Motion planning is a fundamental research area in robotics. Sampling-based methods offer
an efficient solution for what is otherwise a rather challenging dilemma of path planning …

The prospect of driverless cars wide-scale deployment is imminent owing to the advances in
robotics, computational power, communications, and sensor technologies. This promises …

Managing artificial intelligence (AI) marks the dawn of a new age of information technology
management. Managing AI involves communicating, leading, coordinating, and controlling …

The role of information systems (IS) as representations of real-world systems is changing in
an increasingly digitalized world, suggesting that conceptual modeling is losing its …

We consider the problem of generating motion plans for a robot that are guaranteed to
succeed despite uncertainty in the environment, parametric model uncertainty, and …

Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics
applications such as self-driving cars. Existing motion planning methods become ineffective …

Autonomous car racing is a major challenge in robotics. It raises fundamental problems for
classical approaches such as planning minimum-time trajectories under uncertain dynamics …

During the last decade, sampling-based path planning algorithms, such as probabilistic
roadmaps (PRM) and rapidly exploring random trees (RRT), have been shown to work well …

A method is presented for the rapid generation and feasibility verification of motion primitives
for quadrocopters and similar multirotor vehicles. The motion primitives are defined by the …

Autonomous unmanned air vehicles (UAVs) are critical to current and future military, civil,
and commercial operations. Despite their importance, no previous textbook has accessibly …