A line-graph path planner for performance constrained fixed-wing UAVs in wind fields

Benders, Sebastian; Schopferer, Simon

doi:10.1109/icuas.2017.7991317

Cited by 23 publications

(3 citation statements)

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“…A* can fast converge to the ideal by utilising this directing-like strategy. In [21], it employs a collision-free A* in a windy and clustering environment, whereas [22] proposes a local optimum pathfinding method for UGVs and Unmanned Aerial Vehicles (UAVs).…”

Section: Related Workmentioning

confidence: 99%

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Khan¹,

Guivant²,

Li³

et al. 2023

Preprint

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<p>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.</p>

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Section: Related Workmentioning

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

show abstract

“…A* quickly approaches the ideal path due to its directional strategy. In [19] and [20], A* is used for local optimum pathfinding for UGVs and Unmanned Aerial Vehicles in a windy and clustered environment. Dynamic programming, as described by Bellman's principle of optimality, forms the basis for designing an optimal value function (also known as CTG function) for optimal control problems, as stated in [21].…”

Section: Related Workmentioning

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

<p>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.</p>

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“…Work regarding path planning in higher dimensions and complex cost spaces as well as easy access rules for the mentioned regulations are available [1]. Examples of path planning for aerial vehicles are [3,4]. Schopferer and Benders also worked on risk-based path planning using a sampling-based algorithm [5].…”

Section: Introductionmentioning

confidence: 99%