Optimization of Dynamic Obstacle Avoidance Path of Multirotor UAV Based on Ant Colony Algorithm

Yang, Yuexin; Chen, Zhuoxun

doi:10.1155/2022/1299434

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…Through this, the depth camera acquires the depth image, and the left and right infrared cameras measure the depth [9][10][11][12]. The middle infrared dot matrix projector is equivalent to a fill light, and the RGB camera on the right is used to collect color pictures, which can align the color video stream with the depth stream [13].…”

Section: D435i Modulementioning

confidence: 99%

Obstacle Avoidance Design of Aerial Robot Combining A star algorithm Algorithm and Optical Dynamic Capture Method

Shi

Cai²,

Yuan³

2023

J. Phys.: Conf. Ser.

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The traditional A star obstacle avoidance algorithm is based on binocular cameras for indoor positioning, which requires rich obstacle feature points and has the risk of explosion in a single indoor environment. In this paper, the method of merging the A star algorithm and optical dynamic capture is proposed. By pasting Marker points on key moving parts, the actual position of the aerial robot is calculated with the optical dynamic capture system, and compared with the target position planned by the A star algorithm and corrected in real time during a flight. Through the experimental analysis of the original acceleration of the aerial robot and different position between the target point and the actual point in the flight process, the stability and flight accuracy of the aerial robot in the flight process can be significantly improved, which has good practical application value.

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Section: D435i Modulementioning

confidence: 99%

Obstacle Avoidance Design of Aerial Robot Combining A star algorithm Algorithm and Optical Dynamic Capture Method

Shi

Cai²,

Yuan³

2023

J. Phys.: Conf. Ser.

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show abstract

“…If the environmental information that needs to be collected is relatively complex, the time for establishing this map is quite long. Efficiently generating robot motion environment maps has become an urgent problem to be solved [38,39]. a real environment map by taking photos, process the real environment map, transform non-standard environment maps into robot path planning maps suitable for robot path planning, and then carry out path planning and pose planning for robots, this can greatly improve the efficiency of robot path planning.…”

Section: Realistic Issuesmentioning

confidence: 99%

Non-Standard Map Robot Path Planning Approach Based on Ant Colony Algorithms

Li,

Kim,

2023

Sensors

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Robot path planning is an important component of ensuring the robots complete work tasks effectively. Nowadays, most maps used for robot path planning obtain relevant coordinate information through sensor measurement, establish a map model based on coordinate information, and then carry out path planning for the robot, which is time-consuming and labor-intensive. To solve this problem, a method of robot path planning based on ant colony algorithms after the standardized design of non-standard map grids such as photos was studied. This method combines the robot grid map modeling with image processing, bringing in calibration objects. By converting non-standard actual environment maps into standard grid maps, this method was made suitable for robot motion path planning on non-standard maps of different types and sizes. After obtaining the planned path and pose, the robot motion path planning map under the non-standard map was obtained by combining the planned path and pose with the non-standard real environment map. The experimental results showed that this method has a high adaptability to robot non-standard map motion planning, can realize robot path planning under non-standard real environment maps, and can make the obtained robot motion path display more intuitive and convenient.

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“…Compared with traditional graph search algorithms, swarm intelligence algorithms can handle incomplete information in complex environments, easily ensure path continuity, and are more suitable for path planning in 3D space. Common swarm intelligence algorithms include the ant colony algorithm [ 17 ], particle swarm optimization algorithm [ 18 ], and artificial bee colony algorithm [ 19 ]. Although these swarm intelligence algorithms can be used to generate the flight path of plant protection UAVs in 3D space, they have many disadvantages, such as uncertain convergence speed and long planning time, and they are not suitable for application scenarios with high real-time requirements.…”

Section: Introductionmentioning

confidence: 99%

Bionic 3D Path Planning for Plant Protection UAVs Based on Swarm Intelligence Algorithms and Krill Swarm Behavior

Xu,

Zhu,

Sun

2024

Biomimetics

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The protection of plants in mountainous and hilly areas differs from that in plain areas due to the complex terrain, which divides the work plot into many narrow plots. When designing the path planning method for plant protection UAVs, it is important to consider the generality in different working environments. To address issues such as poor path optimization, long operation time, and excessive iterations required by traditional swarm intelligence algorithms, this paper proposes a bionic three-dimensional path planning algorithm for plant protection UAVs. This algorithm aims to plan safe and optimal flight paths between work plots obstructed by multiple obstacle areas. Inspired by krill group behavior and based on group intelligence algorithm theory, the bionic three-dimensional path planning algorithm consists of three states: “foraging behavior”, “avoiding enemy behavior”, and “cruising behavior”. The current position information of the UAV in the working environment is used to switch between these states, and the optimal path is found after several iterations, which realizes the adaptive global and local convergence of the track planning, and improves the convergence speed and accuracy of the algorithm. The optimal flight path is obtained by smoothing using a third-order B-spline curve. Three sets of comparative simulation experiments are designed to verify the performance of this proposed algorithm. The results show that the bionic swarm intelligence algorithm based on krill swarm behavior reduces the path length by 1.1~17.5%, the operation time by 27.56~75.15%, the path energy consumption by 13.91~27.35%, and the number of iterations by 46~75% compared with the existing algorithms. The proposed algorithm can shorten the distance of the planned path more effectively, improve the real-time performance, and reduce the energy consumption.

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Optimization of Dynamic Obstacle Avoidance Path of Multirotor UAV Based on Ant Colony Algorithm

Cited by 9 publications

References 24 publications

Obstacle Avoidance Design of Aerial Robot Combining A star algorithm Algorithm and Optical Dynamic Capture Method

Obstacle Avoidance Design of Aerial Robot Combining A star algorithm Algorithm and Optical Dynamic Capture Method

Non-Standard Map Robot Path Planning Approach Based on Ant Colony Algorithms

Bionic 3D Path Planning for Plant Protection UAVs Based on Swarm Intelligence Algorithms and Krill Swarm Behavior

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