Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs

Goricanec, Jurica; Milas, Ana; Markovic, Lovro; Bogdan, Stjepan

doi:10.1109/access.2023.3303109

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…At present, in the field of path planning research, scholars from various countries are concentrating on improving the efficiency, completeness, and optimality of search paths. The achievements made are mainly divided into graph search algorithms [2][3][4][5][6][7], fluid/potential field algorithms [8][9][10][11][12][13][14], heuristic algorithms [15][16][17][18][19], and artificial intelligence learning algorithms. Graph search is the most effective search method for finding the shortest path in the static road network.…”

Section: Introductionmentioning

confidence: 99%

A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density

Yang,

2024

Drones

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Path planning is one of the most essential parts of autonomous navigation. Most existing works are based on the strategy of adjusting angles for planning. However, drones are susceptible to collisions in environments with densely distributed and high-speed obstacles, which poses a serious threat to flight safety. To handle this challenge, we propose a new method based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density (MSAO2H). Firstly, we propose to extend the obstacle avoidance decisions of drones into angle adjustment, speed adjustment, and obstacle clearance. Hybrid action space is adopted to model each decision. Secondly, the state space of the obstacle environment is constructed to provide effective features for learning decision parameters. The instant reward and the ultimate reward are designed to balance the learning efficiency of decision parameters and the ability to explore optimal solutions. Finally, we innovatively introduced the interferometric fluid dynamics system into the parameterized deep Q-network to guide the learning of angle parameters. Compared with other algorithms, the proposed model has high success rates and generates high-quality planned paths. It can meet the requirements for autonomously planning high-quality paths in densely dynamic obstacle environments.

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Section: Introductionmentioning

confidence: 99%

A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density

Yang,

2024

Drones

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Koopman‐based 3‐dimensional path following control for robotic flexible needles

Zhang,

Qi,

Chen

et al. 2024

Optim Control Appl Methods

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Robotic flexible needles have gained significant attention in minimally invasive medical procedures due to their ability to navigate complex anatomical structures and reach targets with high precision. Addressing the complex control problem in a 3‐dimensional environment, a Koopman‐based data‐driven control strategy is proposed in this paper. First, a 3‐dimensional (3D) path tracking problem is modeled using the simplified high‐dimensional bicycle model with the puncture kinematic. Then, with the Koopman operator theory, a finite linear approximation is designed and trained to simplify the nonlinear system of flexible needles. Finally, based on the linear approximation, a Koopman‐based model predictive control (MPC) scheme is proposed to realize 3‐dimensional path tracking for flexible needles. Based on simulations, the linear approximation and data‐driven control strategy are validated.

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Route planning of mobile robot based on improved RRT star and TEB algorithm

Yin,

Dong,

Wang

et al. 2024

Sci Rep

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This paper presents a fusion algorithm based on the enhanced RRT* TEB algorithm. The enhanced RRT* algorithm is utilized for generating an optimal global path. Firstly, proposing an adaptive sampling function and extending node bias to accelerate global path generation and mitigate local optimality. Secondly, eliminating path redundancy to minimize path length. Thirdly, imposing constraints on the turning angle of the path to enhance path smoothness. Conducting kinematic modeling of the mobile robot and optimizing the TEB algorithm to align the trajectory with the mobile robot's kinematics. The integration of these two algorithms culminates in the development of a fusion algorithm. Simulation and experimental results demonstrate that, in contrast to the traditional RRT* algorithm, the enhanced RRT* algorithm achieves a 5.8% reduction in path length and a 62.5% decrease in the number of turning points. Utilizing the fusion algorithm for path planning, the mobile robot generates a superior, seamlessly smooth global path, adept at circumventing obstacles. Furthermore, the local trajectory meticulously conforms to the kinematic constraints of the mobile robot.

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Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs

Cited by 5 publications

References 40 publications

A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density

A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density

Koopman‐based 3‐dimensional path following control for robotic flexible needles

Route planning of mobile robot based on improved RRT star and TEB algorithm

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