Some real-world transportation systems require moving robots from their start points to goal points without collision, which can be formalized as the multi-agent pathfinding (MAPF) problem. The Priority Inheritance with Backtracking (PIBT) algorithm is an efficient approach for MAPF with low-degree polynomial time complexity and proven completeness. PIBT employs a dynamic prioritization scheme to determine the order of sequential agent planning. However, this scheme may lead to low solution quality, which is measured by the sum of time steps each agent takes to reach its goal for the first time. In this work, we propose two greedy variants of the PIBT algorithm that improve solution quality while preserving completeness: Backflow-based Greedy PIBT (GPIBT-B) and Reduction-based Greedy PIBT (GPIBT-R). GPIBT-B introduces a backflow mechanism that allows a determined agent to adjust its plan during the planning of another agent, achieving higher solution quality while maintaining the same low time complexity as PIBT. GPIBT-R formulates the planning problem as a Mixed-Integer Linear Programming (MILP), enabling the use of efficient MILP solvers to find high-quality solutions. Experimental results show that GPIBT-B substantially improves solution quality with minimal additional computation time, while GPIBT-R achieves even better solution quality at the cost of increased computational time.

The task of the multi-agent pathfinding (MAPF) problem is to navigate a team of agents from their start point to the goal points. However, this setup is unsuitable in the assembly line scenario, which is periodic with a long working hour. To address this issue, the study formalizes the streaming MAPF (S-MAPF) problem, which assumes that the agents in the same agent stream have a periodic start time and share the same action sequence. The proposed solution, Agent Stream Conflict-Based Search (ASCBS), is designed to tackle this problem by incorporating a cyclic vertex/edge constraint to handle conflicts. Additionally, this work explores the potential usage of the disjoint splitting strategy within ASCBS. Experimental results indicate that ASCBS surpasses traditional MAPF solvers in terms of runtime for scenarios with prolonged working hours.

Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are normally further interpreted as high-level map information to assist navigation tasks. Nevertheless, extracting useful information from the complex map input is challenging, for redundant information may mislead the agent and negatively affect driving performance. We propose a novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in end-to-end driving tasks. In addition, we design a new expert to enhance the model performance by considering multi-road rules. Experimental results prove that both proposed improvements enable our agent to achieve superior performance compared with other methods.

Polar Fourier analysis can extract orthogonal and rotation invariant features and demonstrate superior performances in many image processing tasks. For real world applications, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU), this research presents GPU accelerated polar Fourier analysis. Proposed parallel algorithm is based on mathematical properties of polar Fourier analysis and optimization techniques of GPU. Optimal parameter selections for GPU execution are also evaluated. In our experiments, with same computation result proposed method is over 170 times faster. Wide range of applications that using polar Fourier analysis will inspired from this work.