diff --git a/graphs/uniform_search_cost.py b/graphs/uniform_search_cost.py
new file mode 100644
index 000000000..35ed9d3f2
--- /dev/null
+++ b/graphs/uniform_search_cost.py
@@ -0,0 +1,228 @@
+from typing import List
+import heapq
+from copy import deepcopy
+
+
+class UniformCostSearch:
+    def __init__(self, current, final, grid):
+        self.m = len(grid[0])
+        self.n = len(grid)
+
+    # diagonal clockwise
+    dxy1 = [
+        (1, 1),
+        (1, 0),
+        (1, -1),
+        (0, -1),
+        (-1, -1),
+        (-1, 0),
+        (-1, 1),
+        (0, 1),
+    ]
+    # diagonal anti-clockwise
+    dxy2 = [
+        (-1, -1),
+        (-1, 0),
+        (0, -1),
+        (0, 1),
+        (1, 1),
+        (1, 0),
+        (1, -1),
+        (-1, 1),
+    ]
+
+    # start point and end point on same row and column right side
+    dxy3 = [
+        (0, -1),
+        (-1, -1),
+        (-1, 0),
+        (0, 1),
+        (1, 1),
+        (1, 0),
+        (1, -1),
+        (-1, 1),
+    ]
+    # start point and end point on same row and column left side
+    dxy4 = [
+        (0, 1),
+        (1, 1),
+        (1, 0),
+        (1, -1),
+        (0, -1),
+        (-1, -1),
+        (-1, 0),
+        (-1, 1),
+    ]
+    # start point and end point on same column and row down side
+    dxy5 = [
+        (1, 0),
+        (0, 1),
+        (1, 1),
+        (1, -1),
+        (0, -1),
+        (-1, -1),
+        (-1, 0),
+        (-1, 1),
+    ]
+    # start point and end point on same column and row up side
+    dxy6 = [
+        (0, -1),
+        (0, 1),
+        (1, 1),
+        (1, 0),
+        (1, -1),
+        (-1, -1),
+        (-1, 0),
+        (-1, 1),
+    ]
+
+    def get_shortest_path(self, start, end, dist, dxy):
+        shortest_path = []
+        curr_node = end
+        while curr_node != start:
+            shortest_path.append(curr_node)
+            row, col = curr_node
+            min_cell = float("inf")
+            next_cell = None
+            for dr, dc in dxy:
+                if (
+                    0 <= row + dr < self.m
+                    and 0 <= col + dc < self.n
+                    and [row + dr, col + dc] not in shortest_path
+                ):
+                    if dist[row + dr][col + dc] <= min_cell:
+                        min_cell = dist[row + dr][col + dc]
+                        next_cell = [row + dr, col + dc]
+            curr_node = next_cell
+        shortest_path.append(start)
+        return shortest_path
+
+    def ucs(self, current, final, grid, prev, dxy, goal_answer):
+        dist = [[float("inf") for _ in range(self.m)] for _ in range(self.n)]
+        visited = [[0 for _ in range(self.m)] for _ in range(self.n)]
+
+        x = current[0]
+        y = current[1]
+        dist[x][y] = 0
+        final_cnt = 0
+        heap = [(0, x, y)]
+        while heap:
+            d, x, y = heapq.heappop(heap)
+            if visited[x][y]:
+                continue
+            visited[x][y] = 1
+            if [x, y] in final:
+                idxs = [ix for ix, iy in enumerate(final) if iy == [x, y]]
+                if len(idxs) > 1:
+                    return None
+                if goal_answer[idxs[0]] == 10**8:
+                    final_cnt += 1
+                if goal_answer[idxs[0]] > d:
+                    goal_answer[idxs[0]] = d
+                if final_cnt == len(final):
+                    path = self.get_shortest_path(current, final[0], dist, dxy)
+                    return path
+            for dx, dy in dxy:
+                if (
+                    0 <= x + dx < self.m
+                    and 0 <= y + dy < self.n
+                    and grid[x + dx][y + dy] != 1
+                ):
+                    weight = 1
+                    diagonal_weight = 1.414
+                    if (
+                        (dx == -1 and dy == -1)
+                        or (dx == 1 and dy == 1)
+                        or (dx == 1 and dy == -1)
+                        or (dx == -1 and dy == 1)
+                    ):
+                        new_dist = d + diagonal_weight
+                    else:
+                        new_dist = d + weight
+                    if new_dist < dist[x + dx][y + dy]:
+                        dist[x + dx][y + dy] = new_dist
+                        prev[x + dx][y + dy] = (x, y)
+                        heapq.heappush(heap, (new_dist, x + dx, y + dy))
+
+    def your_algorithm(
+        self, start_point: List[int], end_point: List[int], grid: List[List[int]]
+    ) -> List[int]:
+        prev = [[None for _ in range(self.m)] for _ in range(self.n)]
+        dxy = []
+        if start_point[1] - end_point[1] == 0 and start_point[0] - end_point[0] < 0:
+            dxy = self.dxy5
+        elif start_point[1] - end_point[1] == 0 and start_point[0] - end_point[0] > 0:
+            dxy = self.dxy6
+        elif start_point[0] - end_point[0] == 0 and start_point[1] - end_point[1] < 0:
+            dxy = self.dxy4
+        elif start_point[0] - end_point[0] == 0 and start_point[1] - end_point[1] > 0:
+            dxy = self.dxy3
+        elif start_point[0] - end_point[0] > 0:
+            dxy = self.dxy2
+        elif start_point[0] - end_point[0] < 0:
+            dxy = self.dxy1
+        goal_answer = []
+        for cell in end_point:
+            goal_answer.append(10**8)
+        path = self.ucs(start_point, [end_point], grid, prev, dxy, goal_answer)
+        return path
+
+
+def run():
+    executed_object = UniformCostSearch(
+        [0, 7],
+        [19, 17],
+        [
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],
+            [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0],
+            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
+            [0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ],
+    )
+    path_result = executed_object.your_algorithm(
+        [0, 7],
+        [19, 17],
+        [
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],
+            [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0],
+            [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
+            [0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ],
+    )
+    print(path_result)
+
+
+if __name__ == "__main__":
+    run()