Augmenting Computer Vision with IMU from Mobile Phone for Robust Robot Following of Human Leader

Robots are sometimes used as "mules" for carrying supplies for workers or soldiers. In these types of scenarios, the robot must autonomously follow the same path as the person who acts as the leader. This type of robot navigation behavior is generally referred to as leader-follower, and when the leader is a person instead of a vehicle, it is more specifically called "human-leader, robot-follower" (HLRF). Existing approaches to HLRF tend to be overly complex to implement. Computer vision offers a cost-effective solution; however, it can be problematic to rely solely on computer vision when multiple leader signatures of similar qualities are detected or when the leader signature is temporarily lost. This paper investigates the effect of fusing inertial measurement unit (IMU) data from the leader's cell phone with the robot's image data to improve the robustness of HLRF. We found that the addition of IMU data from the leader's mobile phone improved the reliability of the robot's following behavior, as well as the fuzzy controller's response in terms of settling time by up to 2.49 seconds.

[1]  Rao R. Bhavani,et al.  Follow me robot using bluetooth-based position estimation , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[2]  Iain Murray,et al.  Human activity recognition using thigh angle derived from single thigh mounted IMU data , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[3]  Marc Hanheide,et al.  Spatial context-aware person-following for a domestic robot , 2008 .

[4]  Pablo Aqueveque,et al.  Android Platform for Realtime Gait Tracking Using Inertial Measurement Units , 2016, European journal of translational myology.

[5]  Hideki Hashimoto,et al.  Human-following mobile robot in a distributed intelligent sensor network , 2004, IEEE Transactions on Industrial Electronics.

[6]  Peter Simon Sapaty,et al.  Military Robotics: Latest Trends and Spatial Grasp Solutions , 2015 .

[7]  Johann Borenstein,et al.  Human leader and robot follower team: correcting leader's position from follower's heading , 2010, Defense + Commercial Sensing.

[8]  Wilhelm Stork,et al.  Smartphone pedestrian navigation by foot-IMU sensor fusion , 2012, 2012 Ubiquitous Positioning, Indoor Navigation, and Location Based Service (UPINLBS).

[9]  Joowan Kim,et al.  Development of a wearable HCI controller through sEMG & IMU sensor fusion , 2016, 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[10]  Aleksandr Sergeyev,et al.  A Lab-Scale Autonomous Haul Truck for Underground Mine Operations: Design and Development , 2016 .

[11]  Ning An,et al.  Human recognition for following robots with a Kinect sensor , 2016, 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[12]  Hong Zhang,et al.  Mobile robot system architecture for people tracking and following applications , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).