Multirobot path planning in complex environments is a challenging research area. This paper proposes a path planning method for multirobot systems based on distributed multiagent deep reinforcement learning. We propose a multirobot dynamic window approach (MRDWA) in which a central controller facilitates sensor information sharing among robots, enabling locally optimal path planning considering the behavior of other robots. We incorporate the output velocity information into the observation function to form an efficient and low-dimensional state representation. Additionally, we employ the multiagent deep deterministic policy gradient (MADDPG) reinforcement learning algorithm to directly map part of the observation information to motion commands for multiple robots, enabling effective obstacle avoidance strategies. An improved action module is developed by using velocity and angular velocity increments and an action selector to refine the output. Furthermore, we introduce a multirobot reward module utilizing heuristic functions to guide the robots to quickly and efficiently identify feasible paths. We also propose a multirobot dynamic constraint reward function to optimize the multirobot trajectories. The MRDWA-MADDPG algorithm is validated through simulations and real-world experiments, demonstrating its effectiveness in diverse complex multirobot path planning scenarios. Our method outperforms conventional algorithms in terms of success rate, arrival time, and overall decision making in complex scenarios. Moreover, our method has a faster computation speed and a shorter training time, produces smoother trajectories, and is easier to deploy on real robots than other learning-based methods.

In some large environments where humans and machines coexist, such as smart factories, restaurants, and hotels, multiple mobile robots system have certain advantages over single mobile robot in terms of task complexity, execution efficiency, and system robustness. However, path conflicts and blockages may occur among robots without effective global coordination method, especially in some special areas like narrow straight roads (N-S-R) and crossroads (C-R). To solve this problem, using the global topology map might be a good solution. Therefore, this paper proposes a method called Surplus Topology Generation Algorithm (STGA) for constructing a global topological map based on grid map representation. On the basis of grid maps, we designed Node-Filter to generate enough topological nodes, and simplified the node set by Node-Selection. In special areas such as N-S-R and C-R, which are prone to path conflicts and blocking scenarios, we extracted the Harris corner point and combined them to select the corner point combinations that characterize some special features like passage entrance, etc. Then, feature nodes that can characterize the special features were obtained by establishing mapping relationships between nodes and corner point combinations. Finally, the global topology map was obtained after establishing the reachability relationships between nodes. The simulation experiment results showed that the topological maps generated by STGA had obvious advantages in the richness of topological relationships. Besides, the topological maps also showed certain advantages compared to grid maps for the planning efficiency and security of global paths. Furthermore, the rich topological nodes on the global topological map were feasible in solving path conflicts of multiple robots. Note to Practitioners—Multi-robot system has better efficiency and system robustness, but how to solve the path conflicts among robots is a problem. We propose a method to solve multi-robot conflicts with global topological maps. This topological map is constructed by STGA. The grid map environment is discretized into a set of topological nodes, which have been used to maintain the spatial transitions of each robot in the environment. When robots encounter unavoidable path conflicts, they are scheduled with the help of topological nodes to achieve orderly and efficient collaboration.

This work reports a master-slave separate parallel intelligent mobile robot for the fully autonomous transportation of pallets in the smart factory logistics. This separate parallel intelligent mobile robot consists of two independent sub robots, one master robot and one slave robot. It is similar to two forks of the forklift, but the slave robot does not have any physical or mechanical connection with the master robot. A compact driving unit was designed and used to ensure access to the narrow free entry under the pallets. It was also possible for the mobile robot to perform a synchronous pallet lifting action. In order to ensure the consistency and synchronization of the motions of the two sub robots, high-gain observer was used to synchronize the moving speed, the lifting speed and the relative position. Compared with the traditional forklift AGV (Automated Guided Vehicle), the mobile robot has the advantages of more compact structure, higher expandability and safety. It can move flexibly and take zero-radius turn. Therefore, the intelligent mobile robot is quite suitable for the standardized logistics factory with small working space.