Real-Time Long Range Trajectory Replanning for MAVs in the Presence of Dynamic Obstacles

Kulathunga, Geesara; Fedorenko, Roman; Kopylov, Sergey; Klimehik, Alexandr

doi:10.1109/acirs49895.2020.9162605

“…Trajectory generation can be carried out in different ways, including path planning followed by smoothing and trajectory planning. The authors of (Kulathunga et al, 2020) proposed a technique for path planning using Rapidly-Exploring-Random-Trees(RRT*) (Karaman and Frazzoli, 2011) and further smoothing the path by B-spline for a quadrotor. In (Yang and Sukkarieh, 2010), continuous curvature-based path-smoothing technique is proposed for fixed-wind aerial vehicles.…”

Section: Trajectory Generationmentioning

confidence: 99%

Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

Kulathunga

¹

,

Devitt

²

,

Klimchik

³

2021

Preprint

Self Cite

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We present an optimization-based reference trajectory tracking method for quadrotor robots for slow-speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy to Linear Quadratic Gaussian (LQG) in which Nonlinear Model Predictive Control (NMPC) is employed for predicting optimal control policy in each iteration. Multiple-shooting (MS) is suggested over Direct-collocation (DC) for imposing constraints when modelling the NMPC. Incremental Euclidean Distance Transformation Map (EDTM) is constructed for obtaining the closest free distances relative to the predicted trajectory; these distances are considered obstacle constraints. The reference trajectory is generated, ensuring dynamic feasibility. The objective is to minimize the error between the quadrotor’s current pose and the desired reference trajectory pose in each iteration. Finally, we evaluated the proposed method with two other approaches and showed that our proposal is better than those two in terms of reaching the goal without any collision. Additionally, we published a new dataset, which can be used for evaluating the performance of trajectory tracking algorithms.

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“…where r x = max(4.0, r x ), r y = max(4.0, r y ) and r z = max(4.0, r z ); the values of r x , r y , and r z have been set to 4.0 m at max, assuming that the maximum area which covers the search space is sufficient to have 4.0 m at max in each direction. Once r is obtained, the search space orientation is rotated around r. For the procedure of obtaining such a rotation matrix R, we have used a method that is proposed in [32].…”

Section: Path Planning Followed By Kinodynamic Smoothing For Multirotor Aerial Vehicles 497mentioning

confidence: 99%

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

Kulathunga

¹

,

Devitt

²

,

Fedorenko

³

et al. 2021

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Any obstacle-free path planning algorithm, in general, gives a sequence of waypoints that connect start and goal positions by a sequence of straight lines, which does not ensure the smoothness and the dynamic feasibility to maneuver the MAV. Kinodynamic-based motion planning is one of the ways to impose dynamic feasibility in planning. However, kinodynamic motion planning is not an optimal solution due to high computational demands for real-time applications. Thus, we explore path planning followed by kinodynamic smoothing while ensuring the dynamic feasibility of MAV. The main difference in the proposed technique is not to use kinodynamic planning when finding a feasible path, but rather to apply kinodynamic smoothing along the obtained feasible path. We have chosen a geometric-based path planning algorithm “RRT*” as the path finding algorithm. In the proposed technique, we modified the original RRT* introducing an adaptive search space and a steering function that helps to increase the consistency of the planner. Moreover, we propose a multiple RRT* that generates a set of desired paths. The optimal path from the generated paths is selected based on a cost function. Afterwards, we apply kinodynamic smoothing that will result in a dynamically feasible as well as obstacle-free path. Thereafter, a b-spline-based trajectory is generated to maneuver the vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments. According to the experiment results, we were able to speed up the path planning task by 1.3 times when using the proposed multiple RRT* over the original RRT*.

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“…Trajectory generation can be carried out in different ways, including path planning followed by smoothing and trajectory planning. The authors of 9 proposed a technique for path planning using RRT* 10 and further smoothing the path by B-spline for a quadrotor. In 11 , continuous curvature-based path-smoothing technique is proposed for fixed-wind aerial vehicles.…”

Section: Trajectory Generationmentioning

confidence: 99%

Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

Kulathunga

¹

,

Devitt

²

,

Klimchik

³

2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

We present an optimization-based reference trajectory tracking method for quadrotor robots for slow-speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy to Linear Quadratic Gaussian (LQG) in which Nonlinear Model Predictive Control (NMPC) is employed for predicting optimal control policy in each iteration. Multiple-shooting (MS) is suggested over Direct-collocation (DC) for imposing constraints when modelling the NMPC. Incremental Euclidean Distance Transformation Map (EDTM) is constructed for obtaining the closest free distances relative to the predicted trajectory; these distances are considered obstacle constraints. The reference trajectory is generated, ensuring dynamic feasibility. The objective is to minimize the error between the quadrotor’s current pose and the desired reference trajectory pose in each iteration. Finally, we evaluated the proposed method with two other approaches and showed that our proposal is better than those two in terms of reaching the goal without any collision. Additionally, we published a new dataset, which can be used for evaluating the performance of trajectory tracking algorithms.

show abstract

Real-Time Long Range Trajectory Replanning for MAVs in the Presence of Dynamic Obstacles

Cited by 10 publications

References 18 publications

Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

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

Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

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