Learning‐based model predictive control for improved mobile robot path following using Gaussian processes and feedback linearization

Wang, Jie; Fader, Michael T. H.; Marshall, Joshua A.

doi:10.1002/rob.22165

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…The trajectory planning problems in the plan-based control paradigm, in general, are solved adhering to these steps: path search 52 , initial trajectory generation and trajectory refinement 53 , high-level control command generation that can be achieved by several approaches, including differential flatness mapping 54 , receding horizon planning, and finally low-level control commands generation using a flight controller, for example, PX4, DJI. These flight controllers operate independently irrespective of high-level planners.…”

Section: Discussionmentioning

confidence: 99%

Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles

Kulathunga,

Hamed,

Klimchik

2024

Sci Rep

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This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an example scenario. We focus on minimising residual dynamics that arise due to only the kinematical modelling of high-level planning. The kinematical modelling is sufficient for such scenarios due to safety constraints and aggressive manoeuvres that are difficult to perform when the environment is cluttered and dynamic. We used a simplified motion model to represent the motion of the quadrotor when formulating the high-level planner. The Sparse Gaussian Process Regression-based technique is proposed to model the residual dynamics. Recently proposed Data-Driven MPC is targeting aggressive manoeuvres without considering obstacle constraints. The proposed technique is compared with Data-Driven MPC to estimate the residual dynamics error without considering obstacle constraints. The comparison results yield that the proposed technique helps to reduce the nominal model error by a factor of 2 on average. Further, the proposed complete framework was compared with four other trajectory-tracking approaches in terms of tracking the reference trajectory without colliding with obstacles. The proposed approach outperformed the others with less flight time without losing computational efficiency.

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Section: Discussionmentioning

confidence: 99%

Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles

Kulathunga,

Hamed,

Klimchik

2024

Sci Rep

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show abstract

“…The trajectory planning problems in the plan-based control paradigm, in general, are solved adhering to these steps: path search (Wang et al, 2023), initial trajectory generation and trajectory refinement (Liu et al, 2022), high-level control command generation that can be achieved by several approaches, including differential flatness mapping (Talke et al, 2022), receding horizon planning, and finally low-level control commands generation using a flight controller, for example, PX4, DJI. These flight controllers operate independently irrespective of high-level planners.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

Kulathunga

Hamed

Devitt

et al. 2022

IEEE Robot. Autom. Lett.

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This paper presents a technique to cope with the gap between high-level planning, e.g., reference trajectory tracking, and low-level controlling using a learning-based method in the plan-based control paradigm. The technique improves the smoothness of maneuvering through cluttered environments, especially targeting low-speed velocity profiles. In such a profile, external aerodynamic effects that are applied on the quadrotor can be neglected. Hence, we used a simplified motion model to represent the motion of the quadrotor when formulating the Nonlinear Model Predictive Control (NMPC)-based local planner. However, the simplified motion model causes residual dynamics between the high-level planner and the low-level controller. The Sparse Gaussian Process Regression-based technique is proposed to reduce these residual dynamics. The proposed technique is compared with Data-Driven MPC. The comparison results yield that an augmented residual dynamics model-based planner helps to reduce the nominal model error by a factor of 2 on average. Further, we compared the proposed complete framework with four other approaches. The proposed approach outperformed the others in terms of tracking the reference trajectory without colliding with obstacles with less flight time without losing computational efficiency.

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“…• The problem of proving stability and robustness in bound state and some non-linear systems. MPC has been studied in several documented literature for the control of MRs [13], [32]. However, in most studies, the constraints are not considered.…”

Section: Introductionmentioning

confidence: 99%

A Constrained Fuzzy Control for Robotic Systems

Xue,

Zhou,

Chen

et al. 2024

IEEE Access

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The use of wheeled mobile robots (MRs) with symmetrical structure in engineering is rapidly increasing, with applications in various fields, such as industry, agriculture, forestry, healthcare, mining, rehabilitation, search and rescue, household tasks, remote locations, and entertainment. As MRs become more common, researchers are focusing on developing better ways to model and control these robots to improve their performance and adaptability. The main challenges in this area include uncertain dynamics, non-holonomic constraints, and various perturbations, which complicate the design of the control system. This paper presents a new predictive control scheme for MRs that is independent of the dynamics and the robot's working environment. A Type-3 fuzzy logic system is developed to identify the MR dynamics online. The designed predictive scheme improves accuracy and speeds up convergence, while also addressing uncertainties and considering constraints on control input. Additionally, a chaotic-based system is proposed for secure path planning, generating a complex and unpredictable reference trajectory that is useful for patrol MR applications. The effectiveness of the suggested controller is demonstrated through simulations and experiments.

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Learning‐based model predictive control for improved mobile robot path following using Gaussian processes and feedback linearization

Cited by 13 publications

References 31 publications

Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles

Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

A Constrained Fuzzy Control for Robotic Systems

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