Multi-robot collaborative radioactive source search based on particle fusion and adaptive step size

Ling, Mingrun; Huo, Jianwen; Moiseev, Georgy V.; Hu, Li; Xiao, Yue

doi:10.1016/j.anucene.2022.109104

Cited by 14 publications

(3 citation statements)

References 24 publications

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“…In order to reduce the complexity of the locating model in continuous space, the area 𝑆 is discretized and divided into a finite number of square grids with side length 𝐷 𝑙 , and the robot only measures at the center of the grid [18]. Let the length of the entire area be 𝐷 𝑥 , the width be 𝐷 𝑦 , the number of grids in the divided work area be 𝑛 𝑥 , 𝑛 𝑦 , the expressions are shown in eq.…”

Section: Discretization Modelmentioning

confidence: 99%

Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm

Zhao

Liu²,

Zhang³

et al. 2022

J. Inst.

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Radioactive source localization algorithms have been widely used in the detection of nuclear accident areas. But some shortcomings, such as complex algorithm structure, slow localization speed and poor accuracy, were obviously performed to affect mobile robot locating autonomously. In this paper, a potential alternative method was investigated to be a new usage of locating leaks, just via specifying the change of exposure rate. In this model, several key factors, such as gamma ray attenuation, scattering factor, travel angle guide, spatial discretization, etc., were taken into consideration, to demonstrate the effectiveness of the algorithm, which is appropriated in unknown areas of the radioactive waste repository. Since there are three factors with different contribution, such as position, quantity of the source and gamma ray energy, which considered to demonstrate its impact on success. So, a hybrid adaptive grey wolf algorithm (HAGWO) has been adopted and implemented to develop a novel rapid method of radioactive leak location. Three aspects, including the good point set initialization in population size, balanced convergence function, and self-adaptive greedy strategy for population update, were optimized and merged into the locating model. To investigate the effectiveness of the algorithm, results of HAGWO are compared with grey wolf algorithm (GWO), good point set initialization strategy GWO(GGWO) and adaptive head wolf strategy GWO (ALGWO) in convergence speed, accuracy, stability and positioning error of single and double leak points. It is observed that convergence speed is increased by 37.93 ± 2% at the highest; the convergence accuracy is increased by 92.42 ± 2% at the most; the stability is improved by 30% ∼ 50%. The positioning error of single leak point is within 1.08%, and the positioning error of double leak point is less than 8.90%. Besides, compared with GWO, GGWO and ALGWO, the single-point accuracy is improved by 1.36 percentage points (to GWO), and the double-point accuracy is improved by 40.35 percentage points (to ALGWO) at most. It is observed that HAGWO performs the best in locating leaks, with a faster convergence, stronger stability and more accuracy.

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Section: Discretization Modelmentioning

confidence: 99%

Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm

Zhao

Liu²,

Zhang³

et al. 2022

J. Inst.

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show abstract

“…Correspondingly, the swarm robots enable a group of robots to coordinate their actions and cooperate with each other without relying on a central controller or a global communication network. This feature makes swarm robots suitable for various applications that require scalability, robustness, and flexibility, such as target search [6][7][8], target handling [9][10][11], multiple subgroups collecting [12], multi-target trapping [13], group chase [14,15], invasion and defense [16,17], etc. The great advantages of swarm robots provide us with a new way to solve the TSC task.…”

Section: Introductionmentioning

confidence: 99%

Self-Organized Patchy Target Searching and Collecting with Heterogeneous Swarm Robots Based on Density Interactions

et al. 2023

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The issue of searching and collecting targets with patchy distribution in an unknown environment is a challenging task for multiple or swarm robots because the targets are unevenly dispersed in space, which makes the traditional solutions based on the idea of path planning and full spatial coverage very inefficient and time consuming. In this paper, by employing a novel framework of spatial-density-field-based interactions, a collective searching and collecting algorithm for heterogeneous swarm robots is proposed to solve the challenging issue in a self-organized manner. In our robotic system, two types of swarm robots, i.e., the searching robots and the collecting robots, are included. To start with, the searching robots conduct an environment exploration by means of formation movement with Levy flights; when the targets are detected by the searching robots, they spontaneously form a ring-shaped envelope to estimate the spatial distribution of targets. Then, a single robot is selected from the group to enter the patch and locates at the patch’s center to act as a guiding beacon. Subsequently, the collecting robots are recruited by the guiding beacon to gather the patch targets; they first form a ring-shaped envelope around the target patch and then push the scattered targets inward by using a spiral shrinking strategy; in this way, all targets eventually are stacked near the center of the target patch. With the cooperation of the searching robots and the collecting robots, our heterogeneous robotic system can operate autonomously as a coordinated group to complete the task of collecting targets in an unknown environment. Numerical simulations and real swarm robot experiments (up to 20 robots are used) show that the proposed algorithm is feasible and effective, and it can be extended to search and collect different types of targets with patchy distribution.

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“…Huo et al employed a partially observable Markov decision process (POMDP) approach to enable autonomous robots to search for unknown radioactive sources [11] . Ling et al introduced a collaborative search technique for unknown radioactive sources using multi-robot information fusion and an adaptive step-size free energy strategy [12] . In many practical scenarios, multi-robot systems face challenges in search tasks due to complex and limited feasible solutions.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot cooperative search for radioactive sources based on particle swarm optimization particle filter

Luo,

Huo,

Liu

et al. 2023

Intelligence & Robotics

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Effective management and monitoring of radioactive sources are crucial to ensuring nuclear safety, human health, and the ecological environment. A multi-robot collaborative radioactive source search algorithm based on particle swarm optimization particle filters is proposed. In this algorithm, each robot operates as a mobile observation platform using the latest observations to fuse into particle sampling. At the same time, the particle swarm optimization algorithm moves the particle set to a high-likelihood area to overcome particle degradation. In addition, each particle can learn from the search history of other particles to speed up the convergence of the algorithm. Lastly, the Dynamic Window Approach (DWA) for dynamic window obstacle avoidance is used to avoid obstacles in complex mountainous terrains to achieve efficient source search. Experimental results show that the search success rate of the proposed algorithm is as high as 95%, and its average search time is only 3.43 s.

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Multi-robot collaborative radioactive source search based on particle fusion and adaptive step size

Cited by 14 publications

References 24 publications

Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm

Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm

Self-Organized Patchy Target Searching and Collecting with Heterogeneous Swarm Robots Based on Density Interactions

Multi-robot cooperative search for radioactive sources based on particle swarm optimization particle filter

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