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difference between eband_local_planner and teb_local planner

asked 2016-08-19 11:35:53 -0500

rajnunes gravatar image

What exactly is the difference between teb and eband local planner and how exactly do they work differently ?

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answered 2016-08-19 12:28:12 -0500

croesmann gravatar image

updated 2016-08-19 12:55:15 -0500

eband_local_planner (classical Elastic Band approach by Quinlan et al.) and teb_local_planner (Timed Elastic Band (TEB) approach) are two completely different planning algorithms. However, the TEB principle is based on the classic elastic band idea.

eband_local_planner (Elastic Band)

  • Local path deformation (path: no timing law) based on internal and external forces
  • Internal forces contract the path (-> leading to the shortest path between start and goal)
  • External forces repel the path from obstacles
  • Implementation based on bubbles that represent discrete path points and free-space.
  • Adaption of the trajectory length w.r.t. bubbles/free-space (insertion and deletion of discrete points)
  • Extension to non-holonomic kinematics (supports differential-drive and omnidirectional robots)
  • Subject to local minima (e.g. left or right path around an obstacle, depends on initial path)

teb_local_planner (Timed Elastic Band)

  • Local trajectory deformation/optimization (trajectory: includes temporal information)
  • Instead of generating and applying forces, an objective/cost function is minimized
  • Temporal information is subject to optimization -> time-optimal trajectories (replacement for the internal forces)
  • Temporal information allows incorporation of (kino-)dynamic constraints during optimization (no need for a dedicated path-following controller, the teb_local_planner mimics a predictive controller)
  • Adaptation of the trajectory length based on the temporal discretization (insertion and deletion of discrete trajectory points)
  • Supports differential-drive, car-like and omnidirectional robots
  • Explores multiple distinctive topologies for parallel trajectory optimization in order to partially overcome the local minima problem (only in the scope of the local costmap due to limited CPU resources, a global planner is still required).
  • Path-following mode (minimize distance to global plan instead of minimizing transition time)
  • Bottleneck: very high computational burden (-> limited local costmap size/resolution resp. robot size).
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thanks and what do you think i should use if i want to avoid dynamic obstacles from laser scan

rajnunes gravatar imagerajnunes ( 2016-08-19 13:23:53 -0500 )edit

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Asked: 2016-08-19 11:35:53 -0500

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Last updated: Aug 19 '16