diff --git a/README.md b/README.md
index 7ffc9670..270ac14c 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,7 @@ Python sample codes and documents about Autonomous vehicle control algorithm. Th
         * [A*](#a)
         * [Bidirectional A*](#bidirectional-a)
         * [Hybrid A*](#hybrid-a)
+        * [D*](#d)
         * [Dijkstra](#dijkstra)
         * [RRT](#rrt)
         * [Informed RRT*](#informed-rrt)
@@ -121,6 +122,9 @@ Planning
 #### Hybrid A*
 Planning  
 ![](src/simulations/path_planning/astar_hybrid_path_planning/astar_hybrid_search.gif)  
+#### D*
+Planning with dynamic obstacle replanning  
+![](src/simulations/path_planning/dstar_path_planning/dstar_search.gif)  
 #### Dijkstra
 Planning(Reduce frames by sampling every nth node to prevent memory exhaustion)  
 ![](src/simulations/path_planning/dijkstra_path_planning/dijkstra_search.gif)  
diff --git a/src/components/plan/dstar/dstar_path_planner.py b/src/components/plan/dstar/dstar_path_planner.py
new file mode 100644
index 00000000..da4461df
--- /dev/null
+++ b/src/components/plan/dstar/dstar_path_planner.py
@@ -0,0 +1,556 @@
+"""
+dstar_path_planner.py
+
+Implementation of the original D* (Dynamic A*) algorithm (Stentz, 1994)
+with an optional focused heuristic (Focused D*).
+
+D* searches backward from the goal and maintains cost-to-goal values for
+every cell. When new obstacles are discovered on the current path, the
+algorithm efficiently re-propagates costs only in the affected region
+instead of re-planning from scratch.
+
+By default, the priority queue is biased with a Euclidean heuristic toward
+the robot (start) position, making the initial search informed (A*-like).
+Set ``heuristic_weight=0`` to revert to an uninformed Dijkstra-style flood.
+
+Key concepts:
+    - Tag states: NEW (never visited), OPEN (on priority queue), CLOSED (processed).
+    - Each cell stores h(X) = current cost-to-goal estimate.
+    - The priority queue is keyed by k(X) + heuristic(X, robot), which
+      focuses expansion toward the robot's position.
+    - LOWER states propagate optimal costs outward.
+    - RAISE states detect cost increases and attempt to find cheaper back-pointers.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import heapq
+import matplotlib.animation as anm
+import sys
+import json
+from pathlib import Path
+from matplotlib.colors import ListedColormap
+
+abs_dir_path = str(Path(__file__).absolute().parent)
+relative_path = "/../../../components/"
+relative_simulations = "/../../../simulations/"
+
+sys.path.append(abs_dir_path + relative_path + "visualization")
+sys.path.append(abs_dir_path + relative_path + "state")
+sys.path.append(abs_dir_path + relative_path + "obstacle")
+sys.path.append(abs_dir_path + relative_path + "mapping/grid")
+
+from min_max import MinMax
+
+
+class _Tag:
+    """Cell tag constants following Stentz's original D* paper."""
+    NEW = 0
+    OPEN = 1
+    CLOSED = 2
+
+
+class DStarPathPlanner:
+    """
+    Original D* path planner operating on a 2-D occupancy grid.
+
+    The planner performs an initial backward search from the goal to the start.
+    After the robot begins following the path, callers can inject new obstacles
+    via ``update_obstacles`` which triggers efficient incremental replanning.
+
+    Parameters
+    ----------
+    start : tuple[int, int]
+        Start position in world coordinates (x, y).
+    goal : tuple[int, int]
+        Goal position in world coordinates (x, y).
+    map_file : str
+        Path to a ``.json``, ``.npy``, or ``.png`` grid file.
+    x_lim : MinMax
+        X-axis world limits.
+    y_lim : MinMax
+        Y-axis world limits.
+    path_filename : str, optional
+        If provided, the sparse path is saved here as JSON.
+    gif_name : str, optional
+        If provided, the search animation is saved here as a GIF.
+    heuristic_weight : float, optional
+        Weight for the Euclidean heuristic that biases the search toward
+        the robot.  ``1.0`` (default) gives an A*-like informed search.
+        ``0.0`` disables the heuristic (Dijkstra-style uniform flood).
+        Values ``> 1.0`` are more aggressive but may over-expand during
+        replanning.
+    """
+
+    # 8-connected neighbourhood (dx, dy, cost)
+    _NEIGHBOURS = [
+        (-1, 0, 1.0), (1, 0, 1.0), (0, -1, 1.0), (0, 1, 1.0),
+        (-1, -1, 1.414), (1, -1, 1.414), (-1, 1, 1.414), (1, 1, 1.414),
+    ]
+
+    INF = float('inf')
+
+    def __init__(self, start, goal, map_file, x_lim=None, y_lim=None,
+                 path_filename=None, gif_name=None, heuristic_weight=1.0):
+        self.start_world = start
+        self.goal_world = goal
+        self.heuristic_weight = heuristic_weight
+
+        self.grid = self._load_grid(map_file)
+        x_min, x_max = x_lim.min_value(), x_lim.max_value()
+        y_min, y_max = y_lim.min_value(), y_lim.max_value()
+        self.resolution = (x_max - x_min) / self.grid.shape[1]
+        self.x_range = np.arange(x_min, x_max, self.resolution)
+        self.y_range = np.arange(y_min, y_max, self.resolution)
+
+        self.rows, self.cols = self.grid.shape
+
+        # Convert world -> grid indices
+        self.start_idx = self._world_to_grid(start)
+        self.goal_idx = self._world_to_grid(goal)
+
+        # The heuristic target: the search runs backward from goal, so the
+        # heuristic biases expansion toward the robot (start) position.
