Mobile robots are increasingly deployed in applications ranging from household cleaning to hazardous industrial inspection, ...

Teaching robots to manipulate objects with humanlike dexterity has long been one of robotics' toughest challenges. Tasks such as rotating an object in-hand or coordinating two robot arms to maneuver a ...

Abstract: Rapidly-exploring random tree star (RRT*) has attracted intensive attention in track planning due to its asymptotic optimal properties. However, the RRT* algorithm plans costly trajectory ...

With the rapid development of artificial intelligence, computer vision, and sensor technologies, robotics have witnessed remarkable progress over the years. However, a significant challenge modern ...

Imagine you visit a maze with some friends. You emerge from the exit shortly after going in, and wait around for hours before your friends emerge. Naturally, they ask about the path you took — surely ...

The inability of glasses to find their lowest-energy configurations is often attributed to their potential energy landscapes: rugged, barrier-filled surfaces in high-dimensional space that seem ...

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

Abstract: This paper proposes a path planning algorithm using the hybridization of the rapidly-exploring random tree (RRT) and ant colony system (ACS) algorithms. The RRT algorithm can quickly ...