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 …

Path planning constitutes one of the most crucial abilities an autonomous robot should
possess, apart from Simultaneous Localization and Mapping algorithms (SLAM) and …

Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …

In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown
significantly in size and importance in recent years. Having made great progress in the …

This paper proposes a noble multi-robot path planning algorithm using Deep q learning
combined with CNN (Convolution Neural Network) algorithm. In conventional path planning …

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 …

Abstract The Rapidly-exploring Random Tree (RRT) algorithm is a popular algorithm in
motion planning problems. The optimal RRT (RRT*) is an extended algorithm of RRT, which …

The ever-increasing complexity of robot applications induces the need for methods to
approach problems with no (viable) analytical solution. Deep learning (DL) provides a set of …

Planning algorithms are impacting technical disciplines and industries around the world,
including robotics, computer-aided design, manufacturing, computer graphics, aerospace …

In this paper, we present a novel path planning algorithm called Kinematic Constrained Bi-
directional Rapidly-exploring Random Tree with Efficient Branch Pruning (KB-RRT*), which …