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 …

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 …

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 …

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 …

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving
experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The …

The recent proliferation of computing technologies (eg, sensors, computer vision, machine
learning, and hardware acceleration) and the broad deployment of communication …

We survey research on self-driving cars published in the literature focusing on autonomous
cars developed since the DARPA challenges, which are equipped with an autonomy system …

Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great
examples of mobile robots. They can be distinguished from other robots by their ability to …

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 …

Rapidly random-exploring tree (RRT) and its variants are very popular due to their ability to
quickly and efficiently explore the state space. However, they suffer sensitivity to the initial …