diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
new file mode 100644
index 00000000..f54b80b7
--- /dev/null
+++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
@@ -0,0 +1,582 @@
+"""
+Batch Informed Trees based path planning:
+Uses a heuristic to efficiently search increasingly dense 
+RGGs while reusing previous information. Provides faster 
+convergence that RRT*, Informed RRT* and other sampling based
+methods. 
+
+Uses lazy connecting by combining sampling based methods and A* 
+like incremental graph search algorithms. 
+
+author: Karan Chawla(@karanchawla)
+
+Reference: https://arxiv.org/abs/1405.5848
+"""
+
+import random
+import numpy as np
+import copy
+import operator
+import math
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+# Class to represent the explicit tree created
+# while sampling through the state space
+
+
+class RTree(object):
+
+    def __init__(self, start=[0, 0], lowerLimit=[0, 0], upperLimit=[10, 10], resolution=1):
+
+        self.vertices = dict()
+        self.edges = []
+        self.start = start
+        self.lowerLimit = lowerLimit
+        self.upperLimit = upperLimit
+        self.dimension = len(lowerLimit)
+        self.num_cells = [0] * self.dimension
+        self.resolution = resolution
+        # compute the number of grid cells based on the limits and
+        # resolution given
+        for idx in range(self.dimension):
+            self.num_cells[idx] = np.ceil(
+                (upperLimit[idx] - lowerLimit[idx]) / resolution)
+
+        vertex_id = self.realWorldToNodeId(start)
+        self.vertices[vertex_id] = []
+
+    def getRootId(self):
+        # return the id of the root of the tree
+        return 0
+
+    def addVertex(self, vertex):
+        # add a vertex to the tree
+        vertex_id = self.realWorldToNodeId(vertex)
+        self.vertices[vertex_id] = []
+        return vertex_id
+
+    def addEdge(self, v, x):
+        # create an edge between v and x vertices
+        if (v, x) not in self.edges:
+            self.edges.append((v, x))
+        # since the tree is undirected
+        self.vertices[v].append(x)
+        self.vertices[x].append(v)
+
+    def realCoordsToGridCoord(self, real_coord):
+        # convert real world coordinates to grid space
+        # depends on the resolution of the grid
+        # the output is the same as real world coords if the resolution
+        # is set to 1
+        coord = [0] * self.dimension
+        for i in range(len(coord)):
+            start = self.lowerLimit[i]  # start of the grid space
+            coord[i] = np.around((real_coord[i] - start) / self.resolution)
+        return coord
+
+    def gridCoordinateToNodeId(self, coord):
+        # This function maps a grid coordinate to a unique
+        # node id
+        nodeId = 0
+        for i in range(len(coord) - 1, -1, -1):
+            product = 1
+            for j in range(0, i):
+                product = product * self.num_cells[j]
+            nodeId = nodeId + coord[i] * product
+        return nodeId
+
+    def realWorldToNodeId(self, real_coord):
+        # first convert the given coordinates to grid space and then
+        # convert the grid space coordinates to a unique node id
+        return self.gridCoordinateToNodeId(self.realCoordsToGridCoord(real_coord))
+
+    def gridCoordToRealWorldCoord(self, coord):
+        # This function smaps a grid coordinate in discrete space
+        # to a configuration in the full configuration space
+        config = [0] * self.dimension
+        for i in range(0, len(coord)):
+            # start of the real world / configuration space
+            start = self.lowerLimit[i]
+            # step from the coordinate in the grid
+            grid_step = self.resolution * coord[i]
+            half_step = self.resolution / 2  # To get to middle of the grid
+            config[i] = start + grid_step  # + half_step
+        return config
+
+    def nodeIdToGridCoord(self, node_id):
+        # This function maps a node id to the associated
