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Papers by Huckleberry Febbo
Need for increased automated vehicle safety and performance will exist until control systems can ... more Need for increased automated vehicle safety and performance will exist until control systems can fully exploit the vehicle's maneuvering capacity to avoid collisions with both static and moving obstacles in unknown environments. A safe and performancebased trajectory planning algorithm exists that can operate an automated vehicle in unknown static environments. However, this algorithm cannot be used safely in unknown dynamic environments; furthermore, it is not real-time. Accordingly, this thesis addresses two overarching research questions:
Need for increased automated vehicle safety and performance will exist until control systems can ... more Need for increased automated vehicle safety and performance will exist until control systems can fully exploit the vehicle's maneuvering capacity to avoid collisions with both static and moving obstacles in unknown environments. A safe and performancebased trajectory planning algorithm exists that can operate an automated vehicle in unknown static environments. However, this algorithm cannot be used safely in unknown dynamic environments; furthermore, it is not real-time. Accordingly, this thesis addresses two overarching research questions:
Safe trajectory planning for high-performance automated vehicles in an environment with both stat... more Safe trajectory planning for high-performance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time-to-goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a large, high-speed unmanned ground vehicle, that includes the above set of specifications. This paper also evaluates NLOptControl's ability to solve this formulation in real-time in conjunction with the KNITRO nonlinear programming problem solver; NLOptControl is our open-source, direct-collocation based, optimal control problem solver. This formulation is tested with various sets of the specifications. ...
2020 American Control Conference (ACC), 2020
This paper introduces an accurate nonlinear model predictive control-based algorithm for trajecto... more This paper introduces an accurate nonlinear model predictive control-based algorithm for trajectory following. For accuracy, the algorithm incorporates both the planned state and control trajectories into its cost functional. Current following algorithms do not incorporate control trajectories into their cost functionals. Comparisons are made against two trajectory following algorithms, where the trajectory planning problem is to safely follow a person using an automated ATV with control delays in a dynamic environment while simultaneously optimizing speed and steering, minimizing control effort, and minimizing the time-to-goal. Results indicate that the proposed algorithm reduces collisions, tracking error, orientation error, and time-to-goal. Therefore, tracking the control trajectories with the trajectory following algorithm helps the vehicle follow the planned state trajectories more accurately, which ultimately improves safety, especially in dynamic environments.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
This paper presents a trajectory planning algorithm for person following that is more comprehensi... more This paper presents a trajectory planning algorithm for person following that is more comprehensive than existing algorithms. This algorithm is tailored for a front-wheel-steered vehicle, is designed to follow a person while avoiding collisions with both static and moving obstacles, simultaneously optimizing speed and steering, and minimizing control effort. This algorithm uses nonlinear model predictive control, where the underling trajectory optimization problem is approximated using a simultaneous method. Results collected in an unknown environment show that the proposed planning algorithm works well with a perception algorithm to follow a person in uneven grass near obstacles and over ditches and curbs, and on asphalt over train-tracks and near buildings and cars. Overall, the results indicate that the proposed algorithm can safely follow a person in unknown, dynamic environments.
ArXiv, 2020
Current direct-collocation-based optimal control software is either easy to use or fast, but not ... more Current direct-collocation-based optimal control software is either easy to use or fast, but not both. This is a major limitation for users that are trying to formulate complex optimal control problems (OCPs) for use in on-line applications. This paper introduces NLOptControl, an open-source modeling language that allows users to both easily formulate and quickly solve nonlinear OCPs using direct-collocation methods. To achieve these attributes, NLOptControl (1) is written in an efficient, dynamically-typed computing language called Julia, (2) extends an optimization modeling language called JuMP to provide a natural algebraic syntax for modeling nonlinear OCPs; and (3) uses reverse automatic differentiation with the acrylic-coloring method to exploit sparsity in the Hessian matrix. This work explores the novel design features of NLOptControl and compares its syntax and speed to those of PROPT. The syntax comparisons shows that NLOptControl models OCPs more concisely than PROPT. The...
Safe trajectory planning for high-performance automated vehicles in an environment with both stat... more Safe trajectory planning for high-performance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time-to-goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a large, high-speed unmanned ground vehicle, that includes the above set of specifications. This paper also evaluates NLOptControl's ability to solve this formulation in real-time in conjunction with the KNITRO nonlinear programming problem solver; NLOptControl is our open-source, direct-collocation based, optimal control problem solver. This formulation is tested with various sets of the specifications. ...
2017 American Control Conference (ACC)
Transportation Research Part F: Traffic Psychology and Behaviour
Volume 3: 18th International Conference on Advanced Vehicle Technologies; 13th International Conference on Design Education; 9th Frontiers in Biomedical Devices
The design and control of hybrid-electric vehicle (HEV) powertrains presents an optimization prob... more The design and control of hybrid-electric vehicle (HEV) powertrains presents an optimization problem to balance the trade-off between multiple objectives, such as fuel economy, driv-ability, and emissions. However, current design methodologies do not simultaneously incorporate all of these three considerations into both the sizing and control layers of the optimization problem. This paper first demonstrates that the trade-offs between these objectives can be non-trivial in the HEV control problem. This motivates the need for a systematic design procedure that can take all three objectives into account. To address this need, the paper describes the development of a new and efficient design framework called the Hybrid-Vehicle Design Tool (HVDT), which adopts a bi-level optimization strategy. Efficiency is achieved by introducing a neural-network-based meta-model to predict the performance of the optimal control strategy obtained using Dynamic Programming (DP). To demonstrate the HVDT,...
