update global path manually

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I am using neo_local_planner to control my robot. But as i observed when i use this base local plnner even if obstacle in in the global path the path is not being updated. This doesn't happens when i use dwa_local_planner. Navfn is being used as global path planner. Here is the parameter of neo_local_planner:

NeoLocalPlanner:

  # The x acceleration limit of the robot in meters/sec^2
  acc_lim_x : 0.25

  # The rotational acceleration limit of the robot in radians/sec^2
  acc_lim_theta : 0.8

  # The maximum x velocity for the robot in m/s.
  max_vel_x : 0.8

  # The minimum x velocity for the robot in m/s, negative for backwards motion.
  min_vel_x : -0.1

  # The absolute value of the maximum rotational velocity for the robot in rad/s
  max_rot_vel : 0.8

  # The absolute value of the minimum rotational velocity for the robot in rad/s
  min_rot_vel : 0.1

  # The absolute value of the maximum translational velocity for the robot in m/s
  max_trans_vel : 0.8

  # The absolute value of the minimum translational velocity for the robot in m/s
  min_trans_vel : 0.1

  # The tolerance in radians for the controller in yaw/rotation when achieving its goal
  yaw_goal_tolerance : 0.523599

  # The tolerance in meters for the controller in the x & y distance when achieving a goal
  xy_goal_tolerance : 0.1

  # How long to fine tune for goal position after reaching tolerance limits [s]
  goal_tune_time : 0.5

  # How far to predict control pose into the future based on latest odometry [s]
  lookahead_time : 0.2

  # How far to look ahead when computing path orientation [m]
  lookahead_dist : 0.5

  # Threshold yaw error below which we consider to start moving [rad]
  start_yaw_error : 0.2

  # Gain when adjusting final x position for goal [1/s]
  pos_x_gain : 1

  # Gain for lane keeping based on y error (differential only) [rad/s^2]
  pos_y_yaw_gain : 1

  # Gain for lane keeping based on yaw error (differential only) [1/s]
  yaw_gain : 1

  # Gain for adjusting yaw when not translating, or in case of holonomic drive  [1/s]
  static_yaw_gain : 3

  # Gain for y cost avoidance (differential only)
  cost_y_yaw_gain : 0.1

  # How far ahead to compute y cost gradient (constant offset) [m]
  cost_y_lookahead_dist : 0

  # How far ahead to compute y cost gradient (dynamic offset) [s]
  cost_y_lookahead_time : 1

  # Gain for yaw cost avoidance
  cost_yaw_gain : 1

  # Gain for final control low pass filter
  low_pass_gain : 0.5

  # Max cost to allow, above we slow down to min_trans_vel or even stop
  max_cost : 0.95

  # Max velocity based on curvature [rad/s]
  max_curve_vel : 0.2

  # Max distance to goal when looking for it [m]
  max_goal_dist : 0.5

  # Max distance allowable for backing up (zero = unlimited) [m]
  max_backup_dist : 0.5

  # Minimal distance for stopping [m]
  min_stop_dist : 0.5

  # If robot has differential drive, holonomic otherwise
  differential_drive : true

  max_update_count: 20