J
Javier Alonso-Mora
Researcher at Delft University of Technology
Publications - 148
Citations - 5718
Javier Alonso-Mora is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Computer science & Motion planning. The author has an hindex of 31, co-authored 103 publications receiving 3609 citations. Previous affiliations of Javier Alonso-Mora include ETH Zurich & Institute of Robotics and Intelligent Systems.
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Journal ArticleDOI
Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments
TL;DR: This letter builds on nonlinear model-predictive contouring control (MPCC) and extends it to incorporate a static map by computing, online, a set of convex regions in free space and model moving obstacles as ellipsoids and provide a correct bound to approximate the collision region.
Proceedings ArticleDOI
Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control
TL;DR: This paper addresses the problem of multi-MAV reactive collision avoidance by employing a model-based controller to simultaneously track a reference trajectory and avoid collisions and accounts for the full MAV dynamics.
Proceedings ArticleDOI
Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus
TL;DR: The distributed method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles is efficient and scalable with the number of robots and performs well in simulations with up to sixteen quadrotors.
Journal ArticleDOI
Distributed multi-robot formation control in dynamic environments
TL;DR: This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles that is efficient and scalable with the number of robots.
Proceedings ArticleDOI
Parallel autonomy in automated vehicles: Safe motion generation with minimal intervention
TL;DR: A Parallel Autonomy shared-control framework that produces safe trajectories based on human inputs even in much more complex driving scenarios, such as those commonly encountered in an urban setting is introduced.