34 citations · 57 across the 15 of their papers we have counts for
17 papers
Scenario-Based Motion Planning with Bounded Probability of Collision
Oscar de Groot, Laura Ferranti, Dariu Gavrila +1
Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced i…
Contingency Games for Multi-Agent Interaction
Lasse Peters, Andrea Bajcsy, Chih-Yuan Chiu +4
Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under un…
Optimization-based Fault Mitigation for Safe Automated Driving
Niels Lodder, Chris van der Ploeg, Laura Ferranti +1
With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system healt…
Globally Guided Trajectory Planning in Dynamic Environments
O. de Groot, L. Ferranti, D. Gavrila +1
Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacl…
Online and Offline Learning of Player Objectives from Partial Observations in Dynamic Games
Lasse Peters, Vicenç Rubies-Royo, Claire J. Tomlin +4
Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scena…
PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning
Jelle Luijkx, Zlatan Ajanovic, Laura Ferranti +1
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).…
Learning Mixed Strategies in Trajectory Games
Lasse Peters, David Fridovich-Keil, Laura Ferranti +3
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 game…
Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning
Max Lodel, Bruno Brito, Álvaro Serra-Gómez +3
Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current met…
Active Safety System for Semi-Autonomous Teleoperated Vehicles
Smit Saparia, Andreas Schimpe, Laura Ferranti
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, n…
Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments
O. de Groot, B. Brito, L. Ferranti +2
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 m…
DeepKoCo: Efficient latent planning with a task-relevant Koopman representation
Bas van der Heijden, Laura Ferranti, Jens Kober +1
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 linea…
Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments
Bruno Brito, Boaz Floor, Laura Ferranti +1
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 optimiza…
A Distributed Multi-Vehicle Coordination Algorithm for Navigation in Tight Environments
Roya Firoozi, Laura Ferranti, Xiaojing Zhang +2
This work presents a distributed method for multi-vehicle coordination based on nonlinear model predictive control (NMPC) and dual decomposition. Our approach allows the vehicles t…
An Improved Primal-Dual Interior Point Solver for Model Predictive Control
X. Zhang, L. Ferranti, T. Keviczky
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 s…
Asynchronous Splitting Design for Model Predictive Control
Laura Ferranti, Ye Pu, Colin N. Jones +1
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…
Constrained LQR Using Online Decomposition Techniques
L. Ferranti, G. Stathopoulos, C. N. Jones +1
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 re…
A Parallel Dual Fast Gradient Method for MPC Applications
Laura Ferranti, Tamas Keviczky
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…