Laura FerrantiDelft University of Technology | TU · Cognitive Robotics
Laura Ferranti
PhD
About
38
Publications
10,402
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380
Citations
Citations since 2017
Introduction
I am currently an Assistant Professor at Delft University of Technology in the faculty of Mechanical, Maritime, and Materials Engineering (3ME) in the Cognitive Robotics (CoR) Department. I coordinate the Reliable Robot Control (R2C) Lab.
My work focuses on real-time and reliable model predictive control (MPC) for autonomous robot cooperation near humans.
Additional affiliations
November 2019 - present
February 2019 - June 2019
September 2017 - October 2019
Education
April 2013 - September 2017
Publications
Publications (38)
Since their introduction, anti-lock braking systems (ABS) have mostly relied on heuristic, rule-based control strategies. ABS performance, however, can be significantly improved thanks to many recent technological developments. This work presents an extensive review of the state of the art to verify such a statement and quantify the benefits of a n...
This work presents a Nonlinear Model Predictive Control (NMPC) scheme to perform evasive maneuvers and avoid rear-end collisions. Rear-end collisions are among the most common road fatalities. To reduce the risk of collision, it is necessary for the controller to react as quickly as possible and exploit the full vehicle maneuverability (i.e., combi...
We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the marginal risk of collisions at each point in time by incorporating chance constraints into the planning problem. This problem is not suitable for online optimization outri...
Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses). However, these approaches do not consider the uncertainty of the trained policy and simply always execute actions suggested by the current policy as the most probable on...
This work presents a method for multi-robot coordination based on a novel distributed nonlinear model predictive control formulation for trajectory optimization and its modified version to mitigate the effects of packet losses and delays in the communication among the robots. Our algorithms consider that each robot is equipped with an on-board comp...
This letter proposes a distributed strategy to achieve both persistent monitoring and target detection in a rectangular and obstacle-free environment. Each robot has to repeatedly follow a smooth trajectory and avoid collisions with other robots. To achieve this goal, we rely on the time-inverted Kuramoto dynamics and the use of Lissajous curves. W...
This paper presents a rule-compliant trajectory optimization method for the guidance and control of autonomous surface vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea—known as COLREGs—relevant for motion planning. We use these traffic rules to derive a...
Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses). However, these approaches do not consider the uncertainty of the trained policy and simply always execute actions suggested by the current policy as the most probable on...
This paper presents a rule-compliant trajectory optimization method for the guidance and control of autonomous surface vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea-known as COLREGs-relevant for motion planning. We use these traffic rules to derive a...
In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another's behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional "predict then plan" approaches. How...
Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative for f...
This paper presents DeepKoCo, a novel model based agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linear control methods, such as linear model predictive control. Compared to traditional agents, DeepKoCo learns task relevant dynamics, thanks to the use of a tailored lossy...
Autonomous cars can reduce road traffic accidents and provide a safer mode of transport. However, key technical challenges, such as safe navigation in complex urban environments, need to be addressed before deploying these vehicles on the market. Teleoperation can help smooth the transition from human operated to fully autonomous vehicles since it...
Autonomous cars can reduce road traffic accidents and provide a safer mode of transport. However, key technical challenges, such as safe navigation in complex urban environments, need to be addressed before deploying these vehicles on the market. Teleoperation can help smooth the transition from human operated to fully autonomous vehicles since it...
We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the marginal risk of collisions at each point in time by incorporating chance constraints into the planning problem. This problem is not suitable for online optimization outri...
This paper presents DeepKoCo, a novel model based agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linear control methods, such as linear model predictive control. Compared to traditional agents, DeepKoCo learns task relevant dynamics, thanks to the use of a tailored lossy...
This paper presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obstacles. We build on nonlinear model-predictive contou...
This work presents a distributed method for multi-robot coordination based on nonlinear model predictive control (NMPC) and dual decomposition. Our approach allows the robots to coordinate in tight spaces (e.g., highway lanes, parking lots, warehouses, canals, etc.) by using a polytopic description of each robot's shape and formulating the collisio...
This paper presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obstacles. We build on nonlinear model-predictive contou...
This paper presents our research platform SafeVRU for the interaction of self-driving vehicles with Vulnerable Road Users (VRUs, i.e., pedestrians and cyclists). The paper details the design (implemented with a modular structure within ROS) of the full stack of vehicle localization, environment perception, motion planning, and control, with emphasi...
This paper presents a distributed method for splitting and merging of multi-robot formations in dynamic environments with static and moving obstacles. Splitting and merging actions rely on distributed consensus and can be performed to avoid obstacles. Our method accounts for the limited communication range and visibility radius of the robots and re...
This paper focuses on the design of an asynchronous dual solver suitable for model predictive control (MPC) applications. The proposed solver relies on a state-of-the-art variance reduction (VR) scheme, previously used in the context of stochastic proximal gradient methods (Prox-SVRG), and on the alternating minimization algorithm (AMA). The result...
This work presents a method for multi-robot
trajectory planning and coordination based on nonlinear model
predictive control (NMPC). In contrast to centralized approaches,
we consider the distributed case where each robot has
an on-board computation unit to solve a local NMPC problem
and can communicate with other robots in its neighborhood.
