Enhance Connectivity of Promising Regions for Sampling-based Path Planning
This work addresses a specific bottleneck in path planning for robotics or autonomous systems, but it is incremental as it builds directly on previous work to refine region connectivity.
The paper tackles the problem of disconnected promising regions in sampling-based path planning, which can break probabilistic completeness, by proposing a method that regresses connectivity probabilities and adjusts loss weights to enhance connectivity, resulting in significant improvements in connectivity as shown in simulation experiments.
Sampling-based path planning algorithms usually implement uniform sampling methods to search the state space. However, uniform sampling may lead to unnecessary exploration in many scenarios, such as the environment with a few dead ends. Our previous work proposes to use the promising region to guide the sampling process to address the issue. However, the predicted promising regions are often disconnected, which means they cannot connect the start and goal state, resulting in a lack of probabilistic completeness. This work focuses on enhancing the connectivity of predicted promising regions. Our proposed method regresses the connectivity probability of the edges in the x and y directions. In addition, it calculates the weight of the promising edges in loss to guide the neural network to pay more attention to the connectivity of the promising regions. We conduct a series of simulation experiments, and the results show that the connectivity of promising regions improves significantly. Furthermore, we analyze the effect of connectivity on sampling-based path planning algorithms and conclude that connectivity plays an essential role in maintaining algorithm performance.