+        self._focus_target = self.start_idx
+
+        # D* bookkeeping ---------------------------------------------------
+        # tag[r,c]: NEW / OPEN / CLOSED
+        self.tag = np.full((self.rows, self.cols), _Tag.NEW, dtype=np.int8)
+        # h[r,c]: current cost-to-goal
+        self.h = np.full((self.rows, self.cols), self.INF)
+        # k[r,c]: key (priority) of the cell when on OPEN
+        self.k = np.full((self.rows, self.cols), self.INF)
+        # parent back-pointers (toward the goal)
+        self.parent = {}  # (r,c) -> (pr,pc) or None
+
+        # Priority queue: entries are (f_value, counter, (row, col))
+        # where f = k + heuristic_weight * euclidean(node, focus_target)
+        self._open_list = []
+        self._counter = 0
+
+        # Logging for visualisation
+        self.explored_nodes = []
+        self.replan_explored_nodes = []
+
+        self.path = []
+        self.path_filename = path_filename
+
+        # ---------- initial search (goal -> start) ----------
+        self._insert(self.goal_idx, 0.0)
+        self.h[self.goal_idx] = 0.0
+        self.parent[self.goal_idx] = None
+
+        print(f"D* initial search  Start(grid): {self.start_idx}, Goal(grid): {self.goal_idx}"
+              f"  heuristic_weight={self.heuristic_weight}")
+        self._compute_shortest_path()
+
+        # Extract the initial path
+        self.path = self._extract_path()
+        if path_filename and self.path:
+            sparse = self._make_sparse_path(self.path)
+            self._save_path(sparse, path_filename)
+
+        self.visualize_search(gif_name)
+
+    # ------------------------------------------------------------------
+    #  Core D* operations (faithful to Stentz 1994)
+    # ------------------------------------------------------------------
+
+    def _heuristic(self, node):
+        """Euclidean distance from *node* to the focus target (the robot).
+
+        The D* search runs backward from the goal, so biasing toward the
+        robot focuses the search and avoids flooding the entire grid.
+        Returns 0 when ``heuristic_weight`` is 0 (Dijkstra mode).
+        """
+        if self.heuristic_weight == 0:
+            return 0.0
+        r, c = node
+        tr, tc = self._focus_target
+        return self.heuristic_weight * ((r - tr) ** 2 + (c - tc) ** 2) ** 0.5
+
+    def _insert(self, node, h_new):
+        """Insert or re-insert *node* into the OPEN list with updated cost.
+
+        The heap key is ``k + heuristic(node)`` so that expansion is biased
+        toward the robot.  The ``k`` value stored on the node remains
+        un-biased so that the RAISE / LOWER logic (which compares ``k_old``
+        against ``h``) is unaffected.
+        """
+        r, c = node
+        if self.tag[r, c] == _Tag.NEW:
+            k_val = h_new
+        elif self.tag[r, c] == _Tag.OPEN:
+            k_val = min(self.k[r, c], h_new)
+        else:  # CLOSED
+            k_val = min(self.h[r, c], h_new)
+        self.k[r, c] = k_val
+        self.h[r, c] = h_new
+        self.tag[r, c] = _Tag.OPEN
+        self._counter += 1
+        f_val = k_val + self._heuristic(node)
+        heapq.heappush(self._open_list, (f_val, self._counter, (r, c)))
+
+    def _get_kmin(self):
+        """Return the minimum key on the OPEN list (or -1 if empty)."""
+        while self._open_list:
+            k_val, _, node = self._open_list[0]
+            r, c = node
+            if self.tag[r, c] == _Tag.OPEN:
+                return k_val
+            heapq.heappop(self._open_list)  # stale entry
+        return -1
+
+    def _cost(self, a, b):
+        """
+        Arc cost c(a, b). Returns INF if *b* is an obstacle.
+        a, b are (row, col) tuples.
+        """
+        br, bc = b
+        if self.grid[br, bc] != 0:
+            return self.INF
+        ar, ac = a
+        if self.grid[ar, ac] != 0:
+            return self.INF
+        # Euclidean distance between adjacent cells
+        dr = abs(ar - br)
+        dc = abs(ac - bc)
+        if dr + dc == 2:
+            return 1.414
+        return 1.0
+
+    def _neighbours(self, node):
+        """Yield valid in-bounds neighbour (row, col) tuples."""
+        r, c = node
+        for dx, dy, _ in self._NEIGHBOURS:
+            nr, nc = r + dy, c + dx
+            if 0 <= nr < self.rows and 0 <= nc < self.cols:
+                yield (nr, nc)
+
+    def _process_state(self):
+        """
+        Pop the minimum-key OPEN state and propagate costs.
+
+        Returns the k value of the processed state, or -1 if OPEN is empty.
+        This follows Stentz's RAISE / LOWER propagation logic.
+
+        Note: the heap stores ``f = k + heuristic`` for ordering, but the
+        RAISE / LOWER logic uses the un-biased ``k`` value stored on the cell.
+        """
+        # Pop a valid OPEN entry
+        while self._open_list:
+            _f_val, _, node = heapq.heappop(self._open_list)
+            r, c = node
+            if self.tag[r, c] == _Tag.OPEN:
+                break
+        else:
+            return -1  # OPEN list exhausted
+
+        # Recover the real k (without heuristic) for RAISE/LOWER comparison
+        k_old = self.k[r, c]
+
+        self.tag[r, c] = _Tag.CLOSED
+        self.explored_nodes.append((c, r))  # (grid_x, grid_y) for vis
+
+        X = (r, c)
+        h_X = self.h[r, c]
+
+        # ---- CASE 1: RAISE (k_old < h(X))  ----
+        # X's cost has increased.  Check if any neighbour Y can lower X.
