An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments

Chen, Shoufeng; Yang, Zhihua; Liu, Zhentao; Jin, Haojie

doi:10.1109/spac.2017.8304346

Cited by 21 publications

(10 citation statements)

References 8 publications

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“…A UAV requires a significant depth of local environment data to be able to independently evaluate the APF in its vicinity [37]. If a centralised UAV position-to-APF model is used instead [38], then an accurate APF representation of the environment or simulated APF data is required for the entire area of operation a priori. This relies heavily upon an additional appropriate communication mechanism for APF data exchange.…”

Section: Artificial Potential Fieldmentioning

confidence: 99%

“…Conversely where a UAVs velocity is variable, both its time and distance coverage attributes themselves become optimisation variables within the planning problem. Whilst this approach was only identified within 17.6% of the papers surveyed, those papers that did were consistent in focusing upon bio-inspired algorithms [68,84,97] along with the application of an artificial potential field [113] to the environment. Such strategies facilitate the UAV's velocity to be influenced based upon its environmental surroundings, ultimately presenting a more realistic approach to how a UAV would operate, and a more concurrent representation of a UAV's overall flight dynamics problem.…”

Section: Time Considerationsmentioning

confidence: 99%

“…Encountered within this survey are two distinct approaches to samplingbased planning by which the approach can be either passive or active in nature. Active planning approaches, such as RRT [34] and APF [38], can independently achieve a feasible path solution through their own path processing abilities. Contrasting with passive planning approaches, the generation of a Voronoi diagram [29] or probabilistic roadmap [33] provides only a framework of feasible paths, from which a supplementary search algorithm is required to define a suitable path.…”

Section: Uav Path-planning Approachesmentioning

confidence: 99%

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Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

2023

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Unmanned aerial vehicles (UAVs) have the potential to make a significant impact in a range of scenarios where it is too risky or too costly to rely on human labour. Fleets of autonomous UAVs, which complete tasks collaboratively while managing their basic flight and related tasks independently, present further opportunities along with research and regulatory challenges. Improvements in UAV construction and components, along with developments in embedded computing hardware, communication mechanisms and sensors which may be mounted on-board a UAV are nearing the point where commercial deployment of fleets of autonomous UAVs will be technically possible. To fulfil this potential, UAVs will need to operate safely and reliably in complex and potentially dynamically changing environments with path-planning, obstacle sensing and collision avoidance paramount. This survey presents an original environment complexity classification, and critically analyses the current state of the art in relation to UAV path-planning approaches. Moreover, it highlights the existing challenges in environment complexity modelling and representation, path-planning approaches, and outlines open research questions together with future directions.

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Section: Artificial Potential Fieldmentioning

confidence: 99%

Section: Time Considerationsmentioning

confidence: 99%

Section: Uav Path-planning Approachesmentioning

confidence: 99%

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Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

2023

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“…The improved artificial potential field algorithm proposed in [Che+17] resolves potential collisions in dynamic environments by introducing repulsive fields based on relative distance modified directional coordination forces which alter the trajectory of the agent. The authors state that the method has a 98% probability of success.…”

Section: Collision Avoidancementioning

confidence: 99%

Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field

Ivić¹,

Sikirica²,

Crnković³

2021

Preprint

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Heat Equation Driven Area Coverage (HEDAC) is a state-of-the-art multi-agent ergodic motion control guided by a gradient of a potential field. A finite element method is hereby implemented to obtain a solution of Helmholtz partial di erential equation, which models the potential field for surveying motion control. This allows us to survey arbitrarily shaped domains and to include obstacles in an elegant and robust manner intrinsic to HEDAC's fundamental idea. For a simple kinematic motion, the obstacles and boundary avoidance constraints are successfully handled by directing the agent motion with the gradient of the potential. However, including additional constraints, such as the minimal clearance dsitance from stationary and moving obstacles and the minimal path curvature radius, requires further alternations of the control algorithm. We introduce a relatively simple yet robust approach for handling these constraints by formulating a straightforward optimization problem based on collision-free escapes route maneuvers. This approach provides a guaranteed collision avoidance mechanism, while being computationally inexpensive as a result of the optimization problem partitioning. The proposed motion control is evaluated in three realistic surveying scenarios simulations, showing the e ectiveness of the surveying and the robustness of the control algorithm. Furthermore, potential maneuvering di culties due to improperly defined surveying scenarios are highlighted and we provide guidelines on how to overpass them. The results are promising and indiacate real-world applicability of proposed constrained multi-agent motion control for autonomous surveying and potentially other HEDAC utilizations.

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“…Besides, some scholars also added a new force into UAVs for escaping local minima in potential fields. Such as, a new repulsion field whose directional coordination force is coupled with the relative distance between the UAV and the target was constructed in Chen et al (2018). Wang et al (2020) used a collision prediction model (CPM) with APF to overcome the dynamic obstacle environment, which is similar to Song et al (2020) that resolved the local minimum through a predictive APF.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

Fang

Cheng

et al. 2022

Transactions of the Institute of Measurement and Control

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To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.

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An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments

Cited by 21 publications

References 8 publications

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field

Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

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