Novel Motion Models for Time-Optimal Risk-Aware Motion Planning for Variable-Speed AUVs

Wilson, James P.; Mittal, Khushboo; Gupta, Savita

doi:10.23919/oceans40490.2019.8962644

Cited by 7 publications

(7 citation statements)

References 23 publications

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“…Informed subsets [31] will also be incorporated into this non-uniform sampling framework to provide an even faster convergence rate. Finally, this work will be extended to provide these nonuniform sampling benefits to multi-speed non-holonomic vehicles where both travel time and collision risk [32] are considered in the cost [33], [20].…”

Section: Discussionmentioning

confidence: 99%

A Non-uniform Sampling Approach for Fast and Efficient Path Planning

Wilson¹,

Shen²,

Gupta³

2021

Preprint

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In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. The approach uses a novel non-uniform partitioning scheme that divides the area into obstacle-free convex cells. The partitioning results in large cells in obstacle-free areas and small cells in obstacle-dense areas. Subsequently, the boundaries of these cells are used for sampling; thus significantly reducing the burden of uniform sampling. When compared with a standard uniform sampler, this smart sampler significantly 1) reduces the size of the sampling space while providing completeness and optimality guarantee, 2) provides sparse sampling in obstacle-free regions and dense sampling in obstacle-rich regions to facilitate faster exploration, and 3) eliminates the need for expensive collision-checking with obstacles due to the convexity of the cells. This sampling framework is incorporated into the RRT* path planner. The results show that RRT* with the non-uniform sampler gives a significantly better convergence rate and smaller memory footprint as compared to RRT* with a uniform sampler.

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Section: Discussionmentioning

confidence: 99%

A Non-uniform Sampling Approach for Fast and Efficient Path Planning

Wilson¹,

Shen²,

Gupta³

2021

Preprint

Self Cite

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“…This necessitated to form look-up tables to connect neighboring vehicle states in a grid to mitigate the high computation costs. Thus, these prior motion models To the best of our knowledge, our recently developed GMDM [23] is the only kinodynamic motion model that generates high-quality multi-speed trajectories connecting any two states with a computational complexity on par with the Dubins models. This model enables the use of intermediate speeds, which lets the vehicle to select speed from a set of speeds depending on the distance to the obstacle to avoid risk, and hence enable the generation of time-risk optimal paths.…”

Section: T -Lite Algorithmmentioning

confidence: 99%

“…The recently developed GMDM [23] is a fundamental extension of the Dubins motion-model that enables the selection of any feasible speed for any of the three segments of the Dubins paths (i.e., L, S, or R). This extension gives multi-speed autonomous vehicles better maneuverability by controlling the speed, and thus turning radius, to provide faster and safer paths.…”

Section: ) Generalized Multi-speed Dubins Motion Modelmentioning

confidence: 99%

“…With these motion primitives, the system of equations that characterize six GMDM path types (LSL, LSR, RSL, RSR, LRL, RLR) can be obtained, and the solutions to these equations are directly solved for. Further details of the model are provided in [23].…”

Section: ) Generalized Multi-speed Dubins Motion Modelmentioning

confidence: 99%

“…An ideal desired motion model would enable: i) better maneuverability by controlling the vehicle speed and thus turning radius, ii) risk mitigation by selecting speeds and headings based on obstacle proximity, and iii) fast computation for real-time application. To the best of our knowledge, the only motion model with these features is our recently developed Generalized Multi-speed Dubins Motion-model (GMDM) for variable-speed vehicles [23]. Unlike other models, GMDM achieves superior time-risk costs in T while having a low computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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T$^{\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles

Wilson,

Shen,

Gupta

et al. 2020

Preprint

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In this paper, we develop a new algorithm, called T -Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The T -Lite algorithm is a significantly faster version of the previously developed T algorithm. T -Lite uses the novel time-risk cost function of T ; however, instead of a grid-based approach, it uses an asymptotically optimal sampling-based motion planner. Furthermore, it utilizes the recently developed Generalized Multi-speed Dubins Motion-model (GMDM) for sample-to-sample kinodynamic motion planning. The sample-based approach and GMDM significantly reduce the computational burden of T while providing reasonable solution quality. The sample points are drawn from a four-dimensional configuration space consisting of two position coordinates plus vehicle heading and speed. Specifically, T -Lite enables the motion planner to select the vehicle speed and direction based on its proximity to the obstacle to generate faster and safer paths. In this paper, T -Lite is developed using the RRT * motion planner, but adaptation to other motion planners is straightforward and depends on the needs of the planner.

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Collision-free Coverage Path Planning for the Variable-speed Curvature-constrained Robot

Shi

Jin

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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Novel Motion Models for Time-Optimal Risk-Aware Motion Planning for Variable-Speed AUVs

Cited by 7 publications

References 23 publications

A Non-uniform Sampling Approach for Fast and Efficient Path Planning

A Non-uniform Sampling Approach for Fast and Efficient Path Planning

T$^{\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles

Collision-free Coverage Path Planning for the Variable-speed Curvature-constrained Robot

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