Journal of Intelligent & Robotic Systems

, Volume 95, Issue 2, pp 511–526 | Cite as

Physics Based Path Planning for Autonomous Tracked Vehicle in Challenging Terrain

  • Bijo Sebastian
  • Pinhas Ben-TzviEmail author


This paper describes a novel physics-based path planning architecture for autonomous navigation of tracked vehicles in rough terrain conditions. Unlike conventional path planning applications for smooth and structured environments, factors such as slip, slope of the terrain, robot actuator limitations, and dynamics of robot terrain interactions must be considered for rough terrain applications. The proposed path planning method consists of a hybrid planner/simulator, which takes into account all of the above factors by simulating the closed loop motion of the robot with a low-level controller on a realistic terrain model inside a physics engine. Once a feasible path to the goal is obtained, the same low-level closed loop controller is then used to execute the proposed path on the actual robot. The proposed architecture uses the D* Lite algorithm working on a 2D grid representation of the terrain as the high-level planner, Bullet as the physics engine and a hybrid automaton as the low-level closed loop controller. The proposed method is validated both in simulation and through experiments. Inferences based on the results from simulations and experiments show that the proposed planner is more effective in providing an optimal feasible path as compared to existing methodologies, demonstrating clear advantages for rough, unstructured terrain planning. Based on the results, possible improvements to the method are proposed for future work.


Motion planning Mobile robot navigation Rough terrain Tracked vehicle Physics engine Hybrid automaton controller 


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This work is supported in part by the US Army Medical Research & Material Command’s Telemedicine & Advanced Technology Research Center (TATRC), under Contract No. W81XWH-16-C-0062. The views, opinions, and/or findings contained in this report are those of the authors and should not be construed as an official Department of the Army position, policy, or decision unless so designated by other documentation.

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Robotics and Mechatronics LabMechanical Engineering Department, Virginia TechBlacksburgUSA

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