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Björn Lindqvist

5 accepted papers

2026

Deployment of an Aerial Multiagent System for Automated Task Execution in Large-Scale Underground Mining Environments (I)

Niklas Dahlquist, Samuel Nordström, Nikolaos Stathoulopoulos, Björn Lindqvist, Akshit Saradagi, George Nikolakopoulos
ICRA 2026poster

In this article, we present a framework for deploying aerial multiagent systems in large-scale subterranean environments with minimal supporting infrastructure. The objective is to optimally and reactively execute routine inspection tasks, selected by a mine operator on-the-fly. The assignment of cu…

Cited by 0Scholar ↗
2021

A Scalable Distributed Collision Avoidance Scheme for Multi-agent UAV systems

Björn Lindqvist, Pantelis Sopasakis, George Nikolakopoulos
IROS 2021poster

In this article we propose a distributed collision avoidance scheme for multi-agent unmanned aerial vehicles (UAVs) based on nonlinear model predictive control (NMPC), where other agents in the system are considered as dynamic obstacles with respect to the ego agent. Our control scheme operates at a…

Cited by 31Source ↗Scholar ↗
2021

Exploration-RRT: A multi-objective Path Planning and Exploration Framework for Unknown and Unstructured Environments

Björn Lindqvist, Ali-Akbar Agha-Mohammadi, George Nikolakopoulos
IROS 2021poster

This article establishes the Exploration-RRT algorithm: A novel general-purpose combined exploration and path planning algorithm, based on a multi-goal Rapidly-Exploring Random Trees (RRT) framework. Exploration-RRT (ERRT) has been specifically designed for utilization in 3D exploration missions, wi…

Cited by 58Source ↗Scholar ↗
2020

A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty

Sina Sharif Mansouri, Christoforos Kanellakis, Björn Lindqvist, Farhad Pourkamali-Anaraki, Ali-akbar Agha-mohammadi, Joel Burdick +1
RA-L 2020

This letter proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in indoor enclosed environments. The introduced framework allows us to consider the nonlinear dynamics of MAVs, nonlinear geometric constraints, while guarantees real

Cited by 14Source ↗Scholar ↗
2020

Nonlinear MPC for Collision Avoidance and Control of UAVs With Dynamic Obstacles

Björn Lindqvist, Sina Sharif Mansouri, Ali-akbar Agha-mohammadi, George Nikolakopoulos
RA-L 2020

This letter proposes a Novel Nonlinear Model Predictive Control (NMPC) for navigation and obstacle avoidance of an Unmanned Aerial Vehicle (UAV). The proposed NMPC formulation allows for a fully parametric obstacle trajectory, while in this letter we apply a classification scheme to differentiate be

Cited by 247Source ↗Scholar ↗