Motion planning is important in a wide variety of applications such as robotic manipulation. However, it is still challenging to reliably find a collision-free path within a reasonable time. To address the issue, this paper proposes a novel framework which combines a sampling-based planner and deep learning for faster motion planning, focusing on heuristics. The proposed method extends Task-Space Rapidlyexploring Random Trees (TS-RRT) to guide the trees with a "heuristic map" where every voxel has a cost-to-go value toward the goal. It also utilizes fully convolutional neural networks (CNNs) for producing more appropriate heuristic maps, rather than manually-designed heuristics. To verify the effectiveness of the proposed method, experiments for motion planning using a real environment and mobile manipulator are carried out. The results indicate that it outperforms the existing planners, especially in terms of the average planning time with smaller variance.
