Digital transformation of warehousing is revolutionizing operations by integrating advanced
technologies. Automated Guided Vehicles (AGVs) optimize the movement of goods, while …

With the continuous breakthroughs in core technology, the dawn of large-scale integration of
robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on …

Task allocation is a key combinatorial optimization problem, crucial for modern applications
such as multi-robot cooperation and resource scheduling. Decision makers must allocate …

Many multi-robot deployments, such as automated construction of buildings, distributed
search, or cooperative mapping, often require agents to intelligently coordinate their …

With the growing maturity of multi-robot system technology, its applications have expanded
across various domains. This paper addresses the critical issue of task allocation in nuclear …

A unified framework for multi-agent task assignment and distributed trajectory planning that
can autonomously adapt to complex interactive environments and multi-task constraints has …

In many real-world scenarios (eg, product manufacturing), multiple heterogeneous robots
cooperate to complete complex tasks with precedence constraints. In these heterogeneous …

In smart warehouses that use automated guided vehicles (AGVs) for goods transportation,
task allocation has a great impact on operational efficiency. Currently, warehouse task …

Human-in-the-loop reinforcement learning integrates human expertise to accelerate agent
learning and provide critical guidance and feedback in complex fields. However, many …

Recent developments in deep learning have enabled reinforcement learning (RL) methods
to drive optimal policies for a sophisticated high-dimensional environment, which is suitable …