Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)

Kulathunga, Geesara; Devitt, Dmitry; Fedorenko, Roman; Савин, Сергей; Klimchik, Alexandr

doi:10.20537/nd210410

Cited by 5 publications

(8 citation statements)

References 31 publications

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“…These generated trajectories, in general, do not guarantee dynamic feasibility. Kinodynamic-based trajectory planning 16 , 17 addressed these issues through kinodynamic search, whilst ensuing dynamic feasibility. Despite addressing such issues, kinodynamic-based trajectory planning remains a highly computational footprint.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

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

Kulathunga,

Hamed,

Klimchik

2024

Sci Rep

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“…However, it is essential that the generation of an optimal control policy ensures dynamic feasibility. Thus, in [97,98], LQR was incorporated into path planning, by which both dynamic feasibility and local optimality were guaranteed. However, local optimality does not necessarily yield global optimality [99].…”

Section: Disturbance Estimationmentioning

confidence: 99%

Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm

Kulathunga,

Klimchik

2023

Remote Sensing

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In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favor of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning, both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforementioned target metrics more efficiently compared to other approaches, such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyze related literature in order to understand how the motion planning problem, especially the trajectory planning problem, is formulated when being applied for generating optimal trajectories in real-time for multirotor aerial vehicles, as well as how it impacts the listed metrics. As a result of this research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline.

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“…These generated trajectories, in general, do not guarantee dynamic feasibility. Kinodynamic-based trajectory planning (Ding et al, 2019), (Kulathunga et al, 2021) addressed these issues through kinodynamic search, whilst ensuing dynamic feasibility. Despite addressing such issues, kinodynamic-based trajectory planning remains a highly computational footprint.…”

Section: Related Workmentioning

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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Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)

Cited by 5 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

Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm

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

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