Inquiry-Based Learning With RoboGen: An Open-Source Software and Hardware Platform for Robotics and Artificial Intelligence

Auerbach, Joshua E.; Concordel, Alice; Kornatowski, Przemyslaw; Floreano, Dario

doi:10.1109/tlt.2018.2833111

Cited by 34 publications

(23 citation statements)

References 32 publications

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“…Which means that students do not have to deal with hardware details or use C/C++ programming language to develop complex programs. In [9] the authors present RoboGen, an open-source and low-cost platform to study a variety of topics including computer programming, artificial intelligence, evolutionary algorithms, etc. This solution is used in the master's level course at the Ecole Polytechnique Federale de Lausanne (EPFL).…”

Section: Introductionmentioning

confidence: 99%

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

et al. 2019

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The use of mobile robots for teaching automatic control is becoming more popular in engineering curricula. Currently, many robot simulators with high-graphical capabilities can be easily used by instructors to teach control engineering. However, the use of real robots is not as straightforward as simulations. There are many hardware and software details that must be considered before applying control. This paper presents the development of an easy-to-use platform for teaching control of mobile robots. The laboratory has been carefully designed to conceal all technical issues, such as communications or the localization that do not address the fundamental concepts of control engineering. To this end, a position sensor based on computer vision has been developed to provide the positions of the robots on the platform in real time. The Khepera IV robot has been selected for this platform because of its flexibility and advanced built-in sensors but the laboratory could be easily adapted for similar robots. The platform offers the opportunity to perform laboratory practices to test many different control strategies within a real experimental multi-agent environment. A methodology for using the platform in the lab is also provided.INDEX TERMS Robotics education, mobile robot laboratory, vision-based indoor positioning sensor.

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Section: Introductionmentioning

confidence: 99%

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

et al. 2019

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“…The use and application of open source tools and software for different learning scenarios and topics (for example Auerbach et al, 2018;Dean et al, 2019) seem to be a relevant topic within education. The need to manage and use open data is addressed in our literature sample as well.…”

Section: Search Syntaxmentioning

confidence: 99%

Special Issue: Engaging with Open Science in Learning and Teaching

Bossu

Heck

2020

EFI

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“…By co-evolving morphology and controller on a hardware-reconfigurable robot, we can expect to perform a broader range of missions in more challenging scenarios than if just controller tuning were considered (Nygaard et al, 2018b). The relatively few previous studies on the simultaneous optimization of legged robot morphology and control have mostly performed the optimization in simulated environments (Nygaard et al, 2017;Auerbach et al, 2019;Miras and Eiben, 2019;Hornby et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the inherent difficulties associated with making a variable-morphology robot, most research to date on adaptive morphology on physical robots does the optimization ahead of time in simulation, then transfers a select few individuals to the real world for testing. Examples of this include legged robots (Nygaard et al, 2017), soft robots (Kriegman et al, 2019a), modular robots (Auerbach et al, 2019), and even flying robots (Rosser et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Environmental Adaptation of Robot Morphology and Control Through Real-World Evolution

Nygaard

Martín

Howard

et al. 2021

Evolutionary Computation

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Robots operating in the real world will experience a range of different environments and tasks. It is essential for the robot to have the ability to adapt to its surroundings to work efficiently in changing conditions. Evolutionary robotics aims to solve this by optimizing both the control and body (morphology) of a robot, allowing adaptation to internal, as well as external factors. Most work in this field has been done in physics simulators, which are relatively simple and not able to replicate the richness of interactions found in the real world. Solutions that rely on the complex interplay between control, body, and environment are therefore rarely found. In this paper, we rely solely on real-world evaluations and apply evolutionary search to yield combinations of morphology and control for our mechanically self-reconfiguring quadruped robot. We evolve solutions on two distinct physical surfaces and analyze the results in terms of both control and morphology. We then transition to two previously unseen surfaces to demonstrate the generality of our method. We find that the evolutionary search finds high-performing and diverse morphology-controller configurations by adapting both control and body to the different properties of the physical environments. We additionally find that morphology and control vary with statistical significance between the environments. Moreover, we observe that our method allows for morphology and control parameters to transfer to previously-unseen terrains, demonstrating the generality of our approach.

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Inquiry-Based Learning With RoboGen: An Open-Source Software and Hardware Platform for Robotics and Artificial Intelligence

Cited by 34 publications

References 32 publications

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

Special Issue: Engaging with Open Science in Learning and Teaching

Environmental Adaptation of Robot Morphology and Control Through Real-World Evolution

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