scholarly journals A Distributed Strategy for Cooperative Autonomous Robots Using Pedestrian Behavior for Multi-Target Search in the Unknown Environment

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1606
Author(s):  
Haiyun Shi ◽  
Jie Li ◽  
Zhi Li
Keyword(s):  
Autonomous Robots ◽  
Search Problem ◽  
Collision Rate ◽  
Pedestrian Behavior ◽  
Target Search ◽  
Cooperative Strategies ◽  
Swarm Robots ◽  
Initial Location ◽  
Population Sizes ◽  
Central Coordination

Searching multiple targets with swarm robots is a realistic and significant problem. The goal is to search the targets in the minimum time while avoiding collisions with other robots. In this paper, inspired by pedestrian behavior, swarm robotic pedestrian behavior (SRPB) was proposed. It considered many realistic constraints in the multi-target search problem, including limited communication range, limited working time, unknown sources, unknown extrema, the arbitrary initial location of robots, non-oriented search, and no central coordination. The performance of different cooperative strategies was evaluated in terms of average time to find the first, the half, and the last source, the number of located sources and the collision rate. Several experiments with different target signals, fixed initial location, arbitrary initial location, different population sizes, and the different number of targets were implemented. It was demonstrated by numerous experiments that SRPB had excellent stability, quick source seeking, a high number of located sources, and a low collision rate in various search strategies.

scholarly journals Systematic Literature Review of Swarm Robotics Strategies Applied to Target Search Problem with Environment Constraints

2021 ◽  
Vol 11 (5) ◽  
pp. 2383
Author(s):  
Zool Hilmi Ismail ◽  
Mohd Ghazali Mohd Hamami
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Swarm Robotics ◽  
Empty Space ◽  
Difficult Problem ◽  
Search Problem ◽  
Target Search ◽  
Swarm Robots ◽  
Collaboration And Communication ◽  
Target Searching

Target searching is a well-known but difficult problem in many research domains, including computational intelligence, swarm intelligence, and robotics. The main goal is to search for the targets within the specific boundary with the minimum time that is required and the obstacle avoidance that has been equipped in place. Swarm robotics (SR) is an extension of the multi-robot system that particularly discovers a concept of coordination, collaboration, and communication among a large number of robots. Because the robots are collaborating and working together, the task that is given will be completed faster compared to using a single robot. Thus, searching for single or multiple targets with swarm robots is a significant and realistic approach. Robustness, flexibility, and scalability, which are supported by distributed sensing, also make the swarm robots strategy suitable for target searching problems in real-world applications. The purpose of this article is to deliver a systematic literature review of SR strategies that are applied to target search problems, so as to show which are being explored in the fields as well as the performance of current state-of-the-art SR approaches. This review extracts data from four scientific databases and filters with two established high-indexed databases (Scopus and Web of Science). Notably, 25 selected articles fell under two main categories in environment complexity, namely empty space and cluttered. There are four strategies which have been compiled for both empty space and cluttered categories, namely, bio-inspired mechanism, behavior-based mechanism, random strategy mechanism, and hybrid mechanism.

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.

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

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 Escalation of Memory Length in Finite Populations

2017 ◽  
Author(s):  
Kyle Harrington ◽  
Jordan Pollack
Keyword(s):  
Computational Simulations ◽  
Finite Populations ◽  
Arms Races ◽  
Memory Length ◽  
Satisfactory Degree ◽  
Cooperative Strategies ◽  
Population Sizes ◽  
Strategic Complexity ◽  
Counter Argument

AbstractThe escalation of complexity is a commonly cited benefit of coevolutionary systems, but computational simulations generally fail to demonstrate this capacity to a satisfactory degree. We draw on a macroevolutionary theory of escalation to develop a set of criteria for coevolutionary systems to exhibit escalation of strategic complexity. By expanding on a previously developed model of the evolution of memory length for cooperative strategies by Kristian Lindgren, we resolve previously observed limitations to the escalation of memory length. We present long-term coevolutionary simulations showing that larger population sizes tend to support greater escalation of complexity than smaller population sizes. Additionally, escalation is sensitive to perturbation during transitions of complexity. In whole, a long-standing counter-argument to the ubiquitous nature of coevolution is resolved, suggesting that the escalation of coevolutionary arms races can be harnessed by computational simulations.

A stigmergetic method based on vector pheromone for target search with swarm robots

2019 ◽  
Vol 32 (3) ◽  
pp. 533-555
Author(s):  
Qirong Tang ◽  
Fangchao Yu ◽  
Yuan Zhang ◽  
Jingtao Zhang
Keyword(s):  
Target Search ◽  
Swarm Robots

scholarly journals Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

2021 ◽  
Vol 11 (16) ◽  
pp. 7358
Author(s):  
Linlin Li ◽  
Shufang Xu ◽  
Hua Nie ◽  
Yingchi Mao ◽  
Shun Yu
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Chaotic Map ◽  
Poor Performance ◽  
Population Diversity ◽  
Convergence Speed ◽  
Local Optimum ◽  
Search Problem ◽  
Target Search ◽  
Disturbance Factor

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.

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