An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve

Song, Baoye; Wang, Zidong; Zou, Lei

doi:10.1016/j.asoc.2020.106960

Cited by 287 publications

(120 citation statements)

References 46 publications

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“…With the improvement of the mathematical theory, this problem can be solved using optimization technology. As the particle swarm optimization (PSO) algorithm is a good global optimization method using in the different optimization problems (Moradi et al, 2020;Song et al, 2021;Yadav & Anubhav, 2020;Zhang et al, 2021), a PSO algorithm is employed in this study to find the optimal parameters for different surrogate models with the k-fold cross-validation method (Zhou et al, 2017). The optimization step can be summarized as follows:…”

Section: Establishment Of the Surrogate Modelsmentioning

confidence: 99%

Research on the hull form optimization using the surrogate models

Zhang

Tezdogan²,

Zhang

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Section: Establishment Of the Surrogate Modelsmentioning

confidence: 99%

Research on the hull form optimization using the surrogate models

Zhang

Tezdogan²,

Zhang

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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“…However, PSO has never been used for path planning of UAVs for bushfire assessment in NSW Australia, which is a humble contribution of the current study. PSO is a heuristic method that starts its search process using an initial particle population [55][56][57]. Each particle represents a potential solution to the problem [58].…”

Section: Potential Tools and Techniques For Bushfires Managementmentioning

confidence: 99%

UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires

et al. 2021

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Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment.

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“…In recent years, there are numerous researches focused on this issue concerning intelligent methods. In many fields related to artificial intelligence, motion planning for multirobot systems (MRS) is undoubtedly one of the crucial topics that cover all kinds of applications based on swarm optimizers [15][16][17], including ant colony optimization (ACO) [16] and particle swarm optimization (PSO) [17], because of their effective ways to take the advantages of population information to enhance the overall solution quality and accelerate the convergence speed [15]. For example, ACO clustering with crowding mechanism and GA-based multitask scheduling methods were developed for drones safely flight in a specific airspace [18], two phases heuristic algorithm (TPHA) [19], an improved shuffled frog leaping algorithm (SFLA) [20] was embedded into the trajectory smooth path to determine an optimal subsequent position for each robot, and so on.…”

Section: Solving Algorithmsmentioning

confidence: 99%

Parallel Swarm Intelligent Motion Planning with Energy‐Balanced for Multirobot in Obstacle Environment

Zhao

Wang

2021

Wireless Communications and Mobile Computing

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Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly-shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy-balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO-AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A ∗ algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient q on the performance of the algorithm are also discussed in the experiments.

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An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve

Cited by 287 publications

References 46 publications

Research on the hull form optimization using the surrogate models

Research on the hull form optimization using the surrogate models

UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires

Parallel Swarm Intelligent Motion Planning with Energy‐Balanced for Multirobot in Obstacle Environment

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