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Autonomous platooning of multiple ground vehicles in rough terrain
Author(s) -
Shin Jongho,
Kwak Dong Jun,
Kim Jun
Publication year - 2021
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.21984
Subject(s) - motion planning , control theory (sociology) , terrain , kinematics , controller (irrigation) , engineering , model predictive control , constraint (computer aided design) , particle swarm optimization , observer (physics) , control engineering , computer science , robot , control (management) , algorithm , artificial intelligence , mechanical engineering , ecology , physics , classical mechanics , quantum mechanics , agronomy , biology
This study proposes an autonomous platooning algorithm, composed of velocity, and path planning systems, of multiple ground vehicles in rough terrain. The velocity planning system aims to maintain desired distance between preceding and rear vehicles, and the path planning system generates reliable path to make the vehicles move safely under the given rough environment. To supply a reliable velocity command for the maintenance of the predefined distance, a disturbance observer (DOB)‐based control method is employed. An uncertainty‐free longitudinal kinematic model is first utilized for a nominal velocity generator and the DOB is added to the nominal velocity planner for handling various uncertainties generated during the autonomous platooning. On the other hand, the path planning method is developed by employing a passivity‐based model predictive control method. With a derived passivity constraint, the stability of the platooning architecture, that is, the platooning performance, is improved. In addition, the proposed path planning method utilizes dynamic and kinematic models of the ground vehicle as equality constraints, and limited range of states and control input as inequality constraints, and perception result about the vehicle's own neighborhood is employed for generation of the path only in traversable region. Its solution is obtained using the particle swarm optimization method. Then, to follow the generated velocity and path commands, a well developed driving controller, commercially obtainable, is utilized. Finally, field tests are conducted to validate the performance of the proposed algorithm.