Design and experimental validation of a robust model predictive control for the optimal trajectory tracking of a small-scale autonomous bulldozer

Khan, Subhan; Guivant, José; Li, Xuesong

doi:10.1016/j.robot.2021.103903

Cited by 21 publications

(8 citation statements)

References 38 publications

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“…In tracking control of UGVs, the platforms are always subject to different internal and external disturbances. For this purpose, a robust model predictive control is proposed 121 to suppress the effects of uncertainties in [37], a disturbance 122 observers (DO) are used to estimate the external disturbances 123 and uncertainties [38,39]. A robust tracking scheme DO that 124 handles slip-skid is presented in [40].…”

Section: A Related Workmentioning

confidence: 99%

“…Additionally, only when e = 0 and e c = 0 289 is it known that the Lyapunov function V 1 ≥ 0 and V 1 = 0. 290 Taking the subsequent derivative now 291 V1 = 2k 1 (e x + l(1 − cos(e θ ))( ėx + l sin(e θ ) ėθ ) + 2k 1 (e y − l sin(e θ )( ėθ − l cos(e θ )+ 2k e v r sin(e θ ) ėθ + V2 (37) Let the function of Eq. ( 26) has an error e F = F − F , and controller of Eq.…”

Section: E Stability Analysismentioning

confidence: 99%

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Design and implementation of proximal planning and control of an unmanned ground vehicle in the dynamic environment

Khan¹,

Guivant²

2022

Preprint

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This paper presents a novel proximal planning and control (PPC) formulation for an unmanned ground vehicle (UGV) affected by skidding and slip disturbances. The control approach also considers the presence of moving and static obstacles in the context of operation. The PPC technique is divided into three parts; first, a nonlinear model predictive control (NMPC) based path-planner is designed to periodically generate an updated feasible trajectory to prevent the vehicle from colliding with other objects from start to the goal pose. In particular, a proximal averaged Newton-type method for optimal control (PANOC) is used to design NMPC. Second, evolutionary programming (EP) based kinematic control (KC) is designed to generate the velocity commands. Third, a dynamic control law is proposed with an extended state observer (ESO) to estimate the effects of bounded but unknown perturbations. Finally, simulations are performed in the presence of linear and nonlinear trajectories of moving obstacles (MO) and static obstacles (SO) to verify the performance of the proposed scheme. Additionally, we have investigated and confirmed that the proposed PPC could run in real-time on a CPU with limited resources.

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Section: A Related Workmentioning

confidence: 99%

Section: E Stability Analysismentioning

confidence: 99%

Design and implementation of proximal planning and control of an unmanned ground vehicle in the dynamic environment

Khan¹,

Guivant²

2022

Preprint

Self Cite

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“…For instance, a nonlinear MPC is used to accurately follow the patch generated by a high-level planner for moving debris in a construction context [6], planning and control of the hexapod robot for disaster rescue missions [7], collision avoidance mobile robot for preventing evacuees from colliding with rescue robot [8], and simultaneous localization and mapping (SLAM) has been used [9]. Additionally, UGVs are considered to suffer perturbasubhan.khan@sydney.edu.au (S. Khan); j.guivant@unsw.edu.au (J. Guivant) ORCID(s): 0000-0002-0979-3751 (S. Khan); 0000-0002-8556-8652 (J. Guivant) tion due to uncertainties in the models [10], skid-slip [11], and dynamic environments, which increases the complexity of the control and planning tasks [12,13], and robust MPC for optimal trajectory-tracking [14]. However, these tasks for UGVs are considered path-follower or trajectorytracking problems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Khan¹,

Guivant²,

Li³

et al. 2023

Preprint

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Autonomous vehicles can be used for evacuations, particularly for assisting disabled people; they can also be used for moving animals and goods. In those cases, the platform must deal with partially known infrastructure and other context characteristics, including dynamic obstacles. The destination may not be unique, as multiple safe destinations may be feasible. This paper proposes a hybrid model predictive control (HMPC) approach for performing trajectory optimization. The approach exploits the available global cost-to-go (CTG) function and a model of the platform dynamics, in addition to awareness information of the nearby context. The CTG function implicitly allows the selection from multiple destinations in the context of operation. First, the Global 2D CTG is generated and periodically updated at low frequency using, in this case, pseudo priority queues (PPQ) based Dijkstra algorithm, which can generate optimal CTG functions of large maps, requiring low processing cost. Secondly, an MPC process based on stochastic dynamic programming (SDP), running at high frequency, generates the actual control actions, in real-time, based on the last available CTG information, in combination with a model of the platform dynamics and the perceived description of the surrounding area, including static, dynamic objects, skid-slip effects. Finally, simulations are performed to validate the proposed planning and control scheme.

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“…Diverse strategies have been utilised in recent years for the planning and management of emergency preventive concerns. For example, a nonlinear MPC is used to precisely follow the patch generated by a high-level planner for moving debris in a construction context [6], planning and control of the hexapod robot for disaster rescue missions [7], collision avoidance mobile robot for preventing evacuees from colliding with rescue robot [8], and simultaneous localisation and mapping (In addition, UGVs are believed to be perturbed by model uncertainty [9], skid-slip [10], and dynamic environments, which increases the complexity of the control and planning tasks [11,12], and robust MPC for optisubhan.khan@sydney.edu.au (S. Khan); j.guivant@unsw.edu.au (J. Guivant) ORCID(s): 0000-0002-0979-3751 (S. Khan); 0000-0002-8556-8652 (J. Guivant) mal trajectory-tracking [13]. Nevertheless, these tasks for UGVs are path-following or trajectory-tracking challenges.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Khan¹,

Guivant²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

This paper presents an approach for optimizing trajectories of autonomous vehicles in evacuation scenarios, particularly for the transportation of handicapped individuals, animals, and products. First, the hybrid model predictive control (HMPC) technique takes into account partially known infrastructure and dynamic obstacles, and allows for multiple secure destinations. The approach utilizes a platform-specific dynamics model, a global cost-to-go (CTG) function, and contextual awareness. In addition, the CTG function is generated using a Dijkstra algorithm and pseudo priority queues (PPQ), and allows for efficient trajectory planning on large maps. Furthermore, the control actions are generated in real-time using a high-frequency MPC process based on stochastic dynamic programming (SDP), using the most recent CTG information and a model of the vehicle dynamics and the perceived environment, including static and dynamic objects and skid-slip effects. Finally, the effectiveness of the proposed technique is validated through simulations.

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Design and experimental validation of a robust model predictive control for the optimal trajectory tracking of a small-scale autonomous bulldozer

Cited by 21 publications

References 38 publications

Design and implementation of proximal planning and control of an unmanned ground vehicle in the dynamic environment

Design and implementation of proximal planning and control of an unmanned ground vehicle in the dynamic environment

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

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