scholarly journals Multi-Target Coordinated Search Algorithm for Swarm Robotics Considering Practical Constraints

2021 ◽  
Vol 15 ◽  
Author(s):  
You Zhou ◽  
Anhua Chen ◽  
Xinjie He ◽  
Xiaohui Bian
Keyword(s):  
Obstacle Avoidance ◽  
Search Algorithm ◽  
Robot Kinematics ◽  
Force Model ◽  
Target Search ◽  
Random Line ◽  
Swarm Robots ◽  
Distributed Communication ◽  
Swarm Robot ◽  
Practical Constraints

In order to deal with the multi-target search problems for swarm robots in unknown complex environments, a multi-target coordinated search algorithm for swarm robots considering practical constraints is proposed in this paper. Firstly, according to the target detection situation of swarm robots, an ideal search algorithm framework combining the strategy of roaming search and coordinated search is established. Secondly, based on the framework of the multi-target search algorithm, a simplified virtual force model is combined, which effectively overcomes the real-time obstacle avoidance problem in the target search of swarm robots. Finally, in order to solve the distributed communication problem in the multi-target search of swarm robots, a distributed neighborhood communication mechanism based on a time-varying characteristic swarm with a restricted random line of sight is proposed, and which is combined with the multi-target search framework. For the swarm robot kinematics, obstacle avoidance, and communication constraints of swarm robots, the proposed multi-target search strategy is more stable, efficient, and practical than the previous methods. The effectiveness of this proposed method is verified by numerical simulations.

scholarly journals On the Performance Analyses of a Modified Force Field Algorithm and Neural Network Approach for Obstacle Avoidance in Swarm Robotics

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Girish Balasubramanian ◽  
Senthil Arumugam Muthukumaraswamy ◽  
Xianwen Kong
Keyword(s):  
Neural Network ◽  
Force Field ◽  
Obstacle Avoidance ◽  
Swarm Robotics ◽  
Field Method ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Swarm Robots ◽  
Swarm Robot

AbstractObstacle avoidance is a major hurdle when implementing mobile robots and swarm robots. Swarm robots work in groups and therefore require an efficient and functional obstacle avoidance algorithm to stay collision free between themselves and their surroundings. This paper reviews previous research in obstacle avoidance implementation using the force field method (FFM), also known as potential field method (PFM) and a neutral network approach. Moreover, this paper aims to execute simulations using a modified force field algorithm and a neural network approach and compare them. The obtained results are analyzed to identify the performance characteristics and the time taken to perform tasks using a singular mobile robot against a swarm robot environment consisting of four and ten robots, respectively, in both simulation cases. Simulations showed that the algorithm was successful in navigating obstacles for both single and swarm robot environments. A single robot was found to take up to 340% longer to arrive at the required location compared to the first robot in the experiment. Moreover, it was found that the neural network approach showed ~ 27% improvement over the modified force field algorithm when it comes to cases where more than four robots are being used.

scholarly journals A Probabilistic Target Search Algorithm Based on Hierarchical Collaboration for Improving Rapidity of Drones

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2535 ◽  
Author(s):  
Il-Kyu Ha ◽  
You-Ze Cho
Keyword(s):  
High Altitude ◽  
Target Detection ◽  
Information Transfer ◽  
Search Algorithm ◽  
Search Time ◽  
Previous Method ◽  
Target Search ◽  
Low Altitude ◽  
Military Facilities ◽  
Different Altitudes

Finding a target quickly is one of the most important tasks in drone operations. In particular, rapid target detection is a critical issue for tasks such as finding rescue victims during the golden period, environmental monitoring, locating military facilities, and monitoring natural disasters. Therefore, in this study, an improved hierarchical probabilistic target search algorithm based on the collaboration of drones at different altitudes is proposed. This is a method for reducing the search time and search distance by improving the information transfer methods between high-altitude and low-altitude drones. Specifically, to improve the speed of target detection, a high-altitude drone first performs a search of a wide area. Then, when the probability of existence of the target is higher than a certain threshold, the search information is transmitted to a low-altitude drone which then performs a more detailed search in the identified area. This method takes full advantage of fast searching capabilities at high altitudes. In other words, it reduces the total time and travel distance required for searching by quickly searching a wide search area. Several drone collaboration scenarios that can be performed by two drones at different altitudes are described and compared to the proposed algorithm. Through simulations, the performances of the proposed algorithm and the cooperation scenarios are analyzed. It is demonstrated that methods utilizing hierarchical searches with drones are comparatively excellent and that the proposed algorithm is approximately 13% more effective than a previous method and much better compared to other scenarios.

