To improve accuracy， robustness， and generalization of point cloud registration and address the problem of the Iterative Closest Point （ICP） algorithm easily falling into local optimal solution， a point cloud registration method of coordinate Geometric Sampling based on Deep Closest Point （GSDCP） was proposed. Firstly， the central point curvature was estimated using coordinates of surrounding points of each point， and points that preserved geometric features of the point cloud were selected through curvature sizes， so as to realize downsampling of the point cloud. Secondly， a Dynamic Graph Convolutional Neural Network （DGCNN） was employed to coordinate with the downsampled point cloud to learn point cloud features that incorporated local geometry information， and contextual information was captured using a Transformer， and soft Pointers facilitate approximate combination and matching between two feature embedders. Finally， a differentiable Single Value Decomposition （SVD） layer was utilized to estimate the final rigid transformation. Point cloud registration experimental results on ModelNet40 dataset show that compared with ICP， Globally optimal ICP （Go-ICP）， PointNetLK， Fast Global Registration （FGR）， ADGCNNLK （Attention Dynamic Graph Convolutional Neural Network Lucas-Kanade）， Deep Closest Point （DCP）， and Multi-Features Guidance Network （MFGNet）， GSDCP achieves all the best registration accuracy and robustness in scenarios with or without noise， as well as when the point cloud category is invisible. In noise-free scenario， GSDCP reduces rotational Mean Square Error （MSE） by 31.3% and translational MSE by 58.3% compared to MFGNet. In noisy scenario， GSDCP reduces rotational MSE by 33.9% and translational MSE by 73.4% compared to MFGNet. When the point cloud category is invisible， GSDCP reduces rotational MSE by 57.7% and translational MSE by 77.9% compared to MFGNet. Additionally， when dealing with incomplete point cloud data （including random occlusion and fragmentary point cloud）， GSDCP exhibits reductions of 35.1% in rotational MSE and 39.8% in translational MSE compared to MFGNet when point cloud integrity is below 75%.

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.

When multiple agents performing path finding in large-scale warehousing environment， the existing algorithms have problems that agents are prone to fall into congestion areas and it take a long time. In response to the above problem， an improved Conflict-Based Search （CBS） algorithm was proposed. Firstly， the existing single warehousing environment modeling method was optimized. Based on the traditional grid based modeling， which is easy to solve path conflicts， a hybrid modeling method of grid-heat map was proposed， and congestion areas in the warehouse were located through a heat map， thereby addressing the issue of multiple agents prone to falling into congestion areas. Then， an improved CBS algorithm was employed to solve the Multi-Agent Path Finding （MAPF） problems in large-scale warehousing environment. Finally， a Heat Map for Explicit Estimation Conflict-Based Search （HM-EECBS） algorithm was proposed. Experimental results show that on warehouse-20-40-10-2-2 large map set， when the number of agents is 500， compared with Explicit Estimation Conflict-Based Search （EECBS） algorithm and Lazy Constraints Addition for MAPF （LaCAM） algorithm， HM-EECBS algorithm has the solution time reduced by about 88% and 73% respectively； when there is 5%，10% area congestion in warehouse， the success rate of HM-EECBS algorithm is increased by about 49% and 20% respectively， which illustrates that the proposed algorithm is suitable for solving MAPF problems in large-scale and congested warehousing and logistics 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.