+        # grid coordinate
+        coord = [0] * len(self.lowerLimit)
+        for i in range(len(coord) - 1, -1, -1):
+            # Get the product of the grid space maximums
+            prod = 1
+            for j in range(0, i):
+                prod = prod * self.num_cells[j]
+            coord[i] = np.floor(node_id / prod)
+            node_id = node_id - (coord[i] * prod)
+        return coord
+
+    def nodeIdToRealWorldCoord(self, nid):
+        # This function maps a node in discrete space to a configuraiton
+        # in the full configuration space
+        return self.gridCoordToRealWorldCoord(self.nodeIdToGridCoord(nid))
+
+# Uses Batch Informed Trees to find a path from start to goal
+
+
+class BITStar(object):
+
+    def __init__(self, start, goal,
+                 obstacleList, randArea, eta=2.0,
+                 maxIter=80):
+        self.start = start
+        self.goal = goal
+
+        self.minrand = randArea[0]
+        self.maxrand = randArea[1]
+        self.maxIter = maxIter
+        self.obstacleList = obstacleList
+
+        self.vertex_queue = []
+        self.edge_queue = []
+        self.samples = dict()
+        self.g_scores = dict()
+        self.f_scores = dict()
+        self.nodes = dict()
+        self.r = float('inf')
+        self.eta = eta  # tunable parameter
+        self.unit_ball_measure = 1
+        self.old_vertices = []
+
+        # initialize tree
+        lowerLimit = [randArea[0], randArea[0]]
+        upperLimit = [randArea[1], randArea[1]]
+        self.tree = RTree(start=start, lowerLimit=lowerLimit,
+                          upperLimit=upperLimit, resolution=0.01)
+
+    def plan(self, animation=True):
+
+        self.startId = self.tree.realWorldToNodeId(self.start)
+        self.goalId = self.tree.realWorldToNodeId(self.goal)
+
+        # add goal to the samples
+        self.samples[self.goalId] = self.goal
+        self.g_scores[self.goalId] = float('inf')
+        self.f_scores[self.goalId] = 0
+
+        # add the start id to the tree
+        self.tree.addVertex(self.start)
+        self.g_scores[self.startId] = 0
+        self.f_scores[self.startId] = self.computeHeuristicCost(
+            self.startId, self.goalId)
+
+        iterations = 0
+        # max length we expect to find in our 'informed' sample space, starts as infinite
+        cBest = self.g_scores[self.goalId]
+        pathLen = float('inf')
+        solutionSet = set()
+        plan = []
+
+        # Computing the sampling space
+        cMin = math.sqrt(pow(self.start[0] - self.goal[1], 2) +
+                         pow(self.start[0] - self.goal[1], 2)) / 1.5
+        xCenter = np.matrix([[(self.start[0] + self.goal[0]) / 2.0],
+                             [(self.goal[1] - self.start[1]) / 2.0], [0]])
+        a1 = np.matrix([[(self.goal[0] - self.start[0]) / cMin],
+                        [(self.goal[1] - self.start[1]) / cMin], [0]])
+        etheta = math.atan2(a1[1], a1[0])
+        # first column of idenity matrix transposed
+        id1_t = np.matrix([1.0, 0.0, 0.0])
+        M = np.dot(a1, id1_t)
+        U, S, Vh = np.linalg.svd(M, 1, 1)
+        C = np.dot(np.dot(U, np.diag(
+            [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), Vh)
+
+        self.samples.update(self.informedSample(
+            200, cBest, cMin, xCenter, C))
+        foundGoal = False
+        # run until done
+        while (iterations < self.maxIter):
+            if len(self.vertex_queue) == 0 and len(self.edge_queue) == 0:
+                print("Batch: ", iterations)
+                # Using informed rrt star way of computing the samples
+                self.r = 2.0
+                if iterations != 0:
+                    if foundGoal:
+                        # a better way to do this would be to make number of samples
+                        # a function of cMin
+                        m = 200
+                        self.samples = dict()
+                        self.samples[self.goalId] = self.goal
