6 accepted papers
Multi-Agent Path Finding (MAPF) is an important issue in multi-agent cooperation. Many studies apply MultiAgent Reinforcement Learning (MARL) to solve MAPF in partially observable settings. The objective of cooperative MARL is to maximize the cumulative team reward. Nevertheless, in partially observ…
This letter proposes a novel and simple smooth path generation algorithm for autonomous vehicles. The proposed method can rapidly generate feasible and curvature continuous paths connecting any two given states with null curvature. The generated path comprises straight lines, circular arcs and cloth
Local planning for a differential wheeled robot is designed to generate kinodynamic feasible actions that guide the robot to a goal position along the navigation path while avoiding obstacles. Reactive, predictive, and learning-based methods are widely used in local planning. However, few of them ca
Feature Pyramid Network (FPN) has been widely adopted to exploit multi-scale features for scale variation in object detection. However, intrinsic defects in most of the current methods with FPN make it difficult to adapt to the feature of different geometric objects. To address this issue, we introd…
Collision risk and smoothness are the most important factors in global path planning. Currently, planning methods that reduce global path collision risk and improve its smoothness through numerical optimization have achieved good results. However, these methods cannot always optimize the path. The r…
The path planning of mobile robots is an optimization problem that is difficult to solve directly owing to its nonlinear characteristics. This letter proposes the “global-local” Coupling Two-Stage Path Planning (CTSP) method. First, the globally optimal solution in the configuration space is given b