Self-contained, ultrasonic sensor-based mobility generation for differential drive UGVs

This paper suggests a self-contained, mobility system for velocity controlled, differential drive robots in unknown cluttered environments. The system tackles the use of ultrasonic sensing at the servo-level to generate motion. It can, in real-time, convert the raw measurements from the onboard ultrasonic sensors to a control signal that safely propels the robot to its target. The structure uses a safety-based, subjective representation of the environment to synthesize the control signal. This significantly relaxes the burden of having to accurately localize the components of the environment. Moreover, the structure can decouple the computational burden from the size of the environment representation, hence enabling real-time servo-level navigation. The structure is implemented and thoroughly tested on the X80 mobile robot platform using only one out of the six ultrasonic sensors the robot has (the front sensor). The test consistently demonstrated that the robot can safely reach its target, from the first attempt, along a well-behaved trajectory using well-behaved control signals.

[1]  Martial Hebert,et al.  A complete navigation system for goal acquisition in unknown environments , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[2]  Ahmad A. Masoud,et al.  A harmonic potential field approach for joint planning and control of a rigid, separable nonholonomic, mobile robot , 2013, Robotics Auton. Syst..

[3]  Zhaodan Kong,et al.  A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance , 2010, J. Intell. Robotic Syst..

[4]  Larry Eugene Banta ADVANCED DEAD RECKONING NAVIGATION FOR MOBILE ROBOTS , 1987 .

[5]  S. Sitharama Iyengar,et al.  Robot navigation in unknown terrains: Introductory survey of non-heuristic algorithms , 1993 .

[6]  Ahmad A. Masoud,et al.  Motion planning with gamma-harmonic potential fields , 2012, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[7]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[8]  Luis Moreno,et al.  Navigation of mobile robots: open questions , 2000, Robotica.

[9]  Benjamin Kuipers,et al.  Building safety maps using vision for safe local mobile robot navigation , 2009 .

[10]  Andrea Censi,et al.  Bootstrapping vehicles : a formal approach to unsupervised sensorimotor learning based on invariance , 2013 .

[11]  Lindsay Kleeman,et al.  Mobile-Robot Map Building from an Advanced Sonar Array and Accurate Odometry , 1999, Int. J. Robotics Res..

[12]  Libor Preucil,et al.  Knowledge Acquisition for Mobile Robot Environment Mapping , 1999, DEXA.

[13]  Ahmad A. Masoud,et al.  Motion planning in the presence of directional and obstacle avoidance constraints using nonlinear, anisotropic, harmonic potential fields , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[14]  Wolfram Burgard,et al.  A Tutorial on Graph-Based SLAM , 2010, IEEE Intelligent Transportation Systems Magazine.

[15]  Sebastian Thrun,et al.  Lifelong robot learning , 1993, Robotics Auton. Syst..

[16]  Ahmad A. Masoud,et al.  Evolutionary action maps for navigating a robot in an unknown, multidimensional, stationary environment. II. Implementation and results , 1997, Proceedings of International Conference on Robotics and Automation.

[17]  Sang Jo Lee,et al.  Sonar mapping of a mobile robot considering position uncertainty , 1997 .

[18]  Richard M. Murray,et al.  Motion planning in observations space with learned diffeomorphism models , 2013, 2013 IEEE International Conference on Robotics and Automation.

[19]  Ahmad A. Masoud,et al.  Motion planning in the presence of directional and regional avoidance constraints using nonlinear, anisotropic, harmonic potential fields: a physical metaphor , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[20]  Fumio Miyazaki,et al.  Precise dead-reckoning for mobile robots using multiple optical mouse sensorsx4 , 2005, ICINCO.

[21]  Douglas W Gage,et al.  UGV History 101: A Brief History of Unmanned Ground Vehicle (UGV) Development Efforts , 1995 .

[22]  Beatriz L. Boada,et al.  Traversable Region Modeling for Outdoor Navigation , 2005, J. Intell. Robotic Syst..

[23]  Ahmad A. Masoud A Harmonic Potential Approach for Simultaneous Planning and Control of a Generic UAV Platform , 2012, J. Intell. Robotic Syst..

[24]  G. Campion,et al.  Controllability and State Feedback Stabilizability of Nonholonomic Mechanical Systems , 1991 .

[25]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[26]  David Wooden,et al.  A guide to vision-based map building , 2006, IEEE Robotics & Automation Magazine.

[27]  Alberto Elfes,et al.  Occupancy grids: a probabilistic framework for robot perception and navigation , 1989 .