+                    else:
+                        m = 100
+                    cBest = self.g_scores[self.goalId]
+                    self.samples.update(self.informedSample(
+                        m, cBest, cMin, xCenter, C))
+
+                # make the old vertices the new vertices
+                self.old_vertices += self.tree.vertices.keys()
+                # add the vertices to the vertex queue
+                for nid in self.tree.vertices.keys():
+                    if nid not in self.vertex_queue:
+                        self.vertex_queue.append(nid)
+            # expand the best vertices until an edge is better than the vertex
+            # this is done because the vertex cost represents the lower bound
+            # on the edge cost
+            while(self.bestVertexQueueValue() <= self.bestEdgeQueueValue()):
+                self.expandVertex(self.bestInVertexQueue())
+
+            # add the best edge to the tree
+            bestEdge = self.bestInEdgeQueue()
+            self.edge_queue.remove(bestEdge)
+
+            # Check if this can improve the current solution
+            estimatedCostOfVertex = self.g_scores[bestEdge[0]] + self.computeDistanceCost(
+                bestEdge[0], bestEdge[1]) + self.computeHeuristicCost(bestEdge[1], self.goalId)
+            estimatedCostOfEdge = self.computeDistanceCost(self.startId, bestEdge[0]) + self.computeHeuristicCost(
+                bestEdge[0], bestEdge[1]) + self.computeHeuristicCost(bestEdge[1], self.goalId)
+            actualCostOfEdge = self.g_scores[bestEdge[0]] + \
+                self.computeDistanceCost(bestEdge[0], bestEdge[1])
+
+            if(estimatedCostOfVertex < self.g_scores[self.goalId]):
+                if(estimatedCostOfEdge < self.g_scores[self.goalId]):
+                    if(actualCostOfEdge < self.g_scores[self.goalId]):
+                        # connect this edge
+                        firstCoord = self.tree.nodeIdToRealWorldCoord(
+                            bestEdge[0])
+                        secondCoord = self.tree.nodeIdToRealWorldCoord(
+                            bestEdge[1])
+                        path = self.connect(firstCoord, secondCoord)
+                        lastEdge = self.tree.realWorldToNodeId(secondCoord)
+                        if path is None or len(path) == 0:
+                            continue
+                        nextCoord = path[len(path) - 1, :]
+                        nextCoordPathId = self.tree.realWorldToNodeId(
+                            nextCoord)
+                        bestEdge = (bestEdge[0], nextCoordPathId)
+                        if(bestEdge[1] in self.tree.vertices.keys()):
+                            continue
+                        else:
+                            try:
+                                del self.samples[bestEdge[1]]
+                            except(KeyError):
+                                pass
+                            eid = self.tree.addVertex(nextCoord)
+                            self.vertex_queue.append(eid)
+                        if eid == self.goalId or bestEdge[0] == self.goalId or bestEdge[1] == self.goalId:
+                            print("Goal found")
+                            foundGoal = True
+
+                        self.tree.addEdge(bestEdge[0], bestEdge[1])
+
+                        g_score = self.computeDistanceCost(
+                            bestEdge[0], bestEdge[1])
+                        self.g_scores[bestEdge[1]] = g_score + \
+                            self.g_scores[bestEdge[0]]
+                        self.f_scores[bestEdge[1]] = g_score + \
+                            self.computeHeuristicCost(bestEdge[1], self.goalId)
+                        self.updateGraph()
+
+                        # visualize new edge
+                        if animation:
+                            self.drawGraph(xCenter=xCenter, cBest=cBest,
+                                           cMin=cMin, etheta=etheta, samples=self.samples.values(),
