End-to-End Discrete Motion Planner based on Deep Neural Network for Autonomous Mobile Robots

For autonomous navigation of mobile robots, collision avoidance with obstacles is an essential capability. Previously, we found that operators controlled a mobile robot with discrete motion commands, such as straight, right, and left, through a joystick. In this paper, therefore, we propose an end-to-end discrete motion planner. This motion planner is based on a deep neural network. For network training, we adopt a learning-from-demonstration approach. Through the developed teaching system, an operator controls the robot so as to move forward while avoiding collisions with obstacles. In doing so, the robot is allowed to record sensor data and discrete motion commands given by operators as inputs and outputs. In addition to the sensor data, we use a direction angle between the robot and goal destination as the input. All of these input and output data are the training data sets for the deep neural network. In the navigation experiments, we show that the robot based on the end-to-end discrete motion planner is able to move toward the goal destination while avoiding collisions with obstacles.

[1]  Eijiro Takeuchi,et al.  End-to-End Autonomous Mobile Robot Navigation with Model-Based System Support , 2018, J. Robotics Mechatronics.

[2]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[3]  Yann LeCun,et al.  Off-Road Obstacle Avoidance through End-to-End Learning , 2005, NIPS.

[4]  Eijiro Takeuchi,et al.  End-to-End Navigation with Branch Turning Support Using Convolutional Neural Network , 2018, 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[5]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[6]  Johann Marius Zöllner,et al.  Adding navigation to the equation: Turning decisions for end-to-end vehicle control , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[7]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[8]  Yang Gao,et al.  End-to-End Learning of Driving Models from Large-Scale Video Datasets , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Wenshuo Wang,et al.  Feature analysis and selection for training an end-to-end autonomous vehicle controller using deep learning approach , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[10]  Roland Siegwart,et al.  From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[12]  Xin Zhang,et al.  End to End Learning for Self-Driving Cars , 2016, ArXiv.

[13]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[14]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[15]  Masahiro Kato,et al.  Teach-and-Replay of Mobile Robot with Particle Filter on Episode , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).