With the advance in algorithms, deep reinforcement learning (DRL) offers solutions to
trajectory planning under uncertain environments. Different from traditional trajectory …

This paper is concentrated on path planning for robots working in a dynamic environment to
satisfy real-time needs. An efficient bias-goal factor RRT (EBG-RRT), which is multiple-query …

Recently, the wide application of unmanned surface vehicles (USVs) in various fields has
deemed path planning of USVs in complex environments, particularly in intelligent ports and …

To plan a successful and practically executable path for a manipulator in a complex obstacle
environment, the Improved_RRT method is proposed. In this article, we develop the collision …

In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to
perform military missions autonomously. This paper proposes a novel algorithm called flight …

This article establishes the Exploration-RRT algorithm: A novel general-purpose combined
exploration and path planning algorithm, based on a multi-goal Rapidly-Exploring Random …

Local path planning is important in the development of autonomous vehicles since it allows
a vehicle to adapt their movements to dynamic environments, for instance, when obstacles …

This paper proposes a model-free control framework for the path planning of the rigid and
soft robotic manipulator using an intelligent algorithm called Weighted Jacobian Rapidly …

Aiming at the problems of large randomness, slow convergence speed, and deviation of
Rapidly‐Exploring Random Tree algorithm, a new node is generated by a cyclic alternating …

Sampling-based motion planners are widely used in robotics due to their simplicity, flexibility
and computational efficiency. However, in their most basic form, these algorithms operate …