Mobile Robot Motion Planning Based on Time-Delay CNN with Open-Space Image Inputs for Multi-Obstacle Avoidance

During autonomous navigation, mobile robots of-ten need to avoid obstacles in their path. To address this obstacle avoidance issue, we have proposed various motion planners based on deep neural networks. Focusing on obstacle avoidance, a mobile robot is typically required to move straight for a certain duration after avoiding an obstacle before reorienting itself toward the destination. However, in such cases, it is difficult for the robot to plan these different motions based on similar image inputs. To address this challenge, we propose a novel motion planner based on a Time-Delay CNN that utilizes visually distinct time-series image inputs. Through experiments, we demonstrate that the robot is able to plan appropriate avoidance motions as described above and navigate toward the destination in both simulation and real-world environments with multiple dynamic obstacles.