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Planners

BasePlanner 

BasePlanner(workspace, objective)

Bases: ABC

Interface of a planner.

Parameters:

Name Type Description Default
workspace List[float]

Bounding box [xmin, xmax, ymin, ymax] of the planner's workspace.

 required
objective Callable

The objective function.

 required
Source code in pypolo/planners/base_planner.py 
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def __init__(self, workspace: List[float], objective: Callable) -> None:
    r"""Interface of a planner.

    Args:
        workspace (List[float]): Bounding box [xmin, xmax, ymin, ymax]
            of the planner's workspace.
        objective (Callable): The objective function.

    """
    self.workspace = workspace
    self.objective = objective

plan abstractmethod 

plan(state)

Plan informative waypoint(s) given the robot state.

Parameters:

Name Type Description Default
state ndarray

The robot state.

 required

Returns:

Name Type Description
waypoints ndarray

The resulting informative waypoint(s) of shape (num_waypoints, 2).
Source code in pypolo/planners/base_planner.py 
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@abstractmethod
def plan(self, state: np.ndarray) -> np.ndarray:
    """Plan informative waypoint(s) given the robot state.

    Args:
        state (np.ndarray): The robot state.

    Returns:
        waypoints (np.ndarray): The resulting informative waypoint(s) of
            shape (num_waypoints, 2).

    """
    raise NotImplementedError

MyopicPlanner 

MyopicPlanner(workspace, objective, num_candidates=1000)

Bases: BasePlanner

Interface of a planner.

Parameters:

Name Type Description Default
workspace List[float]

Bounding box [xmin, ymin, xmax, ymax] of the planner's workspace.

 required
objective Callable

The objective function.

 required
num_candidates int

The number of candidates to evaluate.

 1000
Source code in pypolo/planners/myopic_planner.py 
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def __init__(
    self,
    workspace: List[float],
    objective: Callable,
    num_candidates: int = 1000,
) -> None:
    r"""Interface of a planner.

    Args:
        workspace (List[float]): Bounding box [xmin, ymin, xmax, ymax]
            of the planner's workspace.
        objective (Callable): The objective function.
        num_candidates (int): The number of candidates to evaluate.

    """
    assert len(workspace) == 4, "Workspace = [xmin, ymin, xmax, ymax]."
    self.workspace = workspace
    self.objective = objective
    self.num_candidates = num_candidates

plan 

plan(state)

Plan informative waypoint(s) given the robot state.

Parameters:

Name Type Description Default
state ndarray

The robot state.

 required

Returns:

Name Type Description
waypoints ndarray

The resulting informative waypoint(s) of shape (num_waypoints, 2).
Source code in pypolo/planners/myopic_planner.py 
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def plan(self, state: np.ndarray) -> np.ndarray:
    """Plan informative waypoint(s) given the robot state.

    Args:
        state (np.ndarray): The robot state.

    Returns:
        waypoints (np.ndarray): The resulting informative waypoint(s) of
            shape (num_waypoints, 2).

    """
    # Sample candidates.
    candidates = np.random.uniform(low=self.workspace[:2],
                                   high=self.workspace[2:],
                                   size=(self.num_candidates, 2))
    # Evaluate candidates.
    informativeness = self.objective(candidates)
    diffs = candidates - state[:2]
    dists = np.hypot(diffs[:, 0], diffs[:, 1])
    # Normalize informativeness and distance.
    informativeness = ((informativeness - informativeness.min()) /
                       informativeness.ptp())
    dists = (dists - dists.min()) / dists.ptp()
    # Compute scores.
    scores = informativeness - dists
    # Select the candidate with the highest score.
    waypoint = candidates[np.argmax(scores)].reshape(1, 2)
    return waypoint