+        if k_old < h_X:
+            for Y in self._neighbours(X):
+                yr, yc = Y
+                if self.tag[yr, yc] != _Tag.NEW and \
+                   self.h[yr, yc] <= k_old and \
+                   h_X > self.h[yr, yc] + self._cost(Y, X):
+                    self.parent[X] = Y
+                    self.h[r, c] = self.h[yr, yc] + self._cost(Y, X)
+                    h_X = self.h[r, c]
+
+        # ---- CASE 2: LOWER (k_old == h(X))  ----
+        # X's cost is optimal.  Propagate to neighbours.
+        if k_old == h_X:
+            for Y in self._neighbours(X):
+                yr, yc = Y
+                c_YX = self._cost(X, Y)
+                if self.tag[yr, yc] == _Tag.NEW or \
+                   (self.parent.get(Y) == X and self.h[yr, yc] != h_X + c_YX) or \
+                   (self.parent.get(Y) != X and self.h[yr, yc] > h_X + c_YX):
+                    self.parent[Y] = X
+                    self._insert(Y, h_X + c_YX)
+        else:
+            # ---- CASE 3: continued RAISE (k_old < h(X) still) ----
+            for Y in self._neighbours(X):
+                yr, yc = Y
+                c_YX = self._cost(X, Y)
+
+                if self.tag[yr, yc] == _Tag.NEW or \
+                   (self.parent.get(Y) == X and self.h[yr, yc] != h_X + c_YX):
+                    self.parent[Y] = X
+                    self._insert(Y, h_X + c_YX)
+                elif self.parent.get(Y) != X and self.h[yr, yc] > h_X + c_YX:
+                    # X can improve Y, but re-insert X first so it will be
+                    # processed as LOWER next time.
+                    self._insert(X, h_X)
+                elif self.parent.get(Y) != X and \
+                     h_X > self.h[yr, yc] + self._cost(Y, X) and \
+                     self.tag[yr, yc] == _Tag.CLOSED and \
+                     self.h[yr, yc] > k_old:
+                    # Y can potentially improve X, re-open Y.
+                    self._insert(Y, self.h[yr, yc])
+
+        return k_old
+
+    def _compute_shortest_path(self):
+        """Run process_state until the start is CLOSED or OPEN is empty."""
+        sr, sc = self.start_idx
+        while self.tag[sr, sc] != _Tag.CLOSED:
+            if self._process_state() == -1:
+                print("D*: OPEN list exhausted – no path found.")
+                break
+
+    # ------------------------------------------------------------------
+    #  Dynamic replanning
+    # ------------------------------------------------------------------
+
+    def update_obstacles(self, obstacle_cells):
+        """
+        Notify the planner that new obstacles have appeared.
+
+        Parameters
+        ----------
+        obstacle_cells : list[tuple[int, int]]
+            Grid cells ``(row, col)`` that are now blocked.
+
+        After calling this, call ``replan()`` to propagate costs.
+        """
+        for r, c in obstacle_cells:
+            if not self._in_bounds(r, c):
+                continue
+            if self.grid[r, c] != 0:
+                continue  # already an obstacle
+
+            old_h = self.h[r, c]
+            self.grid[r, c] = 1  # mark as obstacle
+
+            # Re-insert the blocked cell with INF cost.
+            # Its k will preserve the old value so it enters as a RAISE.
+            self._insert((r, c), self.INF)
+
+            # Any neighbour whose back-pointer goes through (r,c) is now
+            # broken. Raise them as well.
+            for Y in self._neighbours((r, c)):
+                yr, yc = Y
+                if self.parent.get(Y) == (r, c):
+                    self._insert(Y, self.INF)
+
+    def replan(self, current_pos_idx=None):
+        """
+        Run D* incremental repair until the robot's position is resolved.
+
+        After ``update_obstacles`` places cells on the OPEN list, this method
+        drains the OPEN list so that all RAISE / LOWER propagation completes.
+        Then the parent chains are fully repaired and a new path can be
+        extracted.
+
+        Parameters
+        ----------
+        current_pos_idx : tuple[int, int], optional
+            Current robot position in grid indices (row, col).
+            Defaults to ``self.start_idx``.
+
+        Returns
+        -------
+        list[tuple[int, int]]
+            Updated path from the current position to the goal in grid indices.
+        """
+        if current_pos_idx is None:
+            current_pos_idx = self.start_idx
+
+        # Refocus the heuristic toward the robot's current position so
+        # that replan expansion is biased toward where the robot is now.
+        self._focus_target = current_pos_idx
+
+        prev_len = len(self.explored_nodes)
+
+        # Drain the OPEN list so all cost changes are propagated.
+        while True:
+            k_min = self._process_state()
+            if k_min == -1:
+                break
+
+        self.replan_explored_nodes = self.explored_nodes[prev_len:]
+        self.path = self._extract_path(start_idx=current_pos_idx)
+        return self.path
+
+    # ------------------------------------------------------------------
+    #  Helpers
+    # ------------------------------------------------------------------
+
+    def _in_bounds(self, r, c):
+        return 0 <= r < self.rows and 0 <= c < self.cols
+
+    def _world_to_grid(self, world_xy):
+        """Return ``(row, col)`` grid indices for a world ``(x, y)`` point."""
+        x, y = world_xy
+        col = int((x - self.x_range[0]) / self.resolution)
+        row = int((y - self.y_range[0]) / self.resolution)
+        return (row, col)
+
+    def _grid_to_world(self, grid_rc):
+        """Return ``(world_x, world_y)`` for a ``(row, col)`` grid index."""
+        r, c = grid_rc
+        wx = self.x_range[0] + c * self.resolution
+        wy = self.y_range[0] + r * self.resolution
+        return (wx, wy)
+
+    def _extract_path(self, start_idx=None):
+        """Follow parent pointers from *start_idx* to the goal."""
