scholarly journals Formation control for autonomous robots with collision and obstacle avoidance using a rotational and repulsive force–based approach

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984789 ◽  
Author(s):  
Anh Duc Dang ◽  
Hung Manh La ◽  
Thang Nguyen ◽  
Joachim Horn
Keyword(s):  
Force Field ◽  
Control Problem ◽  
Convergence Analysis ◽  
Formation Control ◽  
Autonomous Robots ◽  
Repulsive Force ◽  
New Combination ◽  
Moving Target ◽  
Numerical Examples ◽  
Control Scheme

In this article, we address a formation control problem for a group of autonomous robots to track a moving target in the presence of obstacles. In the proposed method, desired formations, which consist of virtual nodes arranged in specific shapes, are first generated. Then, autonomous robots are driven toward these virtual nodes without collisions with each other using a novel control scheme, which is based on artificial force fields. The convergence analysis is shown based on Lyapunov’s stability. The novelty of the proposed approach lies in a new combination of rotational force field and repulsive force field to design a mechanism so that robots can avoid and escape complex obstacle shapes. The effectiveness of the proposed method is illustrated with numerical examples using V-shape and circular shape formations.

scholarly journals Hybrid Bernstein Block-Pulse Functions Method for Second Kind Integral Equations with Convergence Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mohsen Alipour ◽  
Dumitru Baleanu ◽  
Fereshteh Babaei
Keyword(s):  
Integral Equation ◽  
Approximate Solution ◽  
Convergence Analysis ◽  
Bernstein Polynomials ◽  
Linear Equations ◽  
The Other ◽  
New Combination ◽  
System Of Linear Equations ◽  
Numerical Examples ◽  
Block Pulse Functions

We introduce a new combination of Bernstein polynomials (BPs) and Block-Pulse functions (BPFs) on the interval [0, 1]. These functions are suitable for finding an approximate solution of the second kind integral equation. We call this method Hybrid Bernstein Block-Pulse Functions Method (HBBPFM). This method is very simple such that an integral equation is reduced to a system of linear equations. On the other hand, convergence analysis for this method is discussed. The method is computationally very simple and attractive so that numerical examples illustrate the efficiency and accuracy of this method.

scholarly journals Robust Tracking Control of Mobile Robot Formation with Obstacle Avoidance

2007 ◽  
Vol 2007 ◽  
pp. 1-10 ◽  
Author(s):  
Tiantian Yang ◽  
Zhiyuan Liu ◽  
Hong Chen ◽  
Run Pei
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Formation Control ◽  
Optimization Problem ◽  
Reference Trajectory ◽  
Robust Tracking Control ◽  
Wheeled Mobile Robots ◽  
Lmi Optimization ◽  
Control Scheme

We consider the formation control problem of multiple wheeled mobile robots with parametric uncertainties and actuator saturations in the environment with obstacles. First, a nonconvex optimization problem is introduced to generate the collision-free trajectory. If the robots tracking along the reference trajectory find themselves moving close to the obstacles, a new collision-free trajectory is generated automatically by solving the optimization problem. Then, a distributed control scheme is proposed to keep the robots tracking the reference trajectory. For each interacting robot, optimal control problem is generated. And in the framework of LMI optimization, a distributed moving horizon control scheme is formulated as online solving each optimal control problem at each sampling time. Moreover, closed-loop properties inclusive of stability andH∞performance are discussed. Finally, simulation is performed to highlight the effectiveness of the proposed control law.

scholarly journals Navigational Protocols of Formation Control for Multiagent Network with and without Nonlinear Dynamics

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Shaolin Li ◽  
Yinghui He ◽  
Lili Zhang
Keyword(s):  
Nonlinear Dynamics ◽  
Control Problem ◽  
Lyapunov Stability ◽  
Stability Theory ◽  
Formation Control ◽  
Lyapunov Stability Theory ◽  
Numerical Examples ◽  
Current Location ◽  
Multiagent Networks ◽  
The Difference

Formation control problem for multiagent networks is investigated under the framework of leader-follower consensus. By utilizing the Lyapunov stability theory, two navigational protocols for multiagent network without and with nonlinear dynamics are derived to realize formation control, respectively. In order to achieve the expected formation, controller is adopted to each agent, and the design philosophies of control protocol are required to follow two rules: (i) the destinations of agent are required to be identified and communicating with each other through the network; (ii) at least one agent is needed to be navigator which can detect the difference between its current location and destination. Finally, the two numerical examples are provided to demonstrate the effectiveness of the proposed navigational protocols.

scholarly journals A Collective Control Scheme to Solve Time-varying Formation Control Problem of Multiple Autonomous Underwater Vehicles with Time Delay

2021 ◽  
Author(s):  
Yongnan Jia ◽  
Weicun Zhang
Keyword(s):  
Communication Network ◽  
Time Delay ◽  
Control Problem ◽  
Formation Control ◽  
Autonomous Underwater Vehicles ◽  
Evolutionary Process ◽  
Underwater Vehicles ◽  
Time Varying ◽  
Collective Control ◽  
Control Scheme

