Forklifts are used extensively in various industrial settings and are in high demand for automation. In particular, counterbalance forklifts are highly versatile and are employed in diverse scenarios. However, efforts to automate these processes are lacking, primarily owing to the absence of a safe and performance-verifiable development environment. This study proposes a learning system that combines a photorealistic digital learning environment with a$1 / 14$-scale robotic forklift environment to address this challenge. Inspired by the training-based learning approach adopted by forklift operators, we employ an end-to-end vision-based deep reinforcement learning approach. The learning is conducted in a digitalized environment created from CAD data, making it safe and eliminating the need for real-world data. In addition, we safely validate the method in a physical setting using a$1 / 14$-scale robotic forklift with a configuration similar to that of a real forklift. We achieved a 60% success rate in pallet loading tasks in real experiments using a robotic forklift. Our approach demonstrates zero-shot sim2real with a simple method that does not require heuristic additions. This learning-based approach is considered a first step towards the automation of counterbalance forklifts.

Managing delivery deadlines in automated warehouses and factories is crucial for maintaining customer satisfaction and ensuring seamless production. This study introduces the problem of online multi-agent pickup and delivery with task deadlines (MAPD-D), an advanced variant of the online MAPD problem incorporating delivery deadlines. In the MAPD problem, agents must manage a continuous stream of delivery tasks online. Tasks are added at any time. Agents must complete their tasks while avoiding collisions with each other. MAPD-D introduces a dynamic, deadline-driven approach that incorporates task deadlines, challenging the conventional MAPD frameworks. To tackle MAPD-D, we propose a novel algorithm named deadline-aware token passing (D-TP). The D-TP algorithm calculates pickup deadlines and assigns tasks while balancing execution cost and deadline proximity. Additionally, we introduce the D-TP with task swaps (D-TPTS) method to further reduce task tardiness, enhancing flexibility and efficiency through task-swapping strategies. Numerical experiments were conducted in simulated warehouse environments to showcase the effectiveness of the proposed methods. Both D-TP and D-TPTS demonstrated significant reductions in task tardiness compared to existing methods. Our methods contribute to efficient operations in automated warehouses and factories with delivery deadlines.

In environments where many automated guided vehicles (AGVs) operate, planning efficient, collision-free paths is essential. Related research has mainly focused on environments with pre-defined passages, resulting in space inefficiency. We attempt to relax this assumption. In this study, we define multi-agent and multi-rack path finding (MARPF) as the problem of planning paths for AGVs to convey target racks to their designated locations in environments without passages. In such environments, an AGV without a rack can pass under racks, whereas one with a rack cannot pass under racks to avoid collisions. MARPF entails conveying the target racks without collisions, while the obstacle racks are relocated to prevent any interference with the target racks. We formulated MARPF as an integer linear programming problem in a network flow. To distinguish situations in which an AGV is or is not loading a rack, the proposed method introduces two virtual layers into the network. We optimized the AGVs’ movements to move obstacle racks and convey the target racks. The formulation and applicability of the algorithm were validated through numerical experiments. The results indicated that the proposed algorithm addressed issues in environments with dense racks.

One of the purposes of smart agriculture is to predict the yield of paddy rice using agricultural data using machine learning. LightGBM, one of the machine learning algorithms, is applied to the yield prediction problem of paddy rice in this paper. Since LightGBM has a large number of hyperparameters, the hyperparameter optimization using the stochastic schemata exploiter (SSE) is used. From the results of comparison with Genetic Algorithm (GA), it is confirmed that SSE has a fast convergence speed. In addition, it is found that the higher the mutation rate of SSE, the more converged to the global optimal solution without falling into the local solution.

Stochastic Schemata Exploiter (SSE), which is one of Evolutionary Computations, is designed to find the optimal solution of the function. When comparing it with Genetic Algorithm (GA), which is a population evolutionary computation, SSE has interesting features; quick convergence and smaller number of control parameters. In this study, SSE is applied for designing hyperparameters and structure of Convolutional Neural Network (CNN). The validity of the proposal algorithm is discussed for determining CNN in experiments using CIFAR-10. The results show that SSE can find the better parameters and structure of CNN than GA.