+        if start_idx is None:
+            start_idx = self.start_idx
+        sr, sc = start_idx
+        gr, gc = self.goal_idx
+
+        if self.h[sr, sc] >= self.INF:
+            print("D*: No path exists from start to goal.")
+            return []
+
+        path = [(sc, sr)]  # store as (grid_x, grid_y)
+        current = (sr, sc)
+        visited = set()
+        while current != (gr, gc):
+            if current in visited:
+                print("D*: Cycle detected during path extraction.")
+                return []
+            visited.add(current)
+            parent = self.parent.get(current)
+            if parent is None:
+                print("D*: Broken parent chain.")
+                return []
+            r, c = parent
+            # If the parent is an obstacle, the chain is invalid
+            if self.grid[r, c] != 0 and (r, c) != (gr, gc):
+                print("D*: Parent chain goes through obstacle.")
+                return []
+            current = (r, c)
+            path.append((c, r))
+        return path
+
+    def _make_sparse_path(self, path, num_points=20):
+        if len(path) <= num_points:
+            return [self._grid_to_world((r, c)) for c, r in path]
+        indices = np.linspace(0, len(path) - 1, num_points, dtype=int)
+        # path entries are (grid_x, grid_y), convert to world
+        return [self._grid_to_world((path[i][1], path[i][0])) for i in indices]
+
+    def _save_path(self, path, filename):
+        Path(filename).parent.mkdir(parents=True, exist_ok=True)
+        with open(filename, "w") as f:
+            json.dump(path, f)
+
+    @staticmethod
+    def _load_grid(file_path):
+        ext = Path(file_path).suffix
+        if ext == '.npy':
+            return np.load(file_path)
+        if ext == '.png':
+            grid = plt.imread(file_path)
+            if grid.ndim == 3:
+                grid = np.mean(grid, axis=2)
+            return (grid > 0.5).astype(float)
+        if ext == '.json':
+            with open(file_path, 'r') as f:
+                return np.array(json.load(f))
+        raise ValueError(f"Unsupported grid format: {ext}")
+
+    # ------------------------------------------------------------------
+    #  Visualisation (matches the pattern used by A* / Dijkstra)
+    # ------------------------------------------------------------------
+
+    def visualize_search(self, gif_name=None):
+        """Animate the initial search and, if present, the replan phase.
+
+        When *gif_name* is ``None`` the method is a no-op so that callers
+        (e.g. simulations that drive their own animation) can skip it.
+        """
+        print(f"D* explored {len(self.explored_nodes)} nodes during initial search.")
+        if gif_name is None or not self.explored_nodes:
+            return
+
+        max_frames = 2000
+        step = max(1, len(self.explored_nodes) // max_frames)
+        sampled = self.explored_nodes[::step]
+        self._sampled_nodes = sampled
+
+        figure = plt.figure(figsize=(10, 8))
+        axes = figure.add_subplot(111)
+        axes.set_aspect("equal")
+        axes.set_xlabel("X [m]", fontsize=15)
+        axes.set_ylabel("Y [m]", fontsize=15)
+
+        total_frames = len(sampled) + len(self.path)
+        self.anime = anm.FuncAnimation(
+            figure, self._update_frame, fargs=(axes,),
+            frames=total_frames, interval=50, repeat=False,
+        )
+
+        try:
+            print("Saving D* animation …")
+            self.anime.save(gif_name, writer="pillow", fps=20)
+            print("Animation saved successfully.")
+        except Exception as e:
+            print(f"Error saving animation: {e}")
+
+        plt.clf()
+        plt.close()
+
+    def _update_frame(self, i, axes):
+        display = self.grid.copy()
+        sampled = self._sampled_nodes
+
+        if i < len(sampled):
+            for j in range(i + 1):
+                gx, gy = sampled[j]
+                if 0 <= gx < display.shape[1] and 0 <= gy < display.shape[0]:
+                    display[gy, gx] = 0.25
+        else:
+            for gx, gy in sampled:
+                if 0 <= gx < display.shape[1] and 0 <= gy < display.shape[0]:
+                    display[gy, gx] = 0.25
+            path_idx = i - len(sampled)
+            if path_idx < len(self.path):
+                for j in range(path_idx + 1):
+                    gx, gy = self.path[j]
+                    if 0 <= gx < display.shape[1] and 0 <= gy < display.shape[0]:
+                        display[gy, gx] = 0.5
+
+        axes.clear()
+        colors = [
+            [1.0, 1.0, 1.0],  # free (white)
+            [0.4, 0.8, 1.0],  # explored (light blue)
+            [0.0, 1.0, 0.0],  # path (green)
+            [0.5, 0.5, 0.5],  # clearance (grey)
+            [0.0, 0.0, 0.0],  # obstacle (black)
+        ]
+        cmap = ListedColormap(colors)
+        axes.imshow(display,
+                    extent=[self.x_range[0], self.x_range[-1],
+                            self.y_range[0], self.y_range[-1]],
+                    origin='lower', cmap=cmap, alpha=0.8)
+        axes.plot(self.start_world[0], self.start_world[1], 'go', label="Start")
+        axes.plot(self.goal_world[0], self.goal_world[1], 'ro', label="Goal")
+        axes.legend()
+
+
+if __name__ == "__main__":
+    map_file = "map.json"
+    path_file = "path.json"
+    gif_path = "dstar_search.gif"
+
+    x_lim, y_lim = MinMax(-5, 55), MinMax(-20, 25)
+    start = (0, 0)
+    goal = (50, -10)
+
+    planner = DStarPathPlanner(start, goal, map_file,
+                               x_lim=x_lim, y_lim=y_lim,
+                               path_filename=path_file, gif_name=gif_path)
diff --git a/src/simulations/path_planning/dstar_path_planning/dstar_navigate.gif b/src/simulations/path_planning/dstar_path_planning/dstar_navigate.gif
new file mode 100644
index 00000000..0f60ce1a
Binary files /dev/null and b/src/simulations/path_planning/dstar_path_planning/dstar_navigate.gif differ
diff --git a/src/simulations/path_planning/dstar_path_planning/dstar_path_planning.py b/src/simulations/path_planning/dstar_path_planning/dstar_path_planning.py
new file mode 100644
index 00000000..d0cdbf54
--- /dev/null
+++ b/src/simulations/path_planning/dstar_path_planning/dstar_path_planning.py
@@ -0,0 +1,478 @@
+"""
+dstar_path_planning.py
+
+Simulation that demonstrates D*'s incremental replanning.
