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 …

Motion planning is a fundamental problem in autonomous robotics that requires finding a
path to a specified goal that avoids obstacles and takes into account a robot's limitations and …

In this article, we present an optimization-based framework for multicopter trajectory
planning subject to geometrical configuration constraints and user-defined dynamic …

Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning
problems. The standard version of RRT grows a tree from a start location and stops once it …

Path planning in robotics often requires finding high-quality solutions to continuously valued
and/or high-dimensional problems. These problems are challenging and most planning …

Informed sampling-based planning algorithms exploit problem knowledge for better search
performance. This knowledge is often expressed as heuristic estimates of solution cost and …

Bidirectional rapidly exploring random trees (Bi-RRTs) have been widely applied in path
planning and have been demonstrated to yield the optimal path for indoor robots. However …

Optimal path planning is the problem of finding a valid sequence of states between a start
and goal that optimizes an objective. Informed path planning algorithms order their search …

This paper presents a novel trajectory planning algorithm for quadrotors landing on aerial
vehicle carriers (AVCs). The algorithm involves a quadrotor trajectory planning method …

Planning smooth and energy-efficient paths for wheeled mobile robots is a central task for
applications ranging from autonomous driving to service and intralogistic robotics. Over the …