+                                           start=firstCoord, end=secondCoord, tree=self.tree.edges)
+
+                        for edge in self.edge_queue:
+                            if(edge[0] == bestEdge[1]):
+                                if self.g_scores[edge[0]] + self.computeDistanceCost(edge[0], bestEdge[1]) >= self.g_scores[self.goalId]:
+                                    if(edge[0], best_edge[1]) in self.edge_queue:
+                                        self.edge_queue.remove(
+                                            (edge[0], bestEdge[1]))
+                            if(edge[1] == bestEdge[1]):
+                                if self.g_scores[edge[1]] + self.computeDistanceCost(edge[1], bestEdge[1]) >= self.g_scores[self.goalId]:
+                                    if(lastEdge, bestEdge[1]) in self.edge_queue:
+                                        self.edge_queue.remove(
+                                            (lastEdge, bestEdge[1]))
+            else:
+                print("Nothing good")
+                self.edge_queue = []
+                self.vertex_queue = []
+            iterations += 1
+
+        print("Finding the path")
+        plan.append(self.goal)
+        currId = self.goalId
+        while (currId != self.startId):
+            plan.append(self.tree.nodeIdToRealWorldCoord(currId))
+            currId = self.nodes[currId]
+
+        plan.append(self.start)
+        plan = plan[::-1]  # reverse the plan
+        return plan
+
+    def connect(self, start, end):
+            # A function which attempts to extend from a start coordinates
+            # to goal coordinates
+        steps = self.computeDistanceCost(self.tree.realWorldToNodeId(
+            start), self.tree.realWorldToNodeId(end)) * 10
+        x = np.linspace(start[0], end[0], num=steps)
+        y = np.linspace(start[1], end[1], num=steps)
+        for i in range(len(x)):
+            if(self._collisionCheck(x[i], y[i])):
+                if(i == 0):
+                    return None
+                # if collision, send path until collision
+                return np.vstack((x[0:i], y[0:i])).transpose()
+
+        return np.vstack((x, y)).transpose()
+
+    def _collisionCheck(self, x, y):
+        for (ox, oy, size) in self.obstacleList:
+            dx = ox - x
+            dy = oy - y
+            d = dx * dx + dy * dy
+            if d <= size ** 2:
+                return True  # collision
+        return False
+
+    # def prune(self, c):
+
+    def computeHeuristicCost(self, start_id, goal_id):
+        # Using Manhattan distance as heuristic
+        start = np.array(self.tree.nodeIdToRealWorldCoord(start_id))
+        goal = np.array(self.tree.nodeIdToRealWorldCoord(goal_id))
+
+        return np.linalg.norm(start - goal, 2)
+
+    def computeDistanceCost(self, vid, xid):
+        # L2 norm distance
+        start = np.array(self.tree.nodeIdToRealWorldCoord(vid))
+        stop = np.array(self.tree.nodeIdToRealWorldCoord(xid))
+
+        return np.linalg.norm(stop - start, 2)
+
+    # Sample free space confined in the radius of ball R
+    def informedSample(self, m, cMax, cMin, xCenter, C):
+        samples = dict()
+        print("g_Score goal id: ", self.g_scores[self.goalId])
+        for i in range(m + 1):
+            if cMax < float('inf'):
+                r = [cMax / 2.0,
+                     math.sqrt(cMax**2 - cMin**2) / 2.0,
+                     math.sqrt(cMax**2 - cMin**2) / 2.0]
+                L = np.diag(r)
+                xBall = self.sampleUnitBall()
+                rnd = np.dot(np.dot(C, L), xBall) + xCenter
+                rnd = [rnd[(0, 0)], rnd[(1, 0)]]
+                random_id = self.tree.realWorldToNodeId(rnd)
+                samples[random_id] = rnd
+            else:
+                rnd = self.sampleFreeSpace()
+                random_id = self.tree.realWorldToNodeId(rnd)
+                samples[random_id] = rnd
+        return samples
+
+    # Sample point in a unit ball