+
+Two GIFs are produced:
+    1. **dstar_search.gif** – grid-based animation showing D* expansion,
+       initial path, dynamic obstacle injection, replan expansion, and
+       the replanned path.
+    2. **dstar_navigate.gif** – car-following navigation using the same
+       vehicle / pure-pursuit stack as the other planners.  Midway through,
+       a new obstacle appears on the initial path, D* replans, and the car
+       seamlessly switches to following the new route.
+"""
+
+import numpy as np
+import sys
+import json
+import matplotlib.pyplot as plt
+import matplotlib.animation as anm
+from pathlib import Path
+from matplotlib.colors import ListedColormap
+
+abs_dir_path = str(Path(__file__).absolute().parent)
+relative_path = "/../../../components/"
+relative_simulations = "/../../../simulations/"
+
+sys.path.append(abs_dir_path + relative_path + "visualization")
+sys.path.append(abs_dir_path + relative_path + "state")
+sys.path.append(abs_dir_path + relative_path + "vehicle")
+sys.path.append(abs_dir_path + relative_path + "obstacle")
+sys.path.append(abs_dir_path + relative_path + "mapping/grid")
+sys.path.append(abs_dir_path + relative_path + "course/cubic_spline_course")
+sys.path.append(abs_dir_path + relative_path + "control/pure_pursuit")
+sys.path.append(abs_dir_path + relative_path + "plan/dstar")
+
+from global_xy_visualizer import GlobalXYVisualizer
+from min_max import MinMax
+from time_parameters import TimeParameters
+from vehicle_specification import VehicleSpecification
+from state import State
+from four_wheels_vehicle import FourWheelsVehicle
+from obstacle import Obstacle
+from obstacle_list import ObstacleList
+from cubic_spline_course import CubicSplineCourse
+from pure_pursuit_controller import PurePursuitController
+from binary_occupancy_grid import BinaryOccupancyGrid
+from dstar_path_planner import DStarPathPlanner
+
+# flag to show plot figure
+# when executed as unit test, this flag is set as false
+show_plot = True
+
+
+# ---- helpers ---------------------------------------------------------------
+
+def _build_obstacle_cells(planner, cx, cy, half_w, half_h):
+    """Convert a world-space rectangle into a list of (row, col) grid cells."""
+    cells = []
+    for wx in np.arange(cx - half_w, cx + half_w, planner.resolution):
+        for wy in np.arange(cy - half_h, cy + half_h, planner.resolution):
+            r = int((wy - planner.y_range[0]) / planner.resolution)
+            c = int((wx - planner.x_range[0]) / planner.resolution)
+            if 0 <= r < planner.rows and 0 <= c < planner.cols:
+                cells.append((r, c))
+    return cells
+
+
+def _subsample(lst, max_frames):
+    """Return at most *max_frames* evenly-spaced items from *lst*."""
+    if len(lst) <= max_frames:
+        return list(lst)
+    step = max(1, len(lst) // max_frames)
+    return list(lst[::step])
+
+
+def _path_to_course(planner, grid_path, speed_kmph=20, color='r'):
+    """Convert a D* grid path to a CubicSplineCourse."""
+    world_pts = []
+    for gx, gy in grid_path:
+        wx = planner.x_range[0] + gx * planner.resolution
+        wy = planner.y_range[0] + gy * planner.resolution
+        world_pts.append((wx, wy))
+
+    # Sub-sample to ~20 control points for a smooth spline
+    if len(world_pts) > 20:
+        indices = np.linspace(0, len(world_pts) - 1, 20, dtype=int)
+        world_pts = [world_pts[i] for i in indices]
+
+    xs = [p[0] for p in world_pts]
+    ys = [p[1] for p in world_pts]
+    return CubicSplineCourse(xs, ys, speed_kmph, color=color)
+
+
+class _ReplanPurePursuit:
+    """
+    A wrapper around PurePursuitController that switches to a new course
+    when the vehicle gets close to the dynamic-obstacle region.
+
+    Manages all course visualisation so the viewer sees:
+      - Before replan: initial course drawn as red dots (standard look).
+      - After replan: initial course fades to dotted light-grey,
+        replanned course drawn as red dots, dynamic obstacle appears.
+
+    PurePursuitController uses default green target-point colour for both
+    controllers, matching the standard simulations.