We sho...
![FIGURE 2 Stall load [Colour figure can be viewed at wileyonlinelibrary.com]](publication/323325651/figure/fig7/AS:668329224204290@1536353512247/Stall-load-Colour-figure-can-be-viewed-at-wileyonlinelibrarycom_Q320.jpg)
This paper focuses on the longitudinal control of an Airbus passenger aircraft in the presence of elevator jamming faults. In particular, in this paper, we address permanent and temporary actuator jamming faults using a novel reconfigurable fault-tolerant predictive control design. Due to their different consequences on the available control author...
We propose a primal-dual interior-point (PDIP) method for solving quadratic programming problems with linear inequality constraints that typically arise form MPC applications. We show that the solver converges (locally) quadratically to a suboptimal solution of the MPC problem. PDIP solvers rely on two phases: the damped and the pure Newton phases....
Despite their ability to operate on the limits of performance, handle multivariable and nonlinear systems, and offer online adaptation and reconfiguration capabilities, model predictive control approaches to aerospace applications suffer from limitations related to the online computational burden and complexity of the underlying optimization proble...
This paper focuses on the design of an asynchronous dual solver suitable for embedded model predictive control (MPC) applications. The proposed solver relies on a state-of-the-art variance reduction (VR) scheme, previously used in the context of stochastic proximal gradient methods, and on the alternating minimization algorithm (AMA). The resultant...
This paper presents an algorithm to solve the infinite horizon constrained linear quadratic regulator (CLQR) problem using operator splitting methods. First, the CLQR problem is reformulated as a (finite-time) model predictive control (MPC) problem without terminal constraints. Second, the MPC problem is decomposed into smaller subproblems of fixed...
This paper describes the design of a model predictive controller (MPC) for the longitudinal motion of a passenger aircraft. The main focus of this work is on the performance of the controller in terms of computation time required for online optimization. In particular, the controller must accomplish the following objectives: (i) run in real-time, i...
Actuator jamming in flight control applications may be attributed to a temporary stall load due to large aerodynamic forces, or even a permanently stuck faulty control surface. These two root causes of actuator jamming have different consequences on available control authority and fault duration. As an important consequence, any reconfiguration str...
We propose a parallel adaptive constraint-tightening approach to solve a
linear model predictive control problem for discrete-time systems, based on
inexact numerical optimization algorithms and operator splitting methods. The
underlying algorithm first splits the original problem in as many independent
subproblems as the length of the prediction h...
In this paper, we propose a model predictive control scheme for discrete-time linear invariant systems based on inexact numerical optimization algorithms. We assume that the solution of the associated quadratic program produced by some numerical algorithm is possibly neither optimal nor feasible, but the algorithm is able to provide estimates on pr...
Projects
Projects (6)
Mobile robots, such as autonomous cars, vessels, and drones, can significantly improve our quality of life. These robots are equipped with communication units that allow information sharing and coordination near humans, abilities that are fundamental to improve, for example, traffic efficiency and even save human lives. However, the occurrence of faults and attacks can severely disrupt the coordination with negative consequences on humans’ safety and security. Furthermore, to coordinate, the robots share private users’ information, such as habits, routes, and destinations, which may be inadvertently exposed to prying eyes. Hence, there is an urgent need to address safety, security, and privacy concerns for our society to fully benefit from multi-robot systems.
This project will devise a unified coordination framework for mobile robots to seamlessly perform tasks near humans, providing strong safety, but also security and privacy guarantees.
Mobile robots, such as autonomous cars, vessels, and drones, can significantly improve our quality of life. These robots are equipped with communication units that allow information sharing and coordination near humans, abilities that are fundamental to improve, for example, traffic efficiency and even save human lives. However, the occurrence of faults and attacks can severely disrupt the coordination with negative consequences on humans’ safety and security. Furthermore, to coordinate, the robots share private users’ information, such as habits, routes, and destinations, which may be inadvertently exposed to prying eyes. Hence, there is an urgent need to address safety, security, and privacy concerns for our society to fully benefit from multi-robot systems.
This project will devise a unified coordination framework for mobile robots to seamlessly perform tasks near humans, providing strong safety, but also security and privacy guarantees.
Autonomous vehicles (such as cars and vessels) will be widespread in our daily lives, aiming at reducing pollution while improving traffic efficiency and safety. The ability of these vehicles to cooperate in planning trajectories is one of the main strengths of this technology. The presence of human-operated vehicles and the occurrence of sensor/actuator faults, however, complicate the vehicle cooperation. Failing to handle these mixed-traffic uncertainties and faults in the motion planning strategy can inevitably compromise the cooperation. The goal of this project (SCoop) is to design a cooperation framework to allow autonomous vehicles to safely navigate in the presence of human-operated vehicles and faults. To design a novel safe cooperation framework, the project will rely on tools for uncertainty estimation/fault diagnosis and distributed motion planning. Experiments on real autonomous surface vessels (ASVs) will demonstrate the effectiveness of the proposed design. SCoop is a Cohesion project between the Cognitive Robotics Department and the Maritime and Transport Technology Department.
Contact: Dr. L. Ferranti and Dr. V. Reppa
