simulation_ws/src/rl-agent/markov/environments/mars_env.py [262:319]:
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        # First clear the queue so that we set the state to the start image
        _ = self.image_queue.get(block=True, timeout=None)
        self.set_next_state()

    '''
    DO NOT EDIT - Function to find the distance between the rover and nearest object within 4.5M via LIDAR
    '''
    def get_distance_to_object(self):

        while not self.ranges:
            time.sleep(SLEEP_WAITING_FOR_IMAGE_TIME_IN_SECOND)

        size = len(self.ranges)
        x = np.linspace(0, size - 1, 360)
        xp = np.arange(size)
        val = np.clip(np.interp(x, xp, self.ranges), 0, LIDAR_SCAN_MAX_DISTANCE)
        val[np.isnan(val)] = LIDAR_SCAN_MAX_DISTANCE

        # Find min distance
        self.collision_threshold = np.amin(val)


    '''
    DO NOT EDIT - Function to resize the image from the camera and set observation_space
    '''
    def set_next_state(self):
        try:
            # Make sure the first image is the starting image
            image_data = self.image_queue.get(block=True, timeout=None)
        
            # Read the image and resize to get the state
            image = Image.frombytes('RGB', (image_data.width, image_data.height), image_data.data, 'raw', 'RGB', 0, 1)
            image = image.resize((TRAINING_IMAGE_WIDTH,TRAINING_IMAGE_HEIGHT), PIL.Image.ANTIALIAS)
            
            # TODO - can we crop this image to get additional savings?
           
            self.next_state = np.array(image)
        except Exception as err:
            print("Error!::set_next_state:: {}".format(err))


    '''
    DO NOT EDIT - Reward Function buffer
    '''
    def call_reward_function(self, action):
        self.get_distance_to_object() #<-- Also evaluate for sideswipe and collistion damage
        
        # Get the observation
        self.set_next_state()
        
        # reduce power supply range
        self.power_supply_range = MAX_STEPS - self.steps
        
        # calculate reward
        reward, done = self.reward_function()

        # Accumulate reward for the episode
        self.reward_in_episode += reward
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simulation_ws/src/rl-agent/markov/environments/training_env.py [257:314]:
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        # First clear the queue so that we set the state to the start image
        _ = self.image_queue.get(block=True, timeout=None)
        self.set_next_state()

    '''
    DO NOT EDIT - Function to find the distance between the rover and nearest object within 4.5M via LIDAR
    '''
    def get_distance_to_object(self):

        while not self.ranges:
            time.sleep(SLEEP_WAITING_FOR_IMAGE_TIME_IN_SECOND)

        size = len(self.ranges)
        x = np.linspace(0, size - 1, 360)
        xp = np.arange(size)
        val = np.clip(np.interp(x, xp, self.ranges), 0, LIDAR_SCAN_MAX_DISTANCE)
        val[np.isnan(val)] = LIDAR_SCAN_MAX_DISTANCE

        # Find min distance
        self.collision_threshold = np.amin(val)


    '''
    DO NOT EDIT - Function to resize the image from the camera and set observation_space
    '''
    def set_next_state(self):
        try:
            # Make sure the first image is the starting image
            image_data = self.image_queue.get(block=True, timeout=None)
        
            # Read the image and resize to get the state
            image = Image.frombytes('RGB', (image_data.width, image_data.height), image_data.data, 'raw', 'RGB', 0, 1)
            image = image.resize((TRAINING_IMAGE_WIDTH,TRAINING_IMAGE_HEIGHT), PIL.Image.ANTIALIAS)
            
            # TODO - can we crop this image to get additional savings?
           
            self.next_state = np.array(image)
        except Exception as err:
            print("Error!::set_next_state:: {}".format(err))


    '''
    DO NOT EDIT - Reward Function buffer
    '''
    def call_reward_function(self, action):
        self.get_distance_to_object() #<-- Also evaluate for sideswipe and collistion damage
        
        # Get the observation
        self.set_next_state()
        
        # reduce power supply range
        self.power_supply_range = MAX_STEPS - self.steps
        
        # calculate reward
        reward, done = self.reward_function()

        # Accumulate reward for the episode
        self.reward_in_episode += reward
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