Need for increased automated vehicle safety and performance will exist until control systems can ... more Need for increased automated vehicle safety and performance will exist until control systems can fully exploit the vehicle's maneuvering capacity to avoid collisions with both static and moving obstacles in unknown environments. A safe and performancebased trajectory planning algorithm exists that can operate an automated vehicle in unknown static environments. However, this algorithm cannot be used safely in unknown dynamic environments; furthermore, it is not real-time. Accordingly, this thesis addresses two overarching research questions:
Need for increased automated vehicle safety and performance will exist until control systems can ... more Need for increased automated vehicle safety and performance will exist until control systems can fully exploit the vehicle's maneuvering capacity to avoid collisions with both static and moving obstacles in unknown environments. A safe and performancebased trajectory planning algorithm exists that can operate an automated vehicle in unknown static environments. However, this algorithm cannot be used safely in unknown dynamic environments; furthermore, it is not real-time. Accordingly, this thesis addresses two overarching research questions:
Safe trajectory planning for high-performance automated vehicles in an environment with both stat... more Safe trajectory planning for high-performance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time-to-goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a large, high-speed unmanned ground vehicle, that includes the above set of specifications. This paper also evaluates NLOptControl's ability to solve this formulation in real-time in conjunction with the KNITRO nonlinear programming problem solver; NLOptControl is our open-source, direct-collocation based, optimal control problem solver. This formulation is tested with various sets of the specifications. ...
2020 American Control Conference (ACC), 2020
This paper introduces an accurate nonlinear model predictive control-based algorithm for trajecto... more This paper introduces an accurate nonlinear model predictive control-based algorithm for trajectory following. For accuracy, the algorithm incorporates both the planned state and control trajectories into its cost functional. Current following algorithms do not incorporate control trajectories into their cost functionals. Comparisons are made against two trajectory following algorithms, where the trajectory planning problem is to safely follow a person using an automated ATV with control delays in a dynamic environment while simultaneously optimizing speed and steering, minimizing control effort, and minimizing the time-to-goal. Results indicate that the proposed algorithm reduces collisions, tracking error, orientation error, and time-to-goal. Therefore, tracking the control trajectories with the trajectory following algorithm helps the vehicle follow the planned state trajectories more accurately, which ultimately improves safety, especially in dynamic environments.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
This paper presents a trajectory planning algorithm for person following that is more comprehensi... more This paper presents a trajectory planning algorithm for person following that is more comprehensive than existing algorithms. This algorithm is tailored for a front-wheel-steered vehicle, is designed to follow a person while avoiding collisions with both static and moving obstacles, simultaneously optimizing speed and steering, and minimizing control effort. This algorithm uses nonlinear model predictive control, where the underling trajectory optimization problem is approximated using a simultaneous method. Results collected in an unknown environment show that the proposed planning algorithm works well with a perception algorithm to follow a person in uneven grass near obstacles and over ditches and curbs, and on asphalt over train-tracks and near buildings and cars. Overall, the results indicate that the proposed algorithm can safely follow a person in unknown, dynamic environments.
ArXiv, 2020
Current direct-collocation-based optimal control software is either easy to use or fast, but not ... more Current direct-collocation-based optimal control software is either easy to use or fast, but not both. This is a major limitation for users that are trying to formulate complex optimal control problems (OCPs) for use in on-line applications. This paper introduces NLOptControl, an open-source modeling language that allows users to both easily formulate and quickly solve nonlinear OCPs using direct-collocation methods. To achieve these attributes, NLOptControl (1) is written in an efficient, dynamically-typed computing language called Julia, (2) extends an optimization modeling language called JuMP to provide a natural algebraic syntax for modeling nonlinear OCPs; and (3) uses reverse automatic differentiation with the acrylic-coloring method to exploit sparsity in the Hessian matrix. This work explores the novel design features of NLOptControl and compares its syntax and speed to those of PROPT. The syntax comparisons shows that NLOptControl models OCPs more concisely than PROPT. The...
Safe trajectory planning for high-performance automated vehicles in an environment with both stat... more Safe trajectory planning for high-performance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time-to-goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a large, high-speed unmanned ground vehicle, that includes the above set of specifications. This paper also evaluates NLOptControl's ability to solve this formulation in real-time in conjunction with the KNITRO nonlinear programming problem solver; NLOptControl is our open-source, direct-collocation based, optimal control problem solver. This formulation is tested with various sets of the specifications. ...
2017 American Control Conference (ACC)
Transportation Research Part F: Traffic Psychology and Behaviour
Volume 3: 18th International Conference on Advanced Vehicle Technologies; 13th International Conference on Design Education; 9th Frontiers in Biomedical Devices
The design and control of hybrid-electric vehicle (HEV) powertrains presents an optimization prob... more The design and control of hybrid-electric vehicle (HEV) powertrains presents an optimization problem to balance the trade-off between multiple objectives, such as fuel economy, driv-ability, and emissions. However, current design methodologies do not simultaneously incorporate all of these three considerations into both the sizing and control layers of the optimization problem. This paper first demonstrates that the trade-offs between these objectives can be non-trivial in the HEV control problem. This motivates the need for a systematic design procedure that can take all three objectives into account. To address this need, the paper describes the development of a new and efficient design framework called the Hybrid-Vehicle Design Tool (HVDT), which adopts a bi-level optimization strategy. Efficiency is achieved by introducing a neural-network-based meta-model to predict the performance of the optimal control strategy obtained using Dynamic Programming (DP). To demonstrate the HVDT,...