Active simultaneous localization and map** (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

Recent years have seen a dramatic rise in the popularity of autonomous mobile robots
(AMRs) due to their practicality and potential uses in the modern world. Path planning is …

This comprehensive review focuses on the Autonomous Driving System (ADS), which aims
to reduce human errors that are the reason for about 95% of car accidents. The ADS …

Reliable and efficient trajectory generation methods are a fundamental need for
autonomous dynamical systems. The goal of this article is to provide a comprehensive …

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and
autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths …

The problem of planning for a robot that operates in environments containing a large
number of objects, taking actions to move itself through the world as well as to change the …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD)
have attracted widespread attention due to their outstanding performance and the ability to …

Many safety-critical applications of neural networks, such as robotic control, require safety
guarantees. This article introduces a method for ensuring the safety of learned models for …

Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …