To reduce the blocking rate of multi-robot path planning in dynamic environments， a Distributed Communication and local Attention based Multi-Agent Path Finding （DCAMAPF） was proposed based on Actor-Critic deep reinforcement learning method framework， using request-response communication mechanism and local attention mechanism. In the Actor network， local observation and action information was requested by each robot from other robots in its field of view based on the request-response communication mechanism， and a coordinated action strategy was planned accordingly. In the Critic network， attention weights were dynamically allocated by each robot to the local observation and action information of other robots that had successfully responded within its field of view based on the local attention mechanism. The experimental results showed that， the blocking rate was reduced by approximately 6.91， 4.97， and 3.56 percentage points， respectively， in a discrete initialization environment， compared with traditional dynamic path planning methods such as D^* Lite， the latest distributed reinforcement learning method MAPPER， and the latest centralized reinforcement learning method AB-MAPPER （Attention and BicNet based MAPPER）； in a centralized initialization environment， the mean blocking rate was reduced by approximately 15.86， 11.71 and 5.54 percentage points； while the occupied computing cache was also reduced. Therefore， the proposed method ensures the efficiency of path planning and is applicable for solving multi-robot path planning tasks in different dynamic environments.

The purpose of research on Multi-Robot Task Allocation （MRTA） is to improve the task completion efficiency of robots in smart factories. Aiming at the deficiency of the existing algorithms in dealing with large-scale multi-constrained MRTA， an MRTA Algorithm Combining Genetic Algorithm and Rolling Scheduling （ACGARS） was proposed. Firstly， the coding method based on Directed Acyclic Graph （DAG） was adopted in genetic algorithm to efficiently deal with the priority constraints among tasks. Then， the prior knowledge was added to the initial population of genetic algorithm to improve the search efficiency of the algorithm. Finally， a rolling scheduling strategy based on task groups was designed to reduce the scale of the problem to be solved， thereby solving large-scale problems efficiently. Experimental results on large-scale problem instances show that compared with the schemes generated by Constructive Heuristic Algorithm （CHA）， MinInterfere Algorithm （MIA）， and Genetic Algorithm with Penalty Strategy （GAPS）， the scheme generated by the proposed algorithm has the average order completion time shortened by 30.02%， 16.86% and 75.65% respectively when the number of task groups is 20， which verifies that the proposed algorithm can effectively shorten the average waiting time of orders and improve the efficiency of multi-robot task allocation.