Integrating Planning and Control for Efficient Path Planning in the Presence of Environmental Disturbances. .
abstract   bibtex   
Path planning for nonholonomic robots in real-life environments is a challenging problem, as the planner needs to consider the presence of obstacles, the kinematic constraints of the robot as well as the environmental disturbances (like wind and currents). Previously developed search/sampling based algorithms are inefficient as they generally do not take effect of the environmental disturbances into account. On the other hand, optimal control-based techniques are too computationally intensive for real-world implementation. In this paper, we develop a generic path planning algorithm called Control Based A* (CBA*), which integrates search-based planning (on grid) with a path-following controller, taking the motion constraints and external disturbances into account. We also present another algorithm called Dynamic Control Based A* (DCBA*), which improves upon CBA* by allowing the search to look beyond the immediate grid neighborhood and thus makes it more flexible and robust, especially with high resolution grids. We investigate the performance of the new planners in different environments in comparison with the the following approaches, (i) finding a path in the discretized grid and following it with a nonholonomic robot, and (ii) a kinodynamic sampling-based path planner. The results show that our planners perform considerably better than (i) and (ii), especially in difficult situations such as in cluttered spaces or in presence of strong winds/currents.
@inproceeduings {icaps16-69,
  track={Robotics Track},
  title={Integrating Planning and Control for Efficient Path Planning in the Presence of Environmental Disturbances},
  authors={Sandip Aine, Sujit Pb},
  abstract={Path planning for nonholonomic robots in real-life environments is a challenging problem, as the planner needs to consider the presence of obstacles, the kinematic constraints of the robot as well as the environmental disturbances (like wind and currents). Previously developed search/sampling based algorithms are inefficient as they generally do not take effect of the environmental disturbances into account. On the other hand, optimal control-based techniques are too computationally intensive for real-world implementation. In this paper, we develop a generic path planning algorithm called Control Based A* (CBA*), which integrates search-based planning (on grid) with a path-following controller, taking the motion constraints and external disturbances into account. We also present another algorithm called Dynamic Control Based A* (DCBA*), which improves upon CBA* by allowing the search to look beyond the immediate grid neighborhood and thus makes it more flexible and robust, especially with high resolution grids. We investigate the performance of the new planners in different environments in comparison with the the following approaches, (i) finding a path in the discretized grid and following it with a nonholonomic robot, and (ii) a kinodynamic sampling-based path planner. The results show that our planners perform considerably better than (i) and (ii), especially in difficult situations such as in cluttered spaces or in presence of strong winds/currents.},
  keywords={robot motion; path; task and mission planning and execution}
}

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