+    """
+
+    def __init__(self, spec, initial_course, replanned_course,
+                 switch_x, switch_y, switch_radius,
+                 dynamic_obst_list):
+        self._spec = spec
+        self._initial_course = initial_course
+        self._replanned_course = replanned_course
+        self._initial_ctrl = PurePursuitController(spec, initial_course)
+        self._replan_ctrl = PurePursuitController(spec, replanned_course)
+        self._active = self._initial_ctrl
+        self._switched = False
+        self._switch_x = switch_x
+        self._switch_y = switch_y
+        self._switch_radius = switch_radius
+        self._dyn_obst_list = dynamic_obst_list
+
+    def update(self, state, time_s):
+        # Check if we should switch
+        if not self._switched:
+            dx = state.get_x_m() - self._switch_x
+            dy = state.get_y_m() - self._switch_y
+            if (dx * dx + dy * dy) ** 0.5 < self._switch_radius:
+                self._switched = True
+                self._active = self._replan_ctrl
+        self._active.update(state, time_s)
+
+    def get_target_accel_mps2(self):
+        return self._active.get_target_accel_mps2()
+
+    def get_target_steer_rad(self):
+        return self._active.get_target_steer_rad()
+
+    def get_target_yaw_rate_rps(self):
+        return self._active.get_target_yaw_rate_rps()
+
+    def draw(self, axes, elems):
+        if self._switched:
+            # Old course → dotted light grey line
+            old, = axes.plot(self._initial_course.x_array,
+                             self._initial_course.y_array,
+                             linestyle=':', linewidth=1.2,
+                             color='#AAAAAA', label="Old Course")
+            elems.append(old)
+            # New course → red dots (standard look)
+            self._replanned_course.draw(axes, elems)
+            # Dynamic obstacle becomes visible
+            self._dyn_obst_list.draw(axes, elems)
+        else:
+            # Before replan: draw initial course as red dots (standard)
+            self._initial_course.draw(axes, elems)
+        # Active PurePursuit draws its green target point (standard)
+        self._active.draw(axes, elems)
+
+
+# ---- search GIF -----------------------------------------------------------
+
+# Grid cell colour values used in the animation.
+_V_FREE = 0.0       # white
+_V_EXPLORED = 0.25  # light blue
+_V_REPLAN_EX = 0.35 # orange-ish
+_V_PATH = 0.50      # green
+_V_OLD_PATH = 0.60  # light grey (faded old path)
+_V_CLEARANCE = 0.75 # mid-grey
+_V_OBSTACLE = 1.0   # black
+
+
+def _build_search_gif(planner, initial_path, initial_explored,
+                      grid_before_obstacle, grid_after_obstacle,
+                      replan_explored, replanned_path,
+                      start, goal, gif_path):
+    """
+    Render the grid-based search / replan animation.
+
+    Six phases:
+      0. Initial search expansion (light blue cells, animated).
+      1. Initial path drawn progressively (green cells).
+      2. Hold — admire the initial path.
+      3. Obstacle appears on grid, old path turns grey — hold.
+      4. D* replan expansion from start outward (orange cells, animated).
+      5. Replanned path drawn progressively (green cells).
+      6. Hold — admire the replanned result.
+    """
+
+    # Sub-sampled frame lists per phase
+    ph_search   = _subsample(initial_explored, 80)
+    ph_ipath    = _subsample(initial_path, 30)
+    ph_hold1    = 20                                          # admire initial
+    ph_obst     = 25                                          # obstacle + grey
+    ph_replan   = _subsample(replan_explored, 60)
+    ph_rpath    = _subsample(replanned_path, 30) if replanned_path else []
+    ph_hold2    = 25                                          # admire final
+
+    lengths = [len(ph_search), len(ph_ipath), ph_hold1,
+               ph_obst, len(ph_replan), len(ph_rpath), ph_hold2]
+    offsets = np.cumsum([0] + lengths)
+    total_frames = int(offsets[-1])
+    n_phases = len(lengths)
+
+    def _phase_and_local(i):
+        for p in range(n_phases):
+            if i < offsets[p + 1]:
+                return p, i - int(offsets[p])
+        return n_phases - 1, lengths[-1] - 1
+
+    cmap = ListedColormap([
+        [1.0, 1.0, 1.0],    # 0  free        → white
+        [0.4, 0.8, 1.0],    # 1  explored     → light blue
+        [1.0, 0.6, 0.2],    # 2  replan exp   → orange
+        [0.0, 0.8, 0.0],    # 3  path         → green
+        [0.78, 0.78, 0.78], # 4  old path     → light grey
+        [0.5, 0.5, 0.5],    # 5  clearance    → mid-grey
+        [0.0, 0.0, 0.0],    # 6  obstacle     → black
+    ])
+
+    def _disc(display):
+        """Map continuous grid values to discrete colour indices."""
+        d = np.zeros_like(display, dtype=int)
+        d[display == _V_FREE] = 0
+        d[np.isclose(display, _V_EXPLORED)] = 1
+        d[np.isclose(display, _V_REPLAN_EX)] = 2
+        d[np.isclose(display, _V_PATH)] = 3
+        d[np.isclose(display, _V_OLD_PATH)] = 4
+        d[np.isclose(display, _V_CLEARANCE)] = 5
+        d[display >= 0.99] = 6
+        return d
+
+    def _paint(display, cells, val):
+        for gx, gy in cells:
+            if 0 <= gx < display.shape[1] and 0 <= gy < display.shape[0]:
+                if display[gy, gx] < 0.99:
+                    display[gy, gx] = val
+
+    # Phase-dependent background grid:
+    #   phases 0-2 use grid_before_obstacle (no dynamic obstacle yet)
+    #   phases 3+  use grid_after_obstacle  (obstacle present)
+    def _base_grid(phase):
+        return (grid_before_obstacle if phase <= 2
+                else grid_after_obstacle).copy()
+
+    # ---- rendering helpers that accumulate per-phase ----
+
+    def _draw_initial_explored(display):
+        """Paint all initial-search explored cells (blue)."""
+        _paint(display, ph_search, _V_EXPLORED)
+
+    def _draw_initial_path(display):
+        """Paint the full initial path (green)."""
