Recently, Shuttle-Based Storage and Retrieval Systems (SBS/RSs) have emerged as a cornerstone of modern robotic handling systems. However, motion conflicts between four-way shuttle vehicles are introduced frequently due to the sparsely connected roadmap topology. Furthermore, the different time and energy costs between linear and direction-changing motions bring additional difficulties for classic solvers when applied directly. To address these issues, we propose Dynamic Graph-Driven A* (DGA*) for cooperative path planning in SBS/RSs. By fully exploiting the structured topology of SBS/RS roadmaps, the algorithm extracts sector-level topological graphs enriched with spatiotemporal attributes, effectively resolving conflicts while simultaneously optimizing motion costs. In addition, Recursive Preemption and Avoidance (RPA) is introduced to ensure motion coordination in congestion-prone areas commonly seen in SBS/RSs, through a combination of independent avoidance and recursive avoidance mechanisms. We implement our approach in a real warehouse with more than 240 vehicles and achieve a 17% improvement in system throughput while reducing runtime.

In warehousing systems, to enhance efficiency amid surging demand volumes, much attention has been placed on how to reasonably allocate tasks of delivery to robots. However, the labor of robots is still inevitably wasted to some extent. In this paper, we propose a pre-scheduling enhanced warehousing framework aiming to foresee and act in advance, which consists of task flow prediction and hybrid task allocation. For task prediction, we design the spatio-temporal representations of the task flow and introduce a periodicity-decoupled mechanism tailored for the generation patterns of aggregated orders, and then further extract spatial features of task distribution with a novel combination of graph structures. In hybrid tasks allocation, we consider the known tasks and predicted future tasks simultaneously to optimize the task allocation. In addition, we consider factors such as predicted task uncertainty and sector-level efficiency to realize more balanced and rational allocations. We validate our task prediction model across datasets derived from factories, achieving SOTA performance. Furthermore, we implement our system in a real-world robotic warehouse, demonstrating more than 30% improvements in efficiency.

Robotic systems with hundreds or even thousands of robots are widely implemented in logistic and industrial applications. In such systems, cooperative path planning is of great importance, as local congestion and motion conflict may greatly degrade system performance, especially in the presence of uncertainties. Our idea is to consider traffic flow equilibrium in path planning to relieve any potential congestion and increase efficiency. In this paper, we propose a hierarchical framework, which includes a traffic flow prediction layer, a sector-level planning layer, and a road-level coordination layer. In traffic flow prediction, we propose a spatio-temporal graph neural network that integrates local information to predict the evolution of future robot density distribution. In sector-level planning, we generate sector-level paths that consider travel distance and traffic flow equilibrium simultaneously. In road-level coordination, we implement the conflict-based search algorithm within each sector to ensure conflict-free local paths. In addition, we also explicitly consider motion/communication uncertainties that are unavoided in practical systems. We validate our effectiveness in simulations with over 1000 robots, what’s more, real experiments are provided.

To date, multi-robot path planning has primarily been addressed by centralized solvers, typically aiming to maintain optimality. However, given its NP-hard nature, directly applying existing solvers in large and complex scenarios proves inefficient. A promising alternative lies in adopting a divide- and-conquer strategy to break down the problem into manageable sub-problems. In this work, we propose a systematic, universal, and scalable graph partitioning method, aiming to automatically divide any real-world environment into multiple regions. Building upon this, we convert the path planning on the entire graph into distributed sub-region path planning and devise corresponding inter-regional strategies. Our work can be easily implementable in practical systems and effectively enhances the scalability of existing solvers. Experimentally, our approach contributes to a tenfold improvement in computational efficiency while only sacrificing about 10% of optimality.

Recently, Shuttle-based Storage and Retrieval Systems (SBS/RSs) have garnered significant attention from both academia and industry, owing to their high spatial utilization and rapid response speed. However, the weak connectivity of roadmaps in densely stored environments increases the likelihood of congestion and deadlocks when multiple four-way shuttles operate simultaneously, thereby imposing greater demands for collaborative path planning. Instead of exhaustively coordinating the shuttle motions during the off-line planning or online local control stages, we solve the cooperative path planning challenge from the perspective of altering the road graph structure dynamically. More specifically, we propose an approach to automatically transfer the typical road graph of SBS/RSs into a hierarchical graph with a reduced size, and then dynamically adjust its edge properties to prohibit any motion conflicts. In this manner, the planning problem of large-scale shuttle groups can be easily resolved and all the potential congestions can be eliminated inherently. Finally, we build a complete multi-shuttle cooperative path planning system adaptable for large-scale problems.

Human drivers are remarkably robust against various unexpected occurring variations and corruptions by understanding temporal changes and traffic scenes. In contrast, the neural network based autonomous navigation system can be easily affected by sensor data anomaly, like occlusion, sensor noise, challenging weather and illumination conditions. Such external disturbances are inevitable in practical driving applications. In this paper, we develop a semi-supervised anomaly detection module to detect the corrupted data while extracting the traffic scenario features. We further introduce an end-to-end robust autonomous navigation framework based on the idea that the consecutive frames of clean data depict a similar traffic scenario and the differences among the sequential data imply the dynamic state changes. By taking into consideration both spatial traffic scenario and temporal environmental variation, the model is able to achieve robust navigation against sensor data corruptions. We conduct experiments in CARLA platform and the evaluation results show the effectiveness of the proposed method.