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Path Planning in Dynamic Environments Using Time Warps

                                                                            

by Siavash Farzan


Introduction


   The objective of path planning is to find a suitable path between two specific positions in an environment, which does not collide with static and/or dynamic obstacles. In our project, we present an innovative approach for this path planning problem in dynamic environments when the robot doesn’t have any prior knowledge about the map. Obstacles, such as walls and objects are sensed using a laser sensor mounted on the robot, and a new concept named Time Warps is used in order to predict the future positions of moving objects and to avoid collisions between the robot and moving obstacles by choosing an efficient path based on those predictions. The path is calculated based on harmonic potential fields and optimized by rubber band model.

   The proposed method was tested based on several conducted simulation scenarios employing MobileSim simulator for the Pioneer P3-DX robot. Implementation of the algorithm was done by C/C++ and CUDA programming using NVIDIA GTX 480 graphics processor unit (GPU) to perform the processes in real time and be used for real applications.

 

   

Demo Video

 

Previous work by Ruizhi Hong

 

Path Planning using Harmonic Potentials on the NVidia CUDA System(a)

CUDA Implementation of a mobile robot path planner.

The algorithm is based on harmonic potentials. The initial path is then optimized using an elastic model that simulates smoothing forces.  The parallel algorithm is implemented on CUDA in order to run in real time.

As shown in the figure above, the blue path is the output of the harmonic potential. The white pixels represent high energy potentials while the black pixels represent lower potentials. Red pixels are obstacles. The final and smooth path, in yellow, goes from higher potentials (starting position) to lower potentials (goal position).

The parallel version of the algorithms runs 10 times faster on CUDA than a typical CPU -- depending on the GPU and CPU platforms.

 

    References

  1. S. Farzan and G. N. DeSouza, "Path Planning in Dynamic Environments Using Time Warps". (Ready to be submitted)

  2. Hong, R, and DeSouza, G. N., " A Real-Time Path Planner for a Smart Wheelchair Using Harmonic Potentials and a Rubber Band Model,", in the Proceedings of the 2010 IEEE International Conference on Robotic System (IROS).

  3. DeSouza G.N., Kak A.C., " Vision for Mobile Robot Navigation", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 2, pp. , Feb. 2002.

(a) This research made use of GTX480s and Tesla's S1070 donated by NVIDIA via their Academic Partnership Program.

 

 

 

 

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