorithms for obstacle avoidance in complex environments and discusses trajectory planning for autonomous cars and drones, highlighting future research directions.

Findings

Proposed obstacle avoidance algorithms for 2D and 3D environments.

Trajectory planning methods for self-driving cars and drones.

Identified future research directions in multi-robot cooperation and hybrid algorithms.

Abstract

The rapid development of robotics has benefited by more and more people putting their attention to it. With the demand for robots is growing for the purpose of fulfilling tasks instead of humans, how to control the robot better is becoming a hot topic. For obstacle avoidance, we proposed algorithms for both 2D planar environments and 3D space environments. The example cases we raise are those that need to be addressed but have always been ignored. In addition, we put efforts into trajectory planning for robots. The two scenarios we set are self-driving cars on the road and reconnaissance and surveillance of drones. For future expectations, there are some possible directions. How to combine traditional navigation algorithms and high-tech algorithms together so as to fulfill the tasks perfectly while the computational efficiency is not too high is a worthy topic. In addition, extending the obstacle avoidance algorithms to more competitive situations. Moreover, cooperation among multi robots are worth attention by researchers. All in all, there is still a long way to go for the development of navigation and control of mobile robots. Despite this, we believe we do not need to wait for too long time to see the revolution of robots.