Autonomous Robots

, Volume 36, Issue 4, pp 383–405 | Cite as

Target following with motion prediction for unmanned surface vehicle operating in cluttered environments

  • Petr Švec
  • Atul Thakur
  • Eric Raboin
  • Brual C. Shah
  • Satyandra K. Gupta


The capability of following a moving target in an environment with obstacles is required as a basic and necessary function for realizing an autonomous unmanned surface vehicle (USV). Many target following scenarios involve a follower and target vehicles that may have different maneuvering capabilities. Moreover, the follower vehicle may not have prior information about the intended motion of the target boat. This paper presents a trajectory planning and tracking approach for following a differentially constrained target vehicle operating in an obstacle field. The developed approach includes a novel algorithm for computing a desired pose and surge speed in the vicinity of the target boat, jointly defined as a motion goal, and tightly integrates it with trajectory planning and tracking components of the entire system. The trajectory planner generates a dynamically feasible, collision-free trajectory to allow the USV to safely reach the computed motion goal. Trajectory planning needs to be sufficiently fast and yet produce dynamically feasible and short trajectories due to the moving target. This required speeding up the planning by searching for trajectories through a hybrid, pose-position state space using a multi-resolution control action set. The search in the velocity space is decoupled from the search for a trajectory in the pose space. Therefore, the underlying trajectory tracking controller computes desired surge speed for each segment of the trajectory and ensures that the USV maintains it. We have carried out simulation as well as experimental studies to demonstrate the effectiveness of the developed approach.


Unmanned surface vehicle (USV) Follow behavior Motion prediction Trajectory planning Trajectory tracking 



This work was supported by the U.S. Office of Naval Research under Grants N00014-10-1-0585 and N00014-12-1-0494, managed by R. Brizzolara. Opinions expressed in this paper are those of the authors and do not necessarily reflect opinions of the sponsors. In addition, we would like to thank Dr. David Akin for allowing us to perform experiments in the Neutral Buoyancy Research Facility (NBRF) at the University of Maryland.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Petr Švec
    • 1
  • Atul Thakur
    • 2
  • Eric Raboin
    • 3
  • Brual C. Shah
    • 4
  • Satyandra K. Gupta
    • 5
  1. 1.Simulation Based System Design Laboratory, Maryland Robotics Center, Department of Mechanical EngineeringUniversity of MarylandCollege ParkUSA
  2. 2.Department of Mechanical EngineeringIndian Institute of Technology PatnaPatliputraIndia
  3. 3.Department of Computer ScienceUniversity of MarylandCollege ParkUSA
  4. 4.Simulation Based System Design Laboratory, Department of Mechanical EngineeringUniversity of MarylandCollege ParkUSA
  5. 5.Simulation Based System Design Laboratory, Maryland Robotics Center, Department of Mechanical Engineering and the Institute for Systems ResearchUniversity of MarylandCollege ParkUSA

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