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Model Predictive Path Planning for an Autonomous Ground Vehicle in Rough Terrain

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Abstract

An autonomous ground vehicle (AGV) in rough terrain typically experiences uncertain environment. Because the uncertainty makes overall performance of autonomous navigation degrade, the AGV requires a suitable path to maintain or improve the performance against the uncertainty. In order to handle this problem, this study proposes a model predictive path planning algorithm by employing a passivity-based model predictive control (MPC) optimization setup. The model predictive path planning method is formulated as a finite optimization problem with an objective function and several constraints. In the cost function, environment perception result about the AGV’s own neighborhood is included and the only traversable region has low cost value. To reflect dynamic characteristics of the AGV, the proposed method utilizes dynamic and kinematic models of the AGV as equality constraints and limited range of states and control input as inequality constraints. In addition, the stability of the path planning method is improved by a passivity constraint. The solution of the optimization problem is obtained using the particle swarm optimization (PSO) method. Finally, field tests are conducted to validate the performance of the proposed algorithm, and satisfactory results of the autonomous navigation were obtained.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Chungbuk National University, Cheongju, Korea

    Jongho Shin

  2. Agency for Defense Development, Daejeon, Korea

    Dongjun Kwak & Kiho Kwak

Authors
  1. Jongho Shin
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  2. Dongjun Kwak
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  3. Kiho Kwak
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Correspondence to Jongho Shin.

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This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2019R1G1A1090040, NRF-2020M3C1C1A02086422).

Jongho Shin received his Ph.D. degree in Mechanical and Aerospace Engineering from Seoul National University in 2011. Until 2019, he worked in the Agency for Development, Daejeon, Korea, as a senior researcher and developed the autonomous ground vehicle. Since 2019, he has joined Chungbuk National University, Cheongju, Korea. His research interests include autonomous system, machine learning and nonlinear adaptive control.

Dongjun Kwak received his Ph.D. degree in Mechanical and Aerospace Engineering from Seoul National University in 2014. He is currently a senior researcher in Agency for Defense Development, Daejeon, Korea. His research interests include optimization, machine learning, and decision making for unmanned systems.

Kiho Kwak is a principal researcher in Agency for Defense Development, South Korea. He received his B.S. and M.S. degrees from the Korea University, in 1999 and 2001, respectively, and a Ph.D. degree in ECE from the Carnegie Mellon University (CMU) in 2012. His research interests include sensor fusion, online object modeling and perception and navigation for autonomous vehicles in outdoor environment.

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Shin, J., Kwak, D. & Kwak, K. Model Predictive Path Planning for an Autonomous Ground Vehicle in Rough Terrain. Int. J. Control Autom. Syst. 19, 2224–2237 (2021). https://doi.org/10.1007/s12555-020-0267-2

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  • DOI: https://doi.org/10.1007/s12555-020-0267-2

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