CPPNet: A Coverage Path Planning Network
This work addresses coverage path planning for robotics in partially unknown and dynamic environments, offering an incremental improvement in efficiency.
The paper tackles the problem of coverage path planning by introducing CPPNet, a deep-learning algorithm that uses a CNN to generate near-optimal solutions with significantly reduced computational time, enabling real-time planning in dynamic environments.
This paper presents a deep-learning based CPP algorithm, called Coverage Path Planning Network (CPPNet). CPPNet is built using a convolutional neural network (CNN) whose input is a graph-based representation of the occupancy grid map while its output is an edge probability heat graph, where the value of each edge is the probability of belonging to the optimal TSP tour. Finally, a greedy search is used to select the final optimized tour. CPPNet is trained and comparatively evaluated against the TSP tour. It is shown that CPPNet provides near-optimal solutions while requiring significantly less computational time, thus enabling real-time coverage path planning in partially unknown and dynamic environments.