Decentralized behavior-based formation control of multiple robots considering obstacle avoidance

Lee, Giroung; Chwa, Dongkyoung

doi:10.1007/s11370-017-0240-y

Cited by 197 publications

(72 citation statements)

References 30 publications

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“…The main control methods of multi-robot formation are shown in Figure 1. They mainly include the Leader-Follower method [12], [13], the Behavior-based method [14], [15], and the Artificial Potential Method [16], [17], Virtual Structure method [18], [19], Graph Theory method [20] and so on. Among them, pilot-following method, behavior-based method and artificial potential field method are suitable for distributed control, while the virtual structure method and graph theory method are suitable for centralized control.…”

Section: A Multi-robot Formation Control Methodsmentioning

confidence: 99%

“…Figure 12 shows the cooperative control route of multiple robot formation, among which ellipse, triangle and rectangle represent obstacles. During the process of robot team from (−8, −15) to (8,15), the shortest route principle is adopted, and the robot needs to avoid obstacles and change the distance between robots. It can be seen from the figure that the robot team can perfectly avoid obstacles and reach the designated destination by changing the route and shape of the team.…”

Section: Simulation Of Robot Formation Control Based On Internet mentioning

confidence: 99%

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Robot Formation Control Based on Internet of Things Technology Platform

et al. 2020

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The cooperative control technology of robot formation can sense all kinds of external environment in real time. It is a multi-functional control and management system including visual recognition, task management execution and distribution, behavior decision-making and so on. It can easily adapt to all kinds of harsh environment. In order to meet the efficient response requirements of robot formation control, a real-time transmission system of robot cooperative motion control is built based on the Internet of things platform, which collects and feeds back the trajectory of multiple robots. Through particle swarm optimization deep learning algorithm, more accurate identification, prediction and guidance of the robot's next action. Finally, the simulation of robot formation motion is established by MATLAB software, which verifies the feasibility of particle swarm optimization deep learning neural network algorithm under the Internet of things technology. Compared with the traditional robot formation control method, the optimized control method has faster convergence speed, smaller error and more accurate position, which provides method guidance for the accuracy and efficiency of robot formation control technology. INDEX TERMS Robot, formation cooperative control, Internet of things, particle swarm optimization, deep learning.

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Section: A Multi-robot Formation Control Methodsmentioning

confidence: 99%

Section: Simulation Of Robot Formation Control Based On Internet mentioning

confidence: 99%

Robot Formation Control Based on Internet of Things Technology Platform

et al. 2020

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“…One key example of the application of behaviour-based robotics is in NASA's Mars Exploration Rovers [40], robots that operate autonomously on the surface of Mars, using behaviour-based principles. Behaviour-based robotics research such as [41][42][43] forms a small but recognised part of the broader field of evolutionary robotics [44]. It also sits within Artificial Life and adaptive behaviour research e.g., [45][46][47].…”

Section: Behaviour-based Robotics After Intelligence Without Represenmentioning

confidence: 99%

Intelligence without Representation: A Historical Perspective

Jordanous¹

2020

Systems

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This paper reflects on a seminal work in the history of AI and representation: Rodney Brooks’ 1991 paper Intelligence without Representation. Brooks advocated the removal of explicit representations and engineered environments from the domain of his robotic intelligence experimentation, in favour of an evolutionary-inspired approach using layers of reactive behaviour that operated independently of each other. Brooks criticised the current progress in AI research and believed that removing complex representation from AI would help address problematic areas in modelling the mind. His belief was that we should develop artificial intelligence by being guided by the evolutionary development of our own intelligence and that his approach mirrored how our own intelligence functions. Thus, the field of behaviour-based robotics emerged. This paper offers a historical analysis of Brooks’ behaviour-based robotics approach and its impact on artificial intelligence and cognitive theory at the time, as well as on modern-day approaches to AI.

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“…Here, the formation control while avoiding obstacles was attained by implementing five different behaviours such as moving to the goal, avoiding obstacles, wall-following, avoiding robot, and formation keeping. Another article proposed a decentralized behaviour-based formation control and obstacle avoidance algorithm for multiple robots in which information exchange was considered only between neighbouring robots [64]. In this study, collision avoidance was obtained by determining the avoidance angle using the distance between obstacles as well as the formation generation behaviour being obtained by selecting the leader robot and determining the location in the formation using locations of other mobile robots and formation matrix.…”

Section: Motor Schema-based Controlmentioning

confidence: 99%

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Soni

2018

Robotics

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Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.

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Decentralized behavior-based formation control of multiple robots considering obstacle avoidance

Cited by 197 publications

References 30 publications

Robot Formation Control Based on Internet of Things Technology Platform

Robot Formation Control Based on Internet of Things Technology Platform

Intelligence without Representation: A Historical Perspective

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

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