Collaborative astar (#1247)

* consolidate Node definition

* add base class for single agent planner

* add base class for single agent planner

* its working

* use single agent plotting util

* cleanup, bug fix, add some results to docs

* remove seeding from sta* - it happens in Node

* remove stale todo

* rename CA* and speed up plotting

* paper

* proper paper (ofc its csail)

* some cleanup

* update docs

* add unit test

* add logic for saving animation as gif

* address github bot

* Revert "add logic for saving animation as gif"

This reverts commit 6391677.

* fix tests

* docs lint

* add gifs

* copilot review

* appease mypy

Author: SchwartzCode

PathPlanning/TimeBasedPathPlanning/BaseClasses.py

@@ -0,0 +1,49 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath
+import random
+import numpy.random as numpy_random
+
+# Seed randomness for reproducibility
+RANDOM_SEED = 50
+random.seed(RANDOM_SEED)
+numpy_random.seed(RANDOM_SEED)
+
+class SingleAgentPlanner(ABC):
+    """
+    Base class for single agent planners
+    """
+    
+    @staticmethod
+    @abstractmethod
+    def plan(grid: Grid, start: Position, goal: Position, verbose: bool = False) -> NodePath:
+        pass
+
+@dataclass
+class StartAndGoal:
+    # Index of this agent
+    index: int
+    # Start position of the robot
+    start: Position
+    # Goal position of the robot
+    goal: Position
+
+    def distance_start_to_goal(self) -> float:
+        return pow(self.goal.x - self.start.x, 2) + pow(self.goal.y - self.start.y, 2)
+
+class MultiAgentPlanner(ABC):
+    """
+    Base class for multi-agent planners
+    """       
+    
+    @staticmethod
+    @abstractmethod
+    def plan(grid: Grid, start_and_goal_positions: list[StartAndGoal], single_agent_planner_class: SingleAgentPlanner, verbose: bool = False) -> tuple[list[StartAndGoal], list[NodePath]]:
+        """
+        Plan for all agents. Returned paths are in order corresponding to the returned list of `StartAndGoal` objects
+        """
+        pass

PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py

@@ -7,31 +7,7 @@
 import matplotlib.pyplot as plt
 from enum import Enum
 from dataclasses import dataclass
-
-@dataclass(order=True)
-class Position:
-    x: int
-    y: int
-
-    def as_ndarray(self) -> np.ndarray:
-        return np.array([self.x, self.y])
-
-    def __add__(self, other):
-        if isinstance(other, Position):
-            return Position(self.x + other.x, self.y + other.y)
-        raise NotImplementedError(
-            f"Addition not supported for Position and {type(other)}"
-        )
-
-    def __sub__(self, other):
-        if isinstance(other, Position):
-            return Position(self.x - other.x, self.y - other.y)
-        raise NotImplementedError(
-            f"Subtraction not supported for Position and {type(other)}"
-        )
-
-    def __hash__(self):
-        return hash((self.x, self.y))
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath, Position
 
 @dataclass
 class Interval:
@@ -43,6 +19,8 @@ class ObstacleArrangement(Enum):
     RANDOM = 0
     # Obstacles start in a line in y at center of grid and move side-to-side in x
     ARRANGEMENT1 = 1
+    # Static obstacle arrangement
+    NARROW_CORRIDOR = 2
 
 """
 Generates a 2d numpy array with lists for elements.
@@ -87,6 +65,8 @@ def __init__(
             self.obstacle_paths = self.generate_dynamic_obstacles(num_obstacles)
         elif obstacle_arrangement == ObstacleArrangement.ARRANGEMENT1:
             self.obstacle_paths = self.obstacle_arrangement_1(num_obstacles)
+        elif obstacle_arrangement == ObstacleArrangement.NARROW_CORRIDOR:
+            self.obstacle_paths = self.generate_narrow_corridor_obstacles(num_obstacles)
 
         for i, path in enumerate(self.obstacle_paths):
             obs_idx = i + 1  # avoid using 0 - that indicates free space in the grid
@@ -184,6 +164,26 @@ def obstacle_arrangement_1(self, obs_count: int) -> list[list[Position]]:
             obstacle_paths.append(path)
 
         return obstacle_paths
+    
+    def generate_narrow_corridor_obstacles(self, obs_count: int) -> list[list[Position]]:
+        obstacle_paths = []
+
+        for y in range(0, self.grid_size[1]):
+            if y > obs_count:
+                break
+
+            if y == self.grid_size[1] // 2:
+                # Skip the middle row
+                continue
+
+            obstacle_path = []
+            x = self.grid_size[0] // 2 # middle of the grid
+            for t in range(0, self.time_limit - 1):
+                obstacle_path.append(Position(x, y))
+
+            obstacle_paths.append(obstacle_path)
+
+        return obstacle_paths
 
     """
     Check if the given position is valid at time t
@@ -196,11 +196,11 @@ def obstacle_arrangement_1(self, obs_count: int) -> list[list[Position]]:
         bool: True if position/time combination is valid, False otherwise
     """
     def valid_position(self, position: Position, t: int) -> bool:
-        # Check if new position is in grid
+        # Check if position is in grid
         if not self.inside_grid_bounds(position):
             return False
 
-        # Check if new position is not occupied at time t
+        # Check if position is not occupied at time t
         return self.reservation_matrix[position.x, position.y, t] == 0
 
