UAV path planning with multiagent Ant Colony system approach

Calik, Seda Kazdal

doi:10.1109/siu.2016.7496013

Cited by 16 publications

(3 citation statements)

References 5 publications

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“…ACO is characterized by its focus on pheromone update state transfer and heuristic function construction. In UAVs' global optimal trajectory planning, ACO with a multi-agent structure is employed for modeling and path search [74]. Inspired by the foraging behavior of birds, PSO is frequently used for UAV path planning.…”

Section: Biologically Inspired Algorithmsmentioning

confidence: 99%

Research on Unmanned Aerial Vehicle Path Planning

Luo,

Tian,

Wang

2024

Drones

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As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents innovative path-planning algorithms designed explicitly for UAVs and categorizes them based on algorithmic and functional levels. Moreover, it comprehensively discusses the advantages, disadvantages, application challenges, and notable outcomes of each path-planning algorithm, aiming to examine their performance thoroughly. Additionally, this paper provides insights into future research directions for UAVs, intending to assist researchers in future explorations.

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Section: Biologically Inspired Algorithmsmentioning

confidence: 99%

Research on Unmanned Aerial Vehicle Path Planning

Luo,

Tian,

Wang

2024

Drones

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“…Many researchers have used traditional path planning algorithms (e.g., A* [6], RRT [7], and artificial potential field [8] algorithms) as well as intelligent optimization algorithms (e.g., particle swarm optimization [9,10], ant colony optimization [11], and genetic algorithms [12]) to achieve autonomous UAV navigation. However, these non-learning algorithms require global information and perfect action execution mechanisms to plan feasible paths in a given environment [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Avoid module uses obstacle penalty and collision penalty as reward functions. The reward function of the Pack network is collision penalty as well as (11), which in 𝑑 , denotes the return length of each distance sensor; it is used to measure the degree of openness of the environment in which the UAV is located. The output of the pack module is a two-dimensional one-hot vector for selecting whether to perform navigation action or obstacle avoidance action.…”

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confidence: 99%

Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning

Yin,

Wang,

Zheng

et al. 2024

Electronics

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Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces the difficulty of the algorithm. But the methods without adaptive control are not suitable in low-altitude environments with complex situations, generally suffering from weak obstacle avoidance. Some UAV navigation studies with adaptive flight only have weak obstacle avoidance capabilities. To address the problem of UAV navigation in low-altitude environments, we construct autonomous UAV navigation in 3D environments with adaptive control as a Markov decision process and propose a deep reinforcement learning algorithm. To solve the problem of weak obstacle avoidance, we creatively propose the guide attention method to make a UAV’s decision focus shift between the navigation task and obstacle avoidance task according to changes in the obstacle. We raise a novel velocity-constrained loss function and add it to the original actor loss to improve the UAV’s velocity control capability. Simulation experiment results demonstrate that our algorithm outperforms some of the state-of-the-art deep reinforcement learning algorithms performing UAV navigation tasks in a 3D environment and has outstanding performance in algorithm effectiveness, with the average reward increasing by 9.35, the success rate of navigation tasks increasing by 14%, and the collision rate decreasing by 14%.

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Bat-Inspired Biogeography-Based Optimization Algorithm for Smoothly UAV Track Planning Using Bezier Function

Chong

Yang

2021

Wireless and Satellite Systems

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UAV path planning with multiagent Ant Colony system approach

Cited by 16 publications

References 5 publications

Research on Unmanned Aerial Vehicle Path Planning

Research on Unmanned Aerial Vehicle Path Planning

Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning

Bat-Inspired Biogeography-Based Optimization Algorithm for Smoothly UAV Track Planning Using Bezier Function

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