Persistent Autonomous Operation

A variety of robotic missions in space, air, and water or on ground depend on precision and persistence. Persistent operation requires addressing both science and technology challenges to effectively respond to energy needs in the presence of dynamic conditions and environmental uncertainty. Our approach includes task and energy routing scheduling, efficient path planning and coordination, and low-infrastructure platforms. The goal is to provide practical solutions by lowering deployment and operating costs, while also increasing efficiency, endurance and persistence during search/rescue/recovery/discovery missions. The research efforts are supported by NSF and ONR.

In more technical terms, this project creates a mission planning architecture that ensures robustness of long-term missions through smart energy maintenance. The algorithm simultaneously generates trajectories for operating robots and schedules energy cycling by 1) placing static charging stations or 2) finding the rendezvous positions of mobile chargers. The model meets overall mission specifications, the energy consumption needs, situational conditions, and environmental variables. It will provide a structure to minimize the total mission cost, and the trade-off between energy spent on missions and number of operating robots.

To validate the algorithms, lab-size and real-size experiments are carried out. Lab-size experiments consider extending aerial missions with static charging pads or ground mobile chargers. Real-size experiments include both aerial and marine missions. For a marine experimental test-bed, a novel prototype for an underwater mobile charger capable of autonomous docking and wireless energy transfer is being developed. The outcome will provide a scalable roadmap for a mobile underwater power delivery system and persistent operation.

Related Publications:

  1. Li B., Moridian B., and Mahmoudian N., “Multi-Robot Persistent Area Coverage Mission Planning using Static Charging Stations”, IEEE Transactions on Robotics (submitted).
  2. Moridian B., Mahmoudian N., Weaver W., and Robinett R., “Post-Disaster Electric Power Recovery Using Autonomous Vehicles”, IEEE Transactions on Automation Science and Engineering (T-ASE), vol. 14, no. 1, pp. 62-72, 2017.
  3. Page B., Ziaee Fard S., Pinar A., and Mahmoudian N., “Highly Maneuverable Low-Cost Autonomous Underwater Glider: Design and Development”, IEEE Robotics and Automation Letters (RA-L), vol. 2, no. 1, pp. 344-349, 2017.
  4. Li B., Moridian B., and Mahmoudian N., “Underwater Multi-robot Persistent Area Coverage Mission Planning”, MTS/ IEEE Ocean’s 16, Monterey, CA, September 19-23, 2016.
  5. Ziaee Fard S., Page B., Pinar A., and Mahmoudian N., “Effective Turning Motion Control of Internally Actuated Autonomous Underwater Vehicles”, Journal of Intelligent & Robotic Systems (accepted).

More research...


Nina Mahmoudian
Associate Professor of Mechanical Engineering
Purdue University
585 Purdue Mall
West Lafayette, IN 47907