Potential Field Based Inter-UAV Collision Avoidance Using Virtual Target Relocation

Abeywickrama, Hasini Viranga; Jayawickrama, Beeshanga Abewardana; He, Ying; Dutkiewicz, Eryk

doi:10.1109/vtcspring.2018.8417773

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…As commonly done in the literature ( [10], [4]), a safety distance (L) is defined for the UAVs. When two or more UAVs are closer than the safety distance to each other, it is identified as a collision (Fig.…”

Section: ) Collision Detectionmentioning

confidence: 99%

See 1 more Smart Citation

A Reinforcement Learning Approach for Fair User Coverage Using UAV Mounted Base Stations Under Energy Constraints

Abeywickrama

Dutkiewicz

et al. 2020

IEEE Open J. Veh. Technol.

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Unmanned Aerial Vehicles (UAVs) are gaining popularity in many aspects of wireless communication systems. UAV-mounted mobile base stations (UAV-BSs) are an effective and costefficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. However, UAV-BSs have their limitations and complications, for instance, limited available energy. In addition, when several UAV-BSs are deployed to provide coverage to a specific area, the possibility of inter-UAV collisions and the interference to ground users increase. We propose Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) based methods to deploy UAV-BSs under energy constraints to provide efficient and fair coverage to the ground users, while minimising inter-UAV collisions and interference to ground users. The proposed methods outperform the baseline methods by an average increase of 38.94% in system fairness, 42.54% in individual user coverage, and 15.04% in total system coverage, in comparison with the baseline methods. INDEX TERMS Unmanned aerial vehicles (UAVs), wireless coverage, reinforcement learning.

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Section: ) Collision Detectionmentioning

confidence: 99%

“…In the proposed method, possible collisions are detected as mentioned above and UAV-BSs are prompted to move into hovering points that do not result in collisions. Collisions involving more than two UAV-BSs can be detected by breaking down the collision into multiple two-UAV collisions, as done in [10].…”

Section: ) Collision Detectionmentioning

confidence: 99%

A Reinforcement Learning Approach for Fair User Coverage Using UAV Mounted Base Stations Under Energy Constraints

Abeywickrama

Dutkiewicz

et al. 2020

IEEE Open J. Veh. Technol.

Self Cite

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“…Roberge et al proposed using genetic algorithms and particle swarm algorithms to solve autonomous UAV path planning problems in complex 3D environments, taking into account the width of the UAV and the optimal trajectory criterion in 3D environments to reduce the execution time of the solution [8] . Abeywickrama et al presented an artificial potential field model that demonstrates remarkable efficiency in reducing collisions among UAVs [9] . Vanegas et al proposed a method for optimizing 3D UAV path planning using a non-holonomic constraint path planning approach [10] .…”

Section: Introductionmentioning

confidence: 99%

UAV path planning based on a dual-strategy ant colony optimization algorithm

Mai,

Dong,

Liu

et al. 2023

Intelligence & Robotics

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With the rapid development of modern communication and automatic control technologies, unmanned aerial vehicles (UAVs) have increasingly gained importance in both military and civilian domains. Path planning, a critical aspect for achieving autonomous aerial navigation, has consistently been a focal point in UAV research. However, traditional ant colony algorithms need to be improved for the drawbacks of susceptibility to local optima and weak convergence capabilities. Consequently, a novel path planning methodology is proposed based on a dual-strategy ant colony algorithm. In detail, an improved state transition probability rule is introduced, redefining ant movement rules by integrating the state transition strategy of deterministic selection during the iterative process. Additionally, heuristic information on adjacent node distance and mountain height is added to further improve the search efficiency of the algorithm. Then, a new dynamically adjusted pheromone update strategy is proposed. The update strategy is continuously adjusted during the iteration process, which is beneficial to the algorithm’s global search in the early stage and accelerated convergence in the later stage, preventing the algorithm from falling into local optimality and improving its convergence. Based on the above improvements, a new variation of ant colony optimization (ACO) called dual-strategy ACO algorithm is formed. Experimental results prove that dual-strategy ACO has superior global search capabilities and convergence characteristics from four key aspects: path length, fitness values, iteration number, and running time.

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“…In recent years, various approaches have been put forward for solving this complex optimization problem, such as A* algorithm [ 2 ], rapidly exploring random trees [ 3 ], potential field based method (PFM) [ 4 ], genetic algorithm (GA) [ 5 ], linear programming [ 6 ], and artificial intelligence guidance [ 7 ]. These methods can be classified as deterministic algorithms and non-deterministic algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

Liu

Shu

Liang

et al. 2021

Sensors

154

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The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area. A highly efficient route planning approach is required for this complex high dimensional optimization problem. However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment. In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem. Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem. Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm. Finally, the Cauchy–Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation. The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration.

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Potential Field Based Inter-UAV Collision Avoidance Using Virtual Target Relocation

Cited by 7 publications

References 10 publications

A Reinforcement Learning Approach for Fair User Coverage Using UAV Mounted Base Stations Under Energy Constraints

A Reinforcement Learning Approach for Fair User Coverage Using UAV Mounted Base Stations Under Energy Constraints

UAV path planning based on a dual-strategy ant colony optimization algorithm

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

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