Evolving intelligent vehicle control using multi-objective NEAT

Willigen, W. H. van; Haasdijk, Evert; Kester, Leon

doi:10.1109/civts.2013.6612283

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…While various implementations of multi-objective NEAT have appeared recently [9,21,13], we believe that our implantation happens to be the only one which does not require the removal of the NEAT speciation (noted as one of the key features of the NEAT algorithm [17]). Instead of removing speciation and replacing it with a different diversity metric, or considering the added diversity inherent in multi-objective search to be sufficient on its own, our implementation performs a Pareto ranking of individuals within each species.…”

Section: Multi-objective Neatmentioning

confidence: 98%

Evolving Soft Robots in Tight Spaces

Cheney

Bongard

Lipson

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Soft robots have become increasingly popular in recent years -and justifiably so. Their compliant structures and (theoretically) infinite degrees of freedom allow them to undertake tasks which would be impossible for their rigid body counterparts, such as conforming to uneven surfaces, efficiently distributing stress, and passing through small apertures. Previous work in the automated deign of soft robots has shown examples of these squishy creatures performing traditional robotic task like locomoting over flat ground. However, designing soft robots for traditional robotic tasks fails to fully utilize their unique advantages. In this work, we present the first example of a soft robot evolutionarily designed for reaching or squeezing through a small aperture -a task naturally suited to its type of morphology. We optimize these creatures with the CPPN-NEAT evolutionary algorithm, introducing a novel implementation of the algorithm which includes multi-objective optimization while retaining its speciation feature for diversity maintenance. We show that more compliant and deformable soft robots perform more effectively at this task than their less flexible counterparts. This work serves mainly as a proof of concept, but we hope that it helps to open the door for the better matching of tasks with appropriate morphologies in robotic design in the future. Figure 1: (counter-clockwise rotating-viewpoint time series starting from top left).An evolved soft robot reaches through a hole in the side of a cage surrounding it. The width of this aperture is smaller than any of the dimensions of this creature -thus a robot of the same size without a soft body would not be able to squeeze through it.

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Section: Multi-objective Neatmentioning

confidence: 98%

Evolving Soft Robots in Tight Spaces

Cheney

Bongard

Lipson

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…The weighted-sum methods cannot, however, determine the weights and the normalization factors that can optimally balance and scale the multiple objective functions for a problem with little or no information [24], which can cause misleading optimization results. 2) Pareto-optimal methods offer a set of Pareto-optimal (or non-dominated) solutions [25], where no single solution is better than another in every criterion [26]. Zhang et al [14] have employed a non-dominated sorting genetic algorithm (NSGA) to reduce the manufacturing cost of the energy storage system and prolong battery life.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective component sizing of a hybrid ethanol-electric vehicle propulsion system

Wang

Zhou

et al. 2020

Applied Energy

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Concerns over energy efficiency and greenhouse gas (GHG) emissions are driving research investments into advanced propulsion technologies. Plug-in hybrid electric vehicles (PHEVs) can provide a bridge that connects transport electrification to renewable bioenergy sources such as ethanol. However, it remains unclear how this pathway can simultaneously address economic, energy and environmental goals. To tackle this challenge, the present study explores, for the first time, the multiobjective optimal sizing of PHEVs powered by low-carbon sources of electricity and ethanol-gasoline blend. The empirical ethanolgasoline blend model is incorporated into the PHEV simulation whose relevant parameters are validated using laboratory data from the European Commission -Joint Research Centre. We develop a full picture of the use-phase well-to-wheel (WTW) GHG emissions from ethanol, gasoline and grid electricity and their energy consumptions. Consequently, market-oriented PHEV sizing solutions are provided as per the power utility generation portfolio and automobile fuel properties of the target region. The results indicate that better performances of the PHEV, regarding GHG emissions and energy consumption, are associated with larger battery size and smaller engine displacement but result in a higher cost-to-power ratio. Specifically, for E25-fuelled PHEVs in markets with world average electricity carbon intensity, every 1.0 USD/kW increase in cost-to-power ratio leads to savings of 1.6 MJ energy consumption and 1.7 g CO2-eq/km WTW GHG emissions. Moreover, a clear benefit of using E25 in the hybrid propulsion system is identified, where the energy consumption and GHG emissions can be reduced by 5.9 % and 12.3 %, respectively.

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“…In another study, a marathon competition has been simulated with the help of MovSim . MovSim has been used for simulation of autonomous vehicle agents enabled with a multiobjective method to find multiple prioritizations for passengers traveling in an autonomous vehicle so that the passenger may chose the way car should be drove . To incorporate human factors in microscopic driver models, a model of personality profile has been incorporated in existing microscopic models, and simulation‐based experiments have been performed using MovSim …”

Section: Introductionmentioning

confidence: 99%

A smart integrated environment for vehicular traffic simulation

Mubasher

Jaffry

Yousaf

et al. 2019

Int J Communication

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Summary Globally, urbanization has increased due to the availability of the facilities, jobs, and better standard of living in cities. Increase in urban population has caused increase in urban commute. The increased pressure on urban settlements has made design and optimization of urban civil infrastructure very challenging especially road transport infrastructure. Domain experts such as transportation managers, engineers, scientist, and academics need effective, easy, and readily usable computational modeling and simulation tools to conduct systematic inquiry for design and optimization of the transportation system. Free and open‐source software (FOSS) community has developed many vehicular traffic simulation software that are cost‐effective and extensible, however are difficult to use. Usually, domain experts do not have hands‐on software skills such as XML‐based interfaces, software source versioning, and build management. To bridge this gap, in our work, an integrated development environment (IDE), namely, integrated vehicular traffic simulation environment (ITE), has been developed. ITE is developed on top of an existing FOSS multimodal open‐source vehicular‐traffic simulator (MovSim). ITE includes a scenario building tool for domain experts. It could be used to model road infrastructure and vehicular traffic in an integrated manner. The tool could be used to build and experiment novel traffic infrastructures and policies. Our work helps in reducing learning curve of domain experts by providing a free and open source, easy and readily usable vehicular traffic simulation tool.

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Evolving intelligent vehicle control using multi-objective NEAT

Cited by 6 publications

References 12 publications

Evolving Soft Robots in Tight Spaces

Evolving Soft Robots in Tight Spaces

Multiobjective component sizing of a hybrid ethanol-electric vehicle propulsion system

A smart integrated environment for vehicular traffic simulation

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