     """
@@ -289,9 +289,48 @@ def get_safe_intervals_at_cell(self, cell: Position) -> list[Interval]:
         # both the time step when it is entering the cell,  and the time step when it is leaving the cell.
         intervals = [interval for interval in intervals if interval.start_time != interval.end_time]
         return intervals
+    
+    """
+    Reserve an agent's path in the grid. Raises an exception if the agent's index is 0, or if a position is
+    already reserved by a different agent.
+    """
+    def reserve_path(self, node_path: NodePath, agent_index: int):
+        if agent_index == 0:
+            raise Exception("Agent index cannot be 0")
+        
+        for i, node in enumerate(node_path.path):
+            reservation_finish_time = node.time + 1
+            if i < len(node_path.path) - 1:
+                reservation_finish_time = node_path.path[i + 1].time
 
-show_animation = True
+            self.reserve_position(node.position, agent_index, Interval(node.time, reservation_finish_time))
+
+    """
+    Reserve a position for the provided agent during the provided time interval.
+    Raises an exception if the agent's index is 0, or if the position is already reserved by a different agent during the interval.
+    """
+    def reserve_position(self, position: Position, agent_index: int, interval: Interval):
+        if agent_index == 0:
+            raise Exception("Agent index cannot be 0")
+
+        for t in range(interval.start_time, interval.end_time + 1):
+            current_reserver = self.reservation_matrix[position.x, position.y, t]
+            if current_reserver not in [0, agent_index]:
+                raise Exception(
+                    f"Agent {agent_index} tried to reserve a position already reserved by another agent: {position} at time {t}, reserved by {current_reserver}"
+                )
+            self.reservation_matrix[position.x, position.y, t] = agent_index
+
+    """
+    Clears the initial reservation for an agent by clearing reservations at its start position with its index for
+    from time 0 to the time limit.
+    """
+    def clear_initial_reservation(self, position: Position, agent_index: int):
+        for t in range(self.time_limit):
+            if self.reservation_matrix[position.x, position.y, t] == agent_index:
+                self.reservation_matrix[position.x, position.y, t] = 0
 
+show_animation = True
 
 def main():
     grid = Grid(

PathPlanning/TimeBasedPathPlanning/Node.py

@@ -0,0 +1,99 @@
+from dataclasses import dataclass
+from functools import total_ordering
+import numpy as np
+from typing import Sequence
+
+@dataclass(order=True)
+class Position:
+    x: int
+    y: int
+
+    def as_ndarray(self) -> np.ndarray:
+        return np.array([self.x, self.y])
+
+    def __add__(self, other):
+        if isinstance(other, Position):
+            return Position(self.x + other.x, self.y + other.y)
+        raise NotImplementedError(
+            f"Addition not supported for Position and {type(other)}"
+        )
+
+    def __sub__(self, other):
+        if isinstance(other, Position):
+            return Position(self.x - other.x, self.y - other.y)
+        raise NotImplementedError(
+            f"Subtraction not supported for Position and {type(other)}"
+        )
+
+    def __hash__(self):
+        return hash((self.x, self.y))
+
+@dataclass()
+# Note: Total_ordering is used instead of adding `order=True` to the @dataclass decorator because
+#     this class needs to override the __lt__ and __eq__ methods to ignore parent_index. Parent
+#     index is just used to track the path found by the algorithm, and has no effect on the quality
+#     of a node.
+@total_ordering
+class Node:
+    position: Position
+    time: int
+    heuristic: int
+    parent_index: int
+
+    """
+    This is what is used to drive node expansion. The node with the lowest value is expanded next.
+    This comparison prioritizes the node with the lowest cost-to-come (self.time) + cost-to-go (self.heuristic)
+    """
+    def __lt__(self, other: object):
+        if not isinstance(other, Node):
+            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
+        return (self.time + self.heuristic) < (other.time + other.heuristic)
+
+    """
+    Note: cost and heuristic are not included in eq or hash, since they will always be the same
+          for a given (position, time) pair. Including either cost or heuristic would be redundant.
+    """
+    def __eq__(self, other: object):
+        if not isinstance(other, Node):
+            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
+        return self.position == other.position and self.time == other.time
+
+    def __hash__(self):
+        return hash((self.position, self.time))
+
+class NodePath:
+    path: Sequence[Node]
+    positions_at_time: dict[int, Position]
+    # Number of nodes expanded while finding this path
+    expanded_node_count: int
+
+    def __init__(self, path: Sequence[Node], expanded_node_count: int):
+        self.path = path
+        self.expanded_node_count = expanded_node_count
+
+        self.positions_at_time = {}
+        for i, node in enumerate(path):
+            reservation_finish_time = node.time + 1
+            if i < len(path) - 1:
+                reservation_finish_time = path[i + 1].time
+
+            for t in range(node.time, reservation_finish_time):
+                self.positions_at_time[t] = node.position
+
+    """
+    Get the position of the path at a given time
+    """
+    def get_position(self, time: int) -> Position | None:
+        return self.positions_at_time.get(time)
+
+    """
+    Time stamp of the last node in the path
+    """
+    def goal_reached_time(self) -> int:
+        return self.path[-1].time
+
+    def __repr__(self):
+        repr_string = ""
+        for i, node in enumerate(self.path):
+            repr_string += f"{i}: {node}\n"
+        return repr_string