+        _paint(display, initial_path, _V_PATH)
+
+    def _draw_old_path_grey(display):
+        """Paint the initial path as faded grey (superseded)."""
+        _paint(display, initial_path, _V_OLD_PATH)
+
+    titles = [
+        lambda l: f"D* Initial Search ({min(l+1, len(ph_search))}/{len(ph_search)})",
+        lambda _: "Initial Path Found",
+        lambda _: "Initial Path Found",
+        lambda _: "Obstacle Detected!",
+        lambda l: f"D* Replanning from Start ({min(l+1, len(ph_replan))}/{len(ph_replan)})",
+        lambda _: "Replanned Path Found",
+        lambda _: "Replanned Path Found",
+    ]
+
+    def update_frame(i, axes):
+        phase, local = _phase_and_local(i)
+        display = _base_grid(phase)
+
+        if phase == 0:
+            # Animate initial search expansion
+            _paint(display, ph_search[:local + 1], _V_EXPLORED)
+
+        elif phase == 1:
+            # Draw all explored + animate initial path
+            _draw_initial_explored(display)
+            _paint(display, ph_ipath[:local + 1], _V_PATH)
+
+        elif phase == 2:
+            # Hold: full explored + full initial path
+            _draw_initial_explored(display)
+            _draw_initial_path(display)
+
+        elif phase == 3:
+            # Obstacle has appeared (grid_after_obstacle).
+            # Old explored stays, old path turns grey.
+            _draw_initial_explored(display)
+            _draw_old_path_grey(display)
+
+        elif phase == 4:
+            # Animate replan expansion; old path stays grey
+            _draw_initial_explored(display)
+            _draw_old_path_grey(display)
+            _paint(display, ph_replan[:local + 1], _V_REPLAN_EX)
+
+        elif phase == 5:
+            # All replan explored + animate replanned path
+            _draw_initial_explored(display)
+            _draw_old_path_grey(display)
+            _paint(display, ph_replan, _V_REPLAN_EX)
+            _paint(display, ph_rpath[:local + 1], _V_PATH)
+
+        else:  # phase == 6
+            # Hold: final result
+            _draw_initial_explored(display)
+            _draw_old_path_grey(display)
+            _paint(display, ph_replan, _V_REPLAN_EX)
+            _paint(display, replanned_path, _V_PATH)
+
+        axes.clear()
+        axes.imshow(_disc(display),
+                    extent=[planner.x_range[0], planner.x_range[-1],
+                            planner.y_range[0], planner.y_range[-1]],
+                    origin='lower', cmap=cmap, vmin=0, vmax=6, alpha=0.85)
+        axes.plot(start[0], start[1], 'go', markersize=8, label="Start")
+        axes.plot(goal[0], goal[1], 'ro', markersize=8, label="Goal")
+        axes.set_title(titles[phase](local), fontsize=14)
+        axes.legend(loc='upper left')
+
+    fig = plt.figure(figsize=(10, 8))
+    ax = fig.add_subplot(111)
+    ax.set_aspect("equal")
+    ax.set_xlabel("X [m]", fontsize=15)
+    ax.set_ylabel("Y [m]", fontsize=15)
+
+    if show_plot:
+        print(f"D* search animation: {total_frames} frames")
+        anime = anm.FuncAnimation(fig, update_frame, fargs=(ax,),
+                                  frames=total_frames, interval=30, repeat=False)
+        try:
+            anime.save(gif_path, writer="pillow", fps=20)
+            print(f"Search GIF saved to {gif_path}")
+        except Exception as e:
+            print(f"Error saving search GIF: {e}")
+    else:
+        test_frames = set()
+        for s in offsets[:-1]:
+            test_frames.update([int(s), int(s) + 1])
+        for f in sorted(test_frames):
+            if f < total_frames:
+                update_frame(f, ax)
+    plt.clf()
+    plt.close()
+
+
+# ---- main ------------------------------------------------------------------
+
+def main():
+    """Main process function"""
+
+    x_lim, y_lim = MinMax(-5, 55), MinMax(-20, 25)
+    sim_dir = (abs_dir_path + relative_simulations
+               + "path_planning/dstar_path_planning/")
+    map_path = sim_dir + "map.json"
+    path_filename = sim_dir + "path.json"
+    search_gif_path = sim_dir + "dstar_search.gif"
+    navigate_gif_path = sim_dir + "dstar_navigate.gif"
+
+    # ---- grid + static obstacles ----
+    occ_grid = BinaryOccupancyGrid(x_lim, y_lim, resolution=0.5,
+                                   clearance=1.5, map_path=map_path)
+
+    obst_list = ObstacleList()
+    obst_list.add_obstacle(Obstacle(State(x_m=10.0, y_m=15.0),
+                                    length_m=10, width_m=8))
+    obst_list.add_obstacle(Obstacle(State(x_m=40.0, y_m=0.0),
+                                    length_m=2, width_m=10))
+    occ_grid.add_object(obst_list)
+    occ_grid.save_map()
+
+    # ---- D* initial plan ----
+    start, goal = (0, 0), (50, -10)
+    planner = DStarPathPlanner(start, goal, map_path,
+                               x_lim=x_lim, y_lim=y_lim,
+                               path_filename=path_filename,
+                               gif_name=None)
+
+    initial_path = list(planner.path)
+    if not initial_path:
+        print("D*: no initial path found – aborting.")