+    def sampleUnitBall(self):
+        a = random.random()
+        b = random.random()
+
+        if b < a:
+            a, b = b, a
+
+        sample = (b * math.cos(2 * math.pi * a / b),
+                  b * math.sin(2 * math.pi * a / b))
+        return np.array([[sample[0]], [sample[1]], [0]])
+
+    def sampleFreeSpace(self):
+        rnd = [random.uniform(self.minrand, self.maxrand),
+               random.uniform(self.minrand, self.maxrand)]
+
+        return rnd
+
+    def bestVertexQueueValue(self):
+        if(len(self.vertex_queue) == 0):
+            return float('inf')
+        values = [self.g_scores[v] +
+                  self.computeHeuristicCost(v, self.goalId) for v in self.vertex_queue]
+        values.sort()
+        return values[0]
+
+    def bestEdgeQueueValue(self):
+        if(len(self.edge_queue) == 0):
+            return float('inf')
+        # return the best value in the queue by score g_tau[v] + c(v,x) + h(x)
+        values = [self.g_scores[e[0]] + self.computeDistanceCost(e[0], e[1]) +
+                  self.computeHeuristicCost(e[1], self.goalId) for e in self.edge_queue]
+        values.sort(reverse=True)
+        return values[0]
+
+    def bestInVertexQueue(self):
+        # return the best value in the vertex queue
+        v_plus_vals = [(v, self.g_scores[v] + self.computeHeuristicCost(v, self.goalId))
+                       for v in self.vertex_queue]
+        v_plus_vals = sorted(v_plus_vals, key=lambda x: x[1])
+        # print(v_plus_vals)
+        return v_plus_vals[0][0]
+
+    def bestInEdgeQueue(self):
+        e_and_values = [(e[0], e[1], self.g_scores[e[0]] + self.computeDistanceCost(
+            e[0], e[1]) + self.computeHeuristicCost(e[1], self.goalId)) for e in self.edge_queue]
+        e_and_values = sorted(e_and_values, key=lambda x: x[2])
+
+        return (e_and_values[0][0], e_and_values[0][1])
+
+    def expandVertex(self, vid):
+        self.vertex_queue.remove(vid)
+
+        # get the coordinates for given vid
+        currCoord = np.array(self.tree.nodeIdToRealWorldCoord(vid))
+
+        # get the nearest value in vertex for every one in samples where difference is
+        # less than the radius
+        neigbors = []
+        for sid, scoord in self.samples.items():
+            scoord = np.array(scoord)
+            if(np.linalg.norm(scoord - currCoord, 2) <= self.r and sid != vid):
+                neigbors.append((sid, scoord))
+
+        # add an edge to the edge queue is the path might improve the solution
+        for neighbor in neigbors:
+            sid = neighbor[0]
+            scoord = neighbor[1]
+            estimated_f_score = self.computeDistanceCost(
+                self.startId, vid) + self.computeHeuristicCost(sid, self.goalId) + self.computeDistanceCost(vid, sid)
+            if estimated_f_score < self.g_scores[self.goalId]:
+                self.edge_queue.append((vid, sid))
+
+        # add the vertex to the edge queue
+        if vid not in self.old_vertices:
+            neigbors = []
+            for v, edges in self.tree.vertices.items():
+                if v != vid and (v, vid) not in self.edge_queue and (vid, v) not in self.edge_queue:
+                    vcoord = self.tree.nodeIdToRealWorldCoord(v)
+                    if(np.linalg.norm(vcoord - currCoord, 2) <= self.r and v != vid):
+                        neigbors.append((vid, vcoord))
+
+            for neighbor in neigbors:
+                sid = neighbor[0]
+                scoord = neighbor[1]
+                estimated_f_score = self.computeDistanceCost(self.startId, vid) + \
+                    self.computeDistanceCost(
+                        vid, sid) + self.computeHeuristicCost(sid, self.goalId)
+                if estimated_f_score < self.g_scores[self.goalId] and (self.g_scores[vid] + self.computeDistanceCost(vid, sid)) < self.g_scores[sid]:
+                    self.edge_queue.append((vid, sid))
+
+    def updateGraph(self):
+        closedSet = []
+        openSet = []
