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.

Small 3D LIDAR and a multimodal-based localization are fundamentally important for autonomous robots. This paper describes presentation and demonstration of a sensor and a method for LIDAR-image based localization. Our small LIDAR, named SPAD LIDAR, uses a single-photon avalanche diode (SPAD). The SPAD LIDAR incorporates laser receiver and environmental light receiver in a single chip. Therefore, the sensor simultaneously outputs range data and monocular image data. By virtue of this structure, the sensor requires no external calibration between range data and monocular image data. Based on this sensor, we introduce a localization method using a deep convolutional neural network (SPAD DCNN), which fuses SPAD LIDAR outputs: range data, monocular image data, and peak intensity data. Our method regresses LIDAR's position in an environment. We also introduce improved SPAD DCNN, designated as Fast SPAD DCNN. To reduce the computational demands of SPAD DCNN, Fast SPAD DCNN integrates range data and peak intensity data. The integrated data reduces runtime without greatly increasing localization error compared to the conventional method. We evaluate our SPAD DCNN and Fast SPAD DCNN localization method in indoor environments and compare its performance. Results show that SPAD DCNN and Fast SPAD DCNN improve localization in terms of accuracy and runtime.

Localization approaches typically rely on an already available map to identify the position of the sensor in the environment. Such maps are usually built beforehand and often require the user to record data from the same sensor used for localization. In this paper, we relax this assumption and present a localization approach based on architectural floor plans. In general, floor plans are readily available for most man-made buildings but only represent basic architectural structures. The incomplete knowledge leads to ambiguous pose estimates. To solve this problem, we present W-RGB-D, a new method for indoor global localization based on WiFi and an RGB-D camera. We introduce a sensor model for RGB-D cameras that is suitable to be used with abstract floor plans. To resolve ambiguities during global localization, we estimate a coarse initial distribution about the sensor position using the WiFi signal strength. We evaluate our W-RGB-D localization method in indoor environments and compare its performance with RGB-D-based Monte Carlo localization. Our results demonstrate that the use of WiFi information as proposed with our approach improves the localization in terms of convergence speed and quality of the solution.

Over the last few years, many positioning systems and information support systems using wireless LAN have been developed. Some systems use the received signal strength of a wireless LAN for positioning. However, the received signal strength differs depending on each terminal's wireless LAN adapter. It is important to investigate the differences among the received signal strengths for different wireless LAN adapters, because the differences among each adapter may cause an increase in location estimation errors. In this paper, we examine the differences among the received signal strengths for a number of wireless LAN adapters, and propose wireless LAN data usage using an ideal wireless LAN adapter. Using our method, we can improve the estimation accuracy of the location estimation system