Firefly algorithm-based nonlinear MPC trajectory planner for autonomous driving

Inghilterra, Giuseppe; Arrigoni, Stefano; Braghin, Francesco; Cheli, Federico

doi:10.23919/eeta.2018.8493215

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…Finally the optimization problem is constrained by vehicle model equation ẋ(t) = f (x(t), u(t)) and physical constraint functionh, which represents state limits (geometrical and physical limits) as well as control action limits. In [18], [19] the starting point for the motion planner mathematical formulation integrated on the vehicle is reported.…”

Section: Software Architecturementioning

confidence: 99%

Design of a prototypical platform for autonomous and connected vehicles

Arrigoni,

Mentasti,

Cheli

et al. 2021

Preprint

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Self-driving technology is expected to revolutionize different sectors and is seen as the natural evolution of road vehicles. In the last years, real-world validation of designed and virtually tested solutions is growing in importance since simulated environments will never fully replicate all the aspects that can affect results in the real world. To this end, this paper presents our prototype platform for experimental research on connected and autonomous driving projects. In detail, the paper presents the overall architecture of the vehicle focusing both on mechanical aspects related to remote actuation and sensors set-up and software aspects by means of a comprehensive description of the main algorithms required for autonomous driving as ego-localization, environment perception, motion planning, and actuation. Finally, experimental tests conducted in an urban-like environment are reported to validate and assess the performances of the overall system.

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Section: Software Architecturementioning

confidence: 99%

Design of a prototypical platform for autonomous and connected vehicles

Arrigoni,

Mentasti,

Cheli

et al. 2021

Preprint

Self Cite

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“…The firefly search algorithm and its variants have been applied to trajectory optimization [56][57][58], control parameter optimization [59,60], and dynamics [ [61][62][63] in what can be considered as an introductory investigation by looking for initial successes in applying the FA toward these astronautical research areas.…”

Section: Firefly Search Algorithmmentioning

confidence: 99%

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Cooper¹,

Smeresky²

2020

Advances in Spacecraft Attitude Control

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Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification.

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“…The most common road definition models are: poly-line model, lane-let model, and Hermite spline model with increasing complexity and computational need in given order [13]. According to the different motion planners presented in [14,15], the road map model of the track can be approximated through cubic Hermite spline interpolation [16]. The most important advantage of curvilinear coordinates (s − n) with respect to Cartesian coordinates (X − Y ) is that each road characteristic can be described as a function of only one parameter (i.e., the abscissa s); thus, each function that approximates the centerline is at least surjective.…”

Section: Introductionmentioning

confidence: 99%

“…To conclude, the measurement update of the state estimates can be per-364 formed accounting for the cross covariance matrix given by (14), that is required 365 to compute the Kalman gain matrix as indicated in (15). The updated state 366 vector (x + k ) and covariance (P + k ) are obtained from equations ( 16) and (17).…”

mentioning

confidence: 99%

“…Moreover, according to linear Kalman filtering in discrete time, the state prediction covariance for each obstacle can be computed as in (29), where F k−1 is the matrix of the linear model and P + oi k−1 is the covariance matrix of the state updated by the measurements at the previous step. Nevertheless, the unscented transformation is still performed as in ( 9) to allow computing the cross covariance matrix (14). In practice, among all the 2n + 1 sigma points, only one is used to perform state prediction and covariance, while the remaining 2n are required to compute P xy, oi .…”

mentioning

confidence: 99%

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An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving

Bersani

Mentasti

Dahal

et al. 2021

Robotics and Autonomous Systems

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show abstract

Firefly algorithm-based nonlinear MPC trajectory planner for autonomous driving

Cited by 9 publications

References 9 publications

Design of a prototypical platform for autonomous and connected vehicles

Design of a prototypical platform for autonomous and connected vehicles

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving

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