Constraint-Based Testing of An Industrial Multi-Robot Navigation System

Mühlbacher, Clemens; Steinbauer, Gerald; Reip, Michael; Gspandl, Stephan

doi:10.1109/aitest.2019.00015

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Over the past few decades, numerous methods for autonomous robot path planning have been proposed, such as deep reinforcement learning, [34][35][36][37] constraint-based, 38,39 graph-based models, [40][41][42] and cloud-based methodology. 43,44 The discovery of reinforcement learning (RL) has led to major advancing in neural network technology and multi-robot path planning.…”

Section: Introductionmentioning

confidence: 99%

“…34 To amplify the conventional search tactics of multi-robot systems in wide areas as well as the collaboration and navigation strategies, Bae et al developed a hybrid RL in combination with a convolution neural network algorithm. 35 Muhlbacher et al 38 set out to improve the safety of autonomous multi-robot systems in industrial setting by enhancing their path planning capabilities through constraint-based navigation system. To improve the resilience and autonomy application of multi-robot systems Notomista and Eqerstedt 39 developed a control coordinated constraint-driven navigation system for multi-robot application.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous multi-robot allocation and formation control for remote sensing in environmental exploration

Sellers¹,

Lei²,

Rogers³

et al. 2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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Multi-robot systems have distinct advantages over single-robot systems in terms of operating speed, dependability, and efficiency. They are commonly used for a variety of environmental exploration applications in conjunction with remote sensing. In this paper, a self-organizing map (SOM) neural network algorithm is developed for multirobot task allocation and formation control. Multi-robot task allocation involves controlling a swarm of mobile robots to achieve designated task locations with cooperation and coordination of each robot. This framework is applicable to autonomous remote sensing by a swarm of robots, improving the efficiency and reliability of remote sensing in large environmental exploration areas. In addition to global task allocation, it is also important to control the formation of multi-robots. Each robot has a moving speed, and its field of view describes the visual range perpendicular to the direction of movement. The proposed framework utilizes a particle swarm optimization algorithm to generate the shortest planned trajectory for the multi-robot system. Additionally, a path smoothing technique is used to reduce the overall path and stress on the robots. The simulation studies validate the effectiveness of the SOM-based task allocation and formation control. It can deal with complicated cases such as that the formation changes its geometrical shape to adapt to the new environment in exploration.

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