Open Issues in Evolutionary Robotics

Silva, Fernando; Duarte, Miguel; Correia, Luís; Oliveira, Sancho; Christensen, Anders Lyhne

doi:10.1162/evco_a_00172

Cited by 86 publications

(63 citation statements)

References 158 publications

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“…Here, the number and speed of evaluations is only limited by the access to computational power, and thus such experiments can be performed much faster than their real-world equivalent. Not only are real-world experiments more expensive in terms of building and maintaining a robot, but there are challenges due to noise in measurements caused by the body of the robot, its dynamic environment, and the interactions between them [29]. These advantages make it easy to see the appeal of only using simulations for evolutionary robotics.…”

Section: Real World Evolutionmentioning

confidence: 99%

Real-world evolution adapts robot morphology and control to hardware limitations

Nygaard

Martín

Samuelsen

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

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For robots to handle the numerous factors that can affect them in the real world, they must adapt to changes and unexpected events. Evolutionary robotics tries to solve some of these issues by automatically optimizing a robot for a specific environment. Most of the research in this field, however, uses simplified representations of the robotic system in software simulations. The large gap between performance in simulation and the real world makes it challenging to transfer the resulting robots to the real world. In this paper, we apply real world multi-objective evolutionary optimization to optimize both control and morphology of a four-legged mammal-inspired robot. We change the supply voltage of the system, reducing the available torque and speed of all joints, and study how this affects both the fitness, as well as the morphology and control of the solutions. In addition to demonstrating that this realworld evolutionary scheme for morphology and control is indeed feasible with relatively few evaluations, we show that evolution under the different hardware limitations results in comparable performance for low and moderate speeds, and that the search achieves this by adapting both the control and the morphology of the robot.

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Section: Real World Evolutionmentioning

confidence: 99%

Real-world evolution adapts robot morphology and control to hardware limitations

Nygaard

Martín

Samuelsen

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

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“…Another challenge in evolutionary robotics is how to drive the evolutionary process towards high-quality solutions, avoiding potential local optima. When the task to which solutions are sought reaches a certain level of complexity, traditional evolutionary approaches are prone to suffer from bootstrap problems and deception [42,43]. One approach to deal with these issues is to employ diversity maintenance techniques, such as novelty search [44], in order to allow an evolutionary algorithm to explore multiple different paths through the search space [23,45,46].…”

Section: Evolutionary Synthesis Of Control For Srsmentioning

confidence: 99%

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

et al. 2016

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Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

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“…In the following, we present a number of notable achievements in evolutionary swarm robotics. For comprehensive reviews of the (single-robot) evolutionary robotics literature, we refer the reader to Bongard (2013), Doncieux and Mouret (2014), Trianni (2014), and Silva et al (2016).…”

Section: Off-line Methodsmentioning

confidence: 99%

Automatic Design of Robot Swarms: Achievements and Challenges

Francesca

Birattari

2016

Front. Robot. AI

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Automatic design is a promising approach to the design of control software for robot swarms. In an automatic design method, the design problem is cast into an optimization problem and is addressed using an optimization algorithm. In this article, we review studies in which automatic design methods are successfully applied. In particular, we focus our attention on how automatic methods are empirically assessed. An apparent issue that emerges from our review is that a solid, well-established, and consistently applied empirical practice is still missing. For example, studies that propose new methods and ideas do not typically provide any comparison with existing ones. We maintain that the lack of a proper empirical practice hinders the progress of the domain. In this article, we pursue two goals: we highlight the notable achievements in the automatic design of control software for robot swarms and we discuss the challenges to be overcome to establish a proper empirical practice for the domain.

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Open Issues in Evolutionary Robotics

Cited by 86 publications

References 158 publications

Real-world evolution adapts robot morphology and control to hardware limitations

Real-world evolution adapts robot morphology and control to hardware limitations

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

Automatic Design of Robot Swarms: Achievements and Challenges

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