+        currId = self.startId
+        openSet.append(currId)
+
+        foundGoal = False
+
+        while len(openSet) != 0:
+            # get the element with lowest f_score
+            currId = min(openSet, key=lambda x: self.f_scores[x])
+
+            # remove element from open set
+            openSet.remove(currId)
+
+            # Check if we're at the goal
+            if(currId == self.goalId):
+                self.nodes[self.goalId]
+                foundGoal = True
+                break
+
+            if(currId not in closedSet):
+                closedSet.append(currId)
+
+            # find a non visited successor to the current node
+            successors = self.tree.vertices[currId]
+            for succesor in successors:
+                if(succesor in closedSet):
+                    continue
+                else:
+                    # claculate tentative g score
+                    succesorCoord = self.tree.nodeIdToRealWorldCoord(succesor)
+                    g_score = self.g_scores[currId] + \
+                        self.computeDistanceCost(currId, succesor)
+                    if succesor not in openSet:
+                        # add the successor to open set
+                        openSet.append(succesor)
+                    elif g_score >= self.g_scores[succesor]:
+                        continue
+
+                    # update g and f scores
+                    self.g_scores[succesor] = g_score
+                    self.f_scores[succesor] = g_score + \
+                        self.computeHeuristicCost(succesor, self.goalId)
+
+                    # store the parent and child
+                    self.nodes[succesor] = currId
+
+    def drawGraph(self, xCenter=None, cBest=None, cMin=None, etheta=None,
+                  samples=None, start=None, end=None, tree=None):
+        print("Plotting Graph")
+        plt.clf()
+        for rnd in samples:
+            if rnd is not None:
+                plt.plot(rnd[0], rnd[1], "^k")
+                if cBest != float('inf'):
+                    self.plot_ellipse(xCenter, cBest, cMin, etheta)
+
+        if start is not None and end is not None:
+            plt.plot([start[0], start[1]], [end[0], end[1]], "-g")
+
+        for (ox, oy, size) in self.obstacleList:
+            plt.plot(ox, oy, "ok", ms=30 * size)
+
+        plt.plot(self.start[0], self.start[1], "xr")
+        plt.plot(self.goal[0], self.goal[1], "xr")
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        plt.pause(0.01)
+
+    def plot_ellipse(self, xCenter, cBest, cMin, etheta):
+
+        a = math.sqrt(cBest**2 - cMin**2) / 2.0
+        b = cBest / 2.0
+        angle = math.pi / 2.0 - etheta
+        cx = xCenter[0]
+        cy = xCenter[1]
+
+        t = np.arange(0, 2 * math.pi + 0.1, 0.1)
+        x = [a * math.cos(it) for it in t]
+        y = [b * math.sin(it) for it in t]
+        R = np.matrix([[math.cos(angle), math.sin(angle)],
+                       [-math.sin(angle), math.cos(angle)]])
+        fx = R * np.matrix([x, y])
+        px = np.array(fx[0, :] + cx).flatten()
+        py = np.array(fx[1, :] + cy).flatten()
+        plt.plot(cx, cy, "xc")
+        plt.plot(px, py, "--c")
+
+
+def main():
+    print("Starting Batch Informed Trees Star planning")
+    obstacleList = [
+        (5, 5, 0.5),
+        (9, 6, 1),
+        (7, 5, 1),
+        (1, 5, 1),
+        (3, 6, 1),
+        (7, 9, 1)
+    ]
+
+    bitStar = BITStar(start=[-1, 0], goal=[3, 8], obstacleList=obstacleList,
+                      randArea=[-2, 15])
+    path = bitStar.plan(animation=show_animation)
+    print(path)
+    if show_animation:
+        plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
+        plt.grid(True)
+        plt.pause(0.05)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/BatchInformedRRTStar/bit_star.png b/PathPlanning/BatchInformedRRTStar/bit_star.png
new file mode 100644
index 00000000..ee3036e8
Binary files /dev/null and b/PathPlanning/BatchInformedRRTStar/bit_star.png differ