Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

In a dynamic environment, the moving obstacle makes the path planning of the manipulator
very difficult. Therefore, this paper proposes a path planning with dynamic obstacle …

We propose a framework for planning in unknown dynamic environments with probabilistic
safety guarantees using conformal prediction. Particularly, we design a model predictive …

Effective communication is key to successful, decentralized, multi-robot path planning. Yet, it
is far from obvious what information is crucial to the task at hand, and how and when it must …

The domains of transport and logistics are increasingly relying on autonomous mobile
robots for the handling and distribution of passengers or resources. At large system scales …

We consider the problem of designing distributed collision-avoidance multi-agent control in
large-scale environments with potentially moving obstacles, where a large number of agents …

Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one
of the best-known path planning algorithms. However, the traditional A* algorithm has some …

Applying the learning mechanism of natural living beings to endow intelligent robots with
humanoid perception and decision-making wisdom becomes an important force to promote …

Existing mobile robots cannot complete some functions. To solve these problems, which
include autonomous learning in path planning, the slow convergence of path planning, and …