Abstract Due to the limitation of complexity and uncertainty of the underwater environment, the related technologies of autonomous underwater vehicles(AUVs) develop slowly. Therefore, an ingenious solution characterized by low cost, convenient operation, and low individual intelligence is urgently required. Inspired from these collective behaviours of gregarious creatures in nature, the coordination control problem of multiple AUVs is endowed with new research significance to complete complex underwater operational tasks. This paper aims to propose a general control scheme to solve the time-varying formation control problem of multiple AUVs that take into account the communication time delay. Firstly, a complete six-degrees-of-freedom dynamical model is applied instead of the real AUVs in the following theoretical analysis and simulation verification. Then, a metric-based nearest neighbour interacted rule is introduced to build the communication network of the system. Periodic sampling technology and zero-order hold loop are adopted to simplify the communication problem of time delay. Based on the above dynamical model and communication mechanism, a distributed collective control protocol is proposed to enable these AUVs asymptotically converge to a desired geometrical configuration on the condition that the initial communication network is undirected and connected. During the evolutionary process, no collision happens between any two AUVs. The formation configuration can be maintained until a simple switching controller works for the configuration transformation tasks. Finally, the simulation results proved the effectiveness of the above collective control scheme and visually exhibited the three-dimensional dynamical evolutionary process.

scholarly journals Reinforcement-Learning-Based Asynchronous Formation Control Scheme for Multiple Unmanned Surface Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 546
Author(s):  
Jiajia Xie ◽  
Rui Zhou ◽  
Yuan Liu ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Formation Control ◽  
Rapid Development ◽  
Gradient Algorithm ◽  
Robot System ◽  
Physical Relationship ◽  
Unmanned Surface Vehicles ◽  
Main Challenge ◽  
Control Scheme ◽  
Multi Robot

The high performance and efficiency of multiple unmanned surface vehicles (multi-USV) promote the further civilian and military applications of coordinated USV. As the basis of multiple USVs’ cooperative work, considerable attention has been spent on developing the decentralized formation control of the USV swarm. Formation control of multiple USV belongs to the geometric problems of a multi-robot system. The main challenge is the way to generate and maintain the formation of a multi-robot system. The rapid development of reinforcement learning provides us with a new solution to deal with these problems. In this paper, we introduce a decentralized structure of the multi-USV system and employ reinforcement learning to deal with the formation control of a multi-USV system in a leader–follower topology. Therefore, we propose an asynchronous decentralized formation control scheme based on reinforcement learning for multiple USVs. First, a simplified USV model is established. Simultaneously, the formation shape model is built to provide formation parameters and to describe the physical relationship between USVs. Second, the advantage deep deterministic policy gradient algorithm (ADDPG) is proposed. Third, formation generation policies and formation maintenance policies based on the ADDPG are proposed to form and maintain the given geometry structure of the team of USVs during movement. Moreover, three new reward functions are designed and utilized to promote policy learning. Finally, various experiments are conducted to validate the performance of the proposed formation control scheme. Simulation results and contrast experiments demonstrate the efficiency and stability of the formation control scheme.

scholarly journals Evaluating Life Cycle of Buildings Using an Integrated Approach Based on Quantitative-Qualitative and Simplified Best-Worst Methods (QQM-SBWM)

2021 ◽  
Vol 13 (8) ◽  
pp. 4487
Author(s):  
Maghsoud Amiri ◽  
Mohammad Hashemi-Tabatabaei ◽  
Mohammad Ghahremanloo ◽  
Mehdi Keshavarz-Ghorabaee ◽  
Edmundas Kazimieras Zavadskas ◽  
...  
Keyword(s):  
Life Cycle ◽  
Computer Software ◽  
Integrated Approach ◽  
Comfort Level ◽  
Combination Method ◽  
New Combination ◽  
Numerical Examples ◽  
Environmental Consequences ◽  
Time Required

Evaluating the life cycle of buildings is a valuable tool for assessing sustainability and analyzing environmental consequences throughout the construction operations of buildings. In this study, in order to determine the importance of building life cycle evaluation indicators, a new combination method was used based on a quantitative-qualitative method (QQM) and a simplified best-worst method (SBWM). The SBWM method was used because it simplifies BWM calculations and does not require solving complex mathematical models. Reducing the time required to perform calculations and eliminating the need for complicated computer software are among the advantages of the proposed method. The QQM method has also been used due to its ability to evaluate quantitative and qualitative criteria simultaneously. The feasibility and applicability of the SBWM were examined using three numerical examples and a case study, and the results were evaluated. The results of the case study showed that the criteria of the estimated cost, comfort level, and basic floor area were, in order, the most important criteria among the others. The results of the numerical examples and the case study showed that the proposed method had a lower total deviation (TD) compared to the basic BWM. Sensitivity analysis results also confirmed that the proposed approach has a high degree of robustness for ranking and weighting criteria.

scholarly journals UAV Formation Shape Control via Decentralized Markov Decision Processes

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 91
Author(s):  
Md Ali Azam ◽  
Hans D. Mittelmann ◽  
Shankarachary Ragi
Keyword(s):  
Control Problem ◽  
Shape Control ◽  
Formation Control ◽  
Three Dimensional ◽  
Geographical Region ◽  
Dynamic Programming Method ◽  
Theoretic Approach ◽  
Control Approach ◽  
Uav Swarm ◽  
Markov Decision

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms.

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