Fuzzy Obstacle Avoidance for the Mobile System of Service Robots

This study implements Fuzzy logic-based obstacle avoidance and human tracking on an omnidirectional mobile system for service robots. The mobile system could be separated and combined with the robot which can be controlled remotely and switched to go forward and avoid obstacles in an indoor environment automatically. The system is able to track and go to the user according to the user’s position. The omnidirectional wheel was adapted in the power system to perform translating and spinning movements. The translating movement enables the robot to avoid obstacles faster and flexibly in paths. With the spinning movement, the robot can quickly find the direction of the object. Finally, the experiments show that the proposed system has good performance in service environments.

[1]  Hiroshi Mizoguchi,et al.  Building Environmental Maps of Human Activity for a Mobile Service Robot at the "Miraikan" Museum , 2013, FSR.

[2]  Yuta Sugiura,et al.  RoboJockey: Designing an Entertainment Experience with Robots , 2016, IEEE Computer Graphics and Applications.

[3]  Luc Steels,et al.  The artificial life route to artificial intelligence : building embodied , 1995 .

[4]  Aimé Lay-Ekuakille,et al.  Advanced acoustic sensing system on a mobile robot: design, construction and measurements , 2018, IEEE Instrumentation & Measurement Magazine.

[5]  Yuichi Motai,et al.  Human Behavior-Based Target Tracking With an Omni-Directional Thermal Camera , 2019, IEEE Transactions on Cognitive and Developmental Systems.

[6]  Richard Durbin,et al.  An analogue approach to the travelling salesman problem using an elastic net method , 1987, Nature.

[7]  Dayal R. Parhi,et al.  Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review , 2017, ICRA 2017.

[8]  Chokri Rekik,et al.  Robot Path Planning with Avoiding Obstacles in Known Environment Using Free Segments and Turning Points Algorithm , 2018, Mathematical Problems in Engineering.

[9]  Lin Wu,et al.  A Human-Tracking Robot Using Ultra Wideband Technology , 2018, IEEE Access.

[10]  Mei-Yung Chen,et al.  Design of Path Planning and Obstacle Avoidance for a Wheeled Mobile Robot , 2016, Int. J. Fuzzy Syst..

[11]  Lei Zhang,et al.  Mobile Robot Moving Target Detection and Tracking System , 2017 .

[12]  Bing-Fei Wu,et al.  A New Criterion of Human Comfort Assessment for Wheelchair Robots by Q-Learning Based Accompanist Tracking Fuzzy Controller , 2016, Int. J. Fuzzy Syst..

[13]  Wen-June Wang,et al.  Fuzzy Control Strategy for a Hexapod Robot Walking on an Incline , 2017, Int. J. Fuzzy Syst..

[14]  Ching-Yi Chen,et al.  Real-Time Self-Localization of a Mobile Robot by Vision and Motion System , 2015, 2015 International Conference on Fuzzy Theory and Its Applications (iFUZZY).

[15]  Hsu-Chih Huang,et al.  Backstepping Holonomic Tracking Control of Wheeled Robots Using an Evolutionary Fuzzy System with Qualified Ant Colony Optimization , 2016, Int. J. Fuzzy Syst..