+        return
+
+    grid_before_obstacle = planner.grid.copy()
+
+    # ---- dynamic obstacle on the path ----
+    block_idx = len(initial_path) * 4 // 10
+    block_gx, block_gy = initial_path[block_idx]
+    block_wx = planner.x_range[0] + block_gx * planner.resolution
+    block_wy = planner.y_range[0] + block_gy * planner.resolution
+
+    dyn_half_w, dyn_half_h = 1.5, 5.0
+    dyn_cells = _build_obstacle_cells(planner, block_wx, block_wy,
+                                      dyn_half_w, dyn_half_h)
+
+    robot_step_at_detection = max(1, block_idx // 2)
+
+    # ---- inject & replan ----
+    planner.update_obstacles(dyn_cells)
+    grid_after_obstacle = planner.grid.copy()
+
+    current_grid_idx = (initial_path[robot_step_at_detection][1],
+                        initial_path[robot_step_at_detection][0])
+    replanned_tail = planner.replan(current_pos_idx=current_grid_idx)
+    replan_explored = list(planner.replan_explored_nodes)
+    initial_explored = list(
+        planner.explored_nodes[:len(planner.explored_nodes) - len(replan_explored)]
+    )
+
+    # Build full replanned path: initial prefix (start → detection) + replanned tail
+    initial_prefix = initial_path[:robot_step_at_detection]
+    if replanned_tail:
+        # Deduplicate the junction point if present in both segments
+        if initial_prefix and replanned_tail[0] == initial_prefix[-1]:
+            replanned_path = initial_prefix + replanned_tail[1:]
+        else:
+            replanned_path = initial_prefix + replanned_tail
+    else:
+        replanned_path = replanned_tail
+
+    # ---- 1) search GIF ----
+    _build_search_gif(planner, initial_path, initial_explored,
+                      grid_before_obstacle, grid_after_obstacle,
+                      replan_explored, replanned_path,
+                      start, goal, search_gif_path)
+
+    # ---- 2) navigation GIF (car following) ----
+    # Both courses are red (standard look).  The _ReplanPurePursuit
+    # controller manages which course is drawn and in what style:
+    #   before replan → initial course as red dots
+    #   after  replan → initial course as dotted grey, replanned as red dots
+    initial_course = _path_to_course(planner, initial_path, color='r')
+    replanned_course = _path_to_course(planner, replanned_path, color='r') \
+        if replanned_path else initial_course
+
+    # Dynamic obstacle as a visual Obstacle for the car scene
+    dyn_obst_list = ObstacleList()
+    dyn_obst_list.add_obstacle(
+        Obstacle(State(x_m=block_wx, y_m=block_wy),
+                 length_m=dyn_half_w, width_m=dyn_half_h)
+    )
+
+    # The switch point in world coords (where the robot "detects" the wall)
+    det_gx, det_gy = initial_path[robot_step_at_detection]
+    switch_wx = planner.x_range[0] + det_gx * planner.resolution
+    switch_wy = planner.y_range[0] + det_gy * planner.resolution
+
+    vis = GlobalXYVisualizer(x_lim, y_lim, TimeParameters(span_sec=20),
+                             show_zoom=False, gif_name=navigate_gif_path)
+
+    # Static obstacles (visual)
+    vis.add_object(obst_list)
+
+    # NOTE: courses are NOT added to vis directly — the controller's draw()
+    # handles all course rendering so it can swap styles at replan time.
+
+    # Vehicle with the replan-aware controller
+    spec = VehicleSpecification()
+    replan_ctrl = _ReplanPurePursuit(
+        spec, initial_course, replanned_course,
+        switch_wx, switch_wy, switch_radius=3.0,
+        dynamic_obst_list=dyn_obst_list,
+    )
+    vehicle = FourWheelsVehicle(State(color=spec.color), spec,
+                                controller=replan_ctrl, show_zoom=False)
+    vis.add_object(vehicle)
+
+    if not show_plot:
+        vis.not_show_plot()
+    vis.draw()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/simulations/path_planning/dstar_path_planning/dstar_search.gif b/src/simulations/path_planning/dstar_path_planning/dstar_search.gif
new file mode 100644
index 00000000..9d35b322
Binary files /dev/null and b/src/simulations/path_planning/dstar_path_planning/dstar_search.gif differ
diff --git a/src/simulations/path_planning/dstar_path_planning/map.json b/src/simulations/path_planning/dstar_path_planning/map.json
new file mode 100644
index 00000000..0d8e2b81
--- /dev/null
+++ b/src/simulations/path_planning/dstar_path_planning/map.json
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\ No newline at end of file
diff --git a/src/simulations/path_planning/dstar_path_planning/path.json b/src/simulations/path_planning/dstar_path_planning/path.json
new file mode 100644
index 00000000..ba0a25a9
--- /dev/null
+++ b/src/simulations/path_planning/dstar_path_planning/path.json
@@ -0,0 +1 @@
+[[0.0, 0.0], [2.5, -1.0], [5.0, -2.5], [7.5, -3.5], [10.5, -5.0], [13.0, -6.0], [15.5, -7.0], [18.0, -8.0], [21.0, -9.5], [23.5, -10.5], [26.0, -12.0], [28.5, -12.0], [31.5, -12.0], [34.0, -12.0], [36.5, -12.0], [39.0, -12.0], [42.0, -12.0], [44.5, -10.5], [47.0, -10.0], [50.0, -10.0]]
\ No newline at end of file
diff --git a/test/test_dstar_path_planning.py b/test/test_dstar_path_planning.py
new file mode 100644
index 00000000..77df2299
--- /dev/null
+++ b/test/test_dstar_path_planning.py
@@ -0,0 +1,19 @@
+"""
+Test of D* path planning simulation
+
+Verifies both the initial search and the incremental replanning
+after dynamic obstacles are injected.
+"""
+
+from pathlib import Path
+import sys
+import pytest
+
+sys.path.append(str(Path(__file__).absolute().parent) + "/../src/simulations/path_planning/dstar_path_planning")
+import dstar_path_planning
+
+
+def test_simulation():
+    dstar_path_planning.show_plot = False
+
+    dstar_path_planning.main()