Collision-free path planning based on a genetic algorithm for quadrotor UAVs

Gutierrez-Martinez, Manuel A.; Rojo-Rodriguez, Erik G.; Cabriales-Ramirez, Luis E.; Reyes, Luis A.; Castillo, Pedro; Garcia‐Salazar, Octavio

doi:10.1109/icuas48674.2020.9213956

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…[90] used the optimal solution obtained by the genetic algorithm to initialize the ant colony pheromone matrix in order to improve the convergence speed of UAV trajectory planning. [91] proposed a genetic algorithm capable of generating waypoints and achieving obstacle avoidance considering the minimum turning radius.…”

Section: Methods Based On Swarm Intelligencementioning

confidence: 99%

A Survey of Indoor UAV Obstacle Avoidance Research

Xiong

Yan

et al. 2023

IEEE Access

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UAV obstacle avoidance technology is one of the key factors to realize UAV autonomous flight, efficient and accurate obstacle avoidance is significant to complete the UAV autonomous flight task. In contrast, the dynamic, real-time and uncertainty of the environment in which the UAV is located makes the problem very tricky, especially in the indoor environment. At present, a large number of scholars have shown strong interest in the indoor UAV obstacle avoidance problem. With the rapid development of computer technology and hardware devices, many intelligent algorithms have been proposed to solve the obstacle avoidance problem. However, the research on indoor UAV obstacle avoidance technology is not comprehensive enough, and there is a lack of summarization of the research results in recent years. This paper introduces the sensor modules commonly used for indoor UAV environment sensing, related obstacle avoidance methods based on sensory detection. Classifies and composes the commonly used UAV path planning obstacle avoidance algorithms, and gives several representative UAV flight control research methods. This paper summarizes the advantages and disadvantages of different perception modules and detection methods applied to UAV obstacle avoidance tasks, and compares various current path planning methods. Finally, the critical difficulties and challenges faced in the field of indoor UAV obstacle avoidance are discussed, and future research in the field of UAV obstacle avoidance has prospected.

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Section: Methods Based On Swarm Intelligencementioning

confidence: 99%

A Survey of Indoor UAV Obstacle Avoidance Research

Xiong

Yan

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Tao et al [9] studied the AGV path planning problem of the single production line in the intelligent manufacturing workshop for the materials required by the AGV transportation machine tool, established a mathematical model with the shortest transportation time as the objective function, and proposed an improved particle swarm optimization algorithm (IPSO) to solve the optimal path. Martinez et al [10] proposed a genetic algorithm that can generate navigation waypoints, realize short distance and avoid collision with obstacles. Genetic algorithm uses multiobjective function to obtain waypoints; These functions are the length of the path, the distance from the waypoints to the obstacles, and the probability of the final trajectory to cross an obstacle within a safe zone.…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the other hand, the traditional path planning mainly considers the shortest distance between the starting point and the target point for path planning [5], [6], [7], [8], [9], [10], [11], [12], [13], and completely ignores the remaining power of the robot and the service area after charging. This method plans the most suitable charging pile according to the robot's position on the map, the remaining power and the service area after charging.…”

Section: Introductionmentioning

confidence: 99%

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Deng

Xie

et al. 2023

IEEE Access

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Autonomous mobile service robots are used to complete many tasks, such as cleaning, transporting goods and monitoring. Such tasks usually require uninterrupted and continuous service. However, the battery of the robot is limited and must be charged frequently. For a large number of robots, it is essential to select a suitable charging pile. For this issue, we propose a path planning model for robots to intelligently access a limited number of charging piles distributed on the map. The traditional path planning model mainly considers the shortest path criterion to generate the path. Different from this, the path planning model in this paper not only considers the shortest path, but also the service position of the robot after charging, the remaining power of the robot, the state of the charging pile and the position of the robot in the map. Our path planning assigns the most suitable charging pile to the robot that needs charging. To solve the problem of high memory consumption and slow search speed when traditional A* algorithm is used for path planning, we propose local memorial path planning (LMPP) algorithm to quickly generate effective paths. The simulation results show that the proposed robot charging path planner can improve the robot service satisfaction and plan the effective path to the available charging piles.INDEX TERMS Autonomous mobile robot, service robot, path planning, robot charging

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“…Hence the proposed method was implemented in environments with barriers as obstacles, avoiding colliding with the walls and generating safe trajectories. In [12] and [13], authors showed that bio-inspired and evolutionary methods such as ant colony and GA solve the path planning problem for UAVs by creating efficient trajectories to avoid collisions. In [14], a GA was designed to solve complex maze-like environments through sequences of movements.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm for path planning of UAVs as a maze-solving problem

Gutierrez-Martinez

Cabriales-Ramirez

Rojo-Rodriguez

et al. 2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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This research work addresses the problem of path planning for unmanned aerial vehicles (UAVs) in complex environments such as a maze. A genetic algorithm (GA) with variable chromosomes and gene change conditions is designed when the decision point criteria and a collision with a wall are presented; without considering large chromosomes. Our proposed GA obtains the sequence of minimum movements required to solve the maze. Then, the trajectories are generated by high-order polynomials. Finally, numerical simulations and real-time tests are carried out to validate the proposed algorithm.

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Collision-free path planning based on a genetic algorithm for quadrotor UAVs

Cited by 7 publications

References 19 publications

A Survey of Indoor UAV Obstacle Avoidance Research

A Survey of Indoor UAV Obstacle Avoidance Research

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Genetic algorithm for path planning of UAVs as a maze-solving problem

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