Flocking Control of Mobile Robots with Obstacle Avoidance Based on Simulated Annealing Algorithm

Cheng, Jianhua; Wang, Bin

doi:10.1155/2020/7357464

Cited by 8 publications

(4 citation statements)

References 38 publications

(47 reference statements)

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“…The flocking behavior presents interesting characteristics that make it of high interest for the design of artificial systems, particularly in problems of localization, search and rescue. This type of behavior has been observed in birds, is similar to schooling fish and swarming insects, and is characterized by a joint movement of the group without central coordination [12], [13]. The first basic rules of this dynamic were established in 1987 as alignment, cohesion, and separation [14].…”

Section: Introductionmentioning

confidence: 85%

A Software Framework for Self-Organized Flocking System Motion Coordination Research

Sarmiento¹,

Ariza²,

Jacinto³

2022

IJACSA

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We describe and analyze the basic algorithms for the self-organization of a swarm of robots in coordinated motion as a flock of agents as a strategy for the solution of multi-agent tasks. This analysis allows us to postulate a simulation framework for such systems based on the behavioral rules that characterize the dynamics of these systems. The problem is approached from the perspective of autonomous navigation in an unknown but restricted and locally observable environment. The simulation framework allows defining individually the characteristics of the basic behaviors identified as fundamental to show a flocking behavior, as well as the specific characteristics of the navigation environment. It also allows the incorporation of different path planning approaches to enable the system to navigate the environment for different strategies, both geometric and reactive. The basic behaviors modeled include safe wandering, following, aggregation, dispersion, and homing, which interact to generate flocking behavior, i.e., the swarm aggregates, reach a stable formation and move in an organized fashion toward the target point. The framework concept follows the principle of constrained target tracking, which allows the problem to be solved similarly as a small robot with limited computation would solve it. It is shown that the algorithm and the framework that implements it are robust to the defined constraints and manage to generate the flocking behavior while accomplishing the navigation task. These results provide key guidelines for the implementation of these algorithms on real platforms.

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Section: Introductionmentioning

confidence: 85%

A Software Framework for Self-Organized Flocking System Motion Coordination Research

Sarmiento¹,

Ariza²,

Jacinto³

2022

IJACSA

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“…With this definition, if either ] i > c (then c−] i < 0 and H(c−] i ) � 0) or ] i < -c (then c+] i < 0 and H(c+] i ) � 0), then the second summand vanishes as required. Now, we describe an algorithm for computing any robust estimator by using the Simulated Annealing (SA) method, a GO method that has recently found many successful applications in the field of geosciences, e.g., [25][26][27][28][29].…”

Section: Robust Estimation By Gomentioning

confidence: 99%

Global Optimization of Redescending Robust Estimators

Baselga

Klein

Suraci

et al. 2021

Mathematical Problems in Engineering

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Robust estimation has proved to be a valuable alternative to the least squares estimator for the cases where the dataset is contaminated with outliers. Many robust estimators have been designed to be minimally affected by the outlying observations and produce a good fit for the majority of the data. Among them, the redescending estimators have demonstrated the best estimation capabilities. It is little known, however, that the success of a robust estimation method depends not only on the robust estimator used but also on the way the estimator is computed. In the present paper, we show that for complicated cases, the predominant method of computing the robust estimator by means of an iteratively reweighted least squares scheme may result in a local optimum of significantly lower quality than the global optimum attainable by means of a global optimization method. Further, the sequential use of the proposed global robust estimation proves to successfully solve the problem of M-split estimation, that is, the determination of parameters of different functional models implicit in the data.

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“…Swarm behavior control for object transportation robots includes collective movements in which the robot swarm moves while maintaining either a pre-organized shape in a twodimensional [2] or three-dimensional space [3], or a flock, in which robots move while changing their arrangement to adapt to the velocities and positions of other robots [4,5]. These approaches typically control the robots using local interactions based on interrobot relative distances and orientations [6], global interactions based on a virtual leader robot [7], environmental geometry, and the positions of the robots [8].…”

Section: Introductionmentioning

confidence: 99%

Regular Tessellation-Based Collective Movement for a Robot Swarm with Varying Densities, Scales, and Shapes

Yamagishi¹,

Suzuki²

2022

Adv. sci. technol. eng. syst. j.

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In complex swarm robotic applications that perform different tasks such as transportation and observation, robot swarms should construct and maintain a formation to adapt and move as a single large-scale robot. For example, transportation and observation tasks require unique robot swarms with either high densities to support the weight of the transported objects or low densities to avoid overlapping field of views and avoid obstructions. Previous literature has not focused on structure optimization because swarming provides a largecollective capability. This paper proposes a leader-follower-controlled collective movement method by calculating direction and distance potentials between robots based on geometric constraints, constricting robot positioning along radial gradients around the leader robot according to these potentials. This paper demonstrates a robot swarm applying the proposed method while maintaining formations with different densities while moving and evaluates the robot swarm structure-maintaining performance.

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Flocking Control of Mobile Robots with Obstacle Avoidance Based on Simulated Annealing Algorithm

Cited by 8 publications

References 38 publications

A Software Framework for Self-Organized Flocking System Motion Coordination Research

A Software Framework for Self-Organized Flocking System Motion Coordination Research

Global Optimization of Redescending Robust Estimators

Regular Tessellation-Based Collective Movement for a Robot Swarm with Varying Densities, Scales, and Shapes

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