Illustration of Centralized Command and Control for Flocking Behavior

2014 ◽  
Vol 5 (2) ◽  
pp. 1-22
Author(s):  
Sami Oweis ◽  
Subramaniam Ganesan ◽  
Ka C Cheok
Keyword(s):  
Obstacle Avoidance ◽  
Command And Control ◽  
Flow Chart ◽  
Controlled Systems ◽  
Swarm Robots ◽  
Group A ◽  
Swarming Behavior ◽  
And Control ◽  
Flocking Behavior ◽  
Ground Systems

Flocking is a term that describes the behavior of a group of birds (a “flock”) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems - ‘'Boids''- such as ground systems (robotic vehicles/ swarm robots). Each one of these systems collectively moves inside/outside of a building to reach a target. The flocking behavior is implemented on a server-based control, which processes each of the boids' properties e.g. position, speed & target. Subsequently, the server will assign the appropriate move to a specific boid. The calculated information will be used locally to control and direct the movements/flocking for each boid in the group. A simulation technique and detailed flow chart is presented. In addition to Reynolds three original rules for flocking, two other rules- targeting obstacle avoidance - are presented-. Our result shows that the obstacles' avoiding rule was utilized to ensure that the flock didn't collide with obstacles in each of the boids' paths.

scholarly journals Directional-Sensor Network Deployment Planning for Mobile-Target Search

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 82
Author(s):  
Shiraz Wasim ◽  
Zendai Kashino ◽  
Goldie Nejat ◽  
Beno Benhabib
Keyword(s):  
Sensor Network ◽  
Search Algorithm ◽  
Search Problem ◽  
Target Search ◽  
Physical Constraints ◽  
Sensing Model ◽  
Mobile Target ◽  
Deployment Planning ◽  
Directional Sensor Network ◽  
Directional Sensor

In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

scholarly journals Multitarget Search of Swarm Robots in Unknown Complex Environments

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
You Zhou ◽  
Anhua Chen ◽  
Hongqiang Zhang ◽  
Xin Zhang ◽  
Shaowu Zhou
Keyword(s):  
Closed Loop ◽  
Control Method ◽  
Force Model ◽  
Complex Environments ◽  
Target Response ◽  
Complex Environment ◽  
Multiple Targets ◽  
Virtual Force ◽  
Swarm Robots ◽  
Division Model

When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of subswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the probability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes target type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary to make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By decomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for individual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The simulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively reducing the collision conflicts among the robots, environment, and individuals.

Sharing Experience for Behavior Generation of Real Swarm Robot Systems Using Deep Reinforcement Learning

2019 ◽  
Vol 31 (4) ◽  
pp. 520-525 ◽  
Author(s):  
Toshiyuki Yasuda ◽  
Kazuhiro Ohkura ◽  
◽  
Keyword(s):  
Reinforcement Learning ◽  
Collective Behavior ◽  
Design Methodology ◽  
Learning Performance ◽  
Centralized Control ◽  
Robot System ◽  
Swarm Robots ◽  
Robot Systems ◽  
Swarm Robot ◽  
Typical Design

Swarm robotic systems (SRSs) are a type of multi-robot system in which robots operate without any form of centralized control. The typical design methodology for SRSs comprises a behavior-based approach, where the desired collective behavior is obtained manually by designing the behavior of individual robots in advance. In contrast, in an automatic design approach, a certain general methodology is adopted. This paper presents a deep reinforcement learning approach for collective behavior acquisition of SRSs. The swarm robots are expected to collect information in parallel and share their experience for accelerating their learning. We conducted real swarm robot experiments and evaluated the learning performance of the swarm in a scenario where the robots consecutively traveled between two landmarks.

Research on task allocation of multi-target search with swarm robots

2014 ◽  
Vol 7 (3) ◽  
pp. 297 ◽  
Author(s):  
Guo You Zhang ◽  
Jian Chao Zeng ◽  
Song Dong Xue
Keyword(s):  
Task Allocation ◽  
Target Search ◽  
Swarm Robots
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