Formation forming based low-complexity swarms with distributed processing for decision making and resource allocation

Yang, Jian; Wang, Xin; Bauer, Péter

doi:10.1109/icarcv.2016.7838561

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…It aims to achieve robust and scalable collaborative behavior through simple interaction between members or between members and the environment [2]. This field has been perceived to have the potential in applications of exploration in unknown environments [3], searching particular signal or feature [4], [5], military defense or disaster rescue, etc [6], [7]. Coordinated motion control in swarm robotics aims to ensure the coherence of members in space, i.e., the robots in a swarm perform coordinated movements to maintain spatial structures [8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Coordinated Motion Control for Swarm Robotics Based on Brain Storm Optimization

Yang

Shi

2021

2021 11th International Conference on Information Science and Technology (ICIST)

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Coordinated motion control in swarm robotics aims to ensure the coherence of members in space, i.e., the robots in a swarm perform coordinated movements to maintain spatial structures. This problem can be modeled as a tracking control problem, in which individuals in the swarm follow a target position with the consideration of specific relative distance or orientations. To keep the communication cost low, the PID controller can be utilized to achieve the leader-follower tracking control task without the information of leader velocities. However, the controller's parameters need to be optimized to adapt to situations changing, such as the different swarm population, the changing of the target to be followed, and the anti-collision demands, etc. In this letter, we apply a modified Brain Storm Optimization (BSO) algorithm to an incremental PID tracking controller to get the relatively optimal parameters adaptively for leader-follower formation control for swarm robotics. Simulation results show that the proposed method could reach the optimal parameters during robot movements. The flexibility and scalability are also validated, which ensures that the proposed method can adapt to different situations and be a good candidate for coordinated motion control for swarm robotics in more realistic scenarios.

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Section: Introductionmentioning

confidence: 99%

Adaptive Coordinated Motion Control for Swarm Robotics Based on Brain Storm Optimization

Yang

Shi

2021

2021 11th International Conference on Information Science and Technology (ICIST)

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“…Swarm robotics simulates the emergent behaviors of social insects or animals, such as bees, ants, birds, fish schools, wolves, and even humans [17]. Many works in the literature have shown that this kind of system can be applied to different aspects varies from space exploration to military inspection, from industrial maintenance to medical solutions [16,18]. Collaborative searching problem is one of the popular topics in this field.…”

Section: Introductionmentioning

confidence: 99%

Robotic Brain Storm Optimization: A Multi-target Collaborative Searching Paradigm for Swarm Robotics

Yang¹,

Shi²

2021

Preprint

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Swarm intelligence optimization algorithms can be adopted in swarm robotics for target searching tasks in a 2-D or 3-D space by treating the target signal strength as fitness values. Many current works in the literature have achieved good performance in single-target search problems. However, when there are multiple targets in an environment to be searched, many swarm intelligence-based methods may converge to specific locations prematurely, making it impossible to explore the environment further. The Brain Storm Optimization (BSO) algorithm imitates a group of humans in solving problems collectively. A series of guided searches can finally obtain a relatively optimal solution for particular optimization problems. Furthermore, with a suitable clustering operation, it has better multi-modal optimization performance, i.e., it can find multiple optima in the objective space. By matching the members in a robotic swarm to the individuals in the algorithm under both environments and robots constraints, this paper proposes a BSO-based collaborative searching framework for swarm robotics called Robotic BSO. The simulation results show that the proposed method can simulate the BSO's guided search characteristics and has an excellent prospect for multi-target searching problems for swarm robotics.

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“…It aims to achieve emergent collective behaviors through the limited perception ability of swarm members and simple interactions with surroundings or teammates. e robot members in the system generally have the characteristics of low complexity [4]. At the system level, it uses redundancy to ensure robustness with excellent scalability and economy.…”

Section: Introductionmentioning

confidence: 99%

Exploration Enhanced RPSO for Collaborative Multitarget Searching of Robotic Swarms

et al. 2020

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Particle Swarm Optimization (PSO) is an excellent population-based optimization algorithm. Meanwhile, because of its inspiration source and the velocity update feature, it is also widely used in the collaborative searching tasks for swarm robotics. One of the PSO-based models for robotic swarm searching tasks is Robotic PSO (RPSO). It adds additional items for obstacle avoidance into standard PSO and has been applied to many single-target search tasks. However, due to PSO’s global optimization characteristics, it is easy to converge to a specific position in the search space and lose the ability to explore further. When faced with the problem of multitarget searching, it may become inefficient or even invalid. This paper proposes an Exploration Enhanced Robotic PSO (E2RPSO) method for multitarget searching problems for robotic swarms. The proposed method modifies the third item in the RPSO as an additional attraction term. This item not only enables the robot to avoid collisions but also guides the swarm to search unexplored regions as much as possible. This operation increases the swarm’s task-specific (top-down) diversity, making the system cover a broader search area and avoid falling into local optimums. Besides, the aggregation degree and evolution speed factors are also included in determining the inertia weight of the proposed method, which adjusts the swarm’s internal (bottom-up) diversity dynamically. The comparison results show that this method can balance the relationship between exploration and exploitation well, which has the potential to be applied to multitarget searching scenarios.

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Formation forming based low-complexity swarms with distributed processing for decision making and resource allocation

Cited by 7 publications

References 9 publications

Adaptive Coordinated Motion Control for Swarm Robotics Based on Brain Storm Optimization

Adaptive Coordinated Motion Control for Swarm Robotics Based on Brain Storm Optimization

Robotic Brain Storm Optimization: A Multi-target Collaborative Searching Paradigm for Swarm Robotics

Exploration Enhanced RPSO for Collaborative Multitarget Searching of Robotic Swarms

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