Neuroevolutionary Control for Autonomous Soaring

Kim, Eric J.; Perez, Ruben E.

doi:10.3390/aerospace8090267

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…Early on, Neuroevolution learning was demonstrated for its ability to learn and master playing video games [15,16]. More recently, engineering-based publications have shown interest in Neuroevolution of Augmented Topology (NEAT) for antenna beam forming control [17], and UAV autonomous soaring applications [18]. At this time, the lack of explainability and transparency are key disadvantages to AI applications in general.…”

Section: Methodsmentioning

confidence: 99%

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

et al. 2022

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The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process.

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Section: Methodsmentioning

confidence: 99%

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

et al. 2022

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“…Video game playing [20] has been popular in demonstrating this technique's ability to beat human players in real time [21,22]. Engineering applications have recently been published around antenna beam forming control [23], UAV control [24] and swarm robotics [20,25] using this method.…”

Section: Methodsmentioning

confidence: 99%

Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection

Jacquelin

Bae

Chen

et al. 2023

Eng

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Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go vehicle dynamics. Low deviation from the road speed limit is hence continuously sustained for cohorts of at most 50 m long. This limitation can be mitigated with additional lanes that the cohorts can split into. The concept also allows the testing and implementation of fast-turning lanes by simply replicating and reconnecting the control architecture at each new road crossing, enabling high scalability for complex road network analysis. The controller is also successfully validated within a high-fidelity vehicle dynamic environment, showing its potential for driverless vehicle control in addition to offering a new traffic control simulation model for future autonomous operation studies.

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“…Many publications indicate the successful use of NEAT algorithms in training ANN in various control tasks [33]. Typically, NEAT is used to create and optimize ANN in a virtual environment but, like in [34,35], UAV flight mechanics is oversimplified. Usually, autonomous UAVs operating as standalone platforms in GPS-less environments rely on vision, combining optical flow sensors and visual-inertial odometry (VIO) with sonar and LIDAR for distance measurement [27,23,28].…”

Section: Related Workmentioning

confidence: 99%

Application of artificial neural networks and NEAT algorithm to control a quadcopter

Przychodzki,

Niewiadomski

2023

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We present a framework for building ArtificialNeuralNetworks (ANNs) able to control a quadcopter and perform basic maneuvers, like hover or following waypoints. In this approach, we make use of the Neuroevolution of Augmenting Topologies (NEAT) algorithm which is aimed at creating the network structure and the weights in result of evolutionary computations. In order to evaluate fitness of individuals, we use physics based, realistic simulation engine Gazebo, where each individual controls a drone in a simulated environment. Our approach is aimed at using one of existing, popular protocols used to remotely control drones, and train ANNs able to imitate signals received from a radio controller operated by a human pilot. Thus, contrary to the most of other approaches, our autonomous controller cooperates with standard drone software. Our ultimate goal is to train ANNs able to control a real-world quadcopter and perform advanced tasks autonomously. Not only such ANNs should be able to perform the maneuvers correctly, but they should be small enough to transfer them into a quadcopter’s limited memory. In this paper we report the first stage of our project - a successful development and deployment of the ANNs distributed training framework, and choosing the activation function for further research.

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Neuroevolutionary Control for Autonomous Soaring

Cited by 14 publications

References 24 publications

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection

Application of artificial neural networks and NEAT algorithm to control a quadcopter

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