Trajectory Planners

I. Risk-aware Trajectory Planner


The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. In order to address this challenge, we have developed a lattice-based 5D trajectory planner for USVs. The planner estimates, collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. The planner also incorporates avoidance behaviors of the vessels into the search for a dynamically feasible trajectory to minimize collision risk. In order to be computationally efficient, it dynamically scales the control action primitives of a trajectory based on the distribution and concentration of civilian vessels while preserving the dynamical feasibility of the primitives. We present a novel congestion metric to compare the complexity of different scenarios when evaluating the performance of the planner. Our results demonstrate that the basic version of the risk and contingency-aware planner (RCAP) significantly decreases the number of collisions compared to a baseline,\ Velocity Obstacles (VO) based planner, especially in complex scenarios with a high number of civilian vessels. The adaptive version of the planner (A-RCAP) improves the computational performance of RCAP by 500%. This leads to a high replanning rate, which allows shorter traversal distances and smaller arrival times, while ensuring comparable incidence of collisions.

Published Work:

  • B. C. Shah, P. Švec, I.R. Bertaska, W. Klinger, A. J. Sinisterra, K. von Ellenrieder, M. Dhanak, and S.K. Gupta. Resolution-Adaptive Risk-Aware Trajectory Planning for Surface Vehicles Operating in Congested Civilian Traffic. Autonomous Robots. PDF
  • B. C. Shah, P. Švec, I. R. Bertaska, W. Klinger, A. J. Sinisterra, K. von Ellenrieder, M. Dhanak, and S. K. Gupta. Trajectory Planning with Adaptive Control Primitives for Autonomous Surface Vehicles Operating in Congested Civilian Traffic.  IEEE/RSJ  International Conference on Intelligent Robots and Systems (IROS ’14), Chicago, IL, USA, September 14-18, 2014. PDF

II. Trajectory Planner for Dynamic Target Following


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.

Published Work:

  • P. Švec, A. Thakur, E. Raboin, B.C. Shah and S.K. Gupta. Target Following with Motion Prediction for Unmanned Surface Vehicle Operating in Cluttered Environments.Autonomous Robots, 36(4): 383-405, 2014. PDF
  • P. Švec, A. Thakur, B.C. Shah, and S.K. Gupta. USV trajectory planning for time varying motion goals in an environment with obstacles. ASME Mechanism and Robotics Conference, Chicago, IL, August 2012. PDF