Dennis Benders

@misc{benders_embedded_2024,
	title = {Embedded {Hierarchical} {MPC} for {Autonomous} {Navigation}},
	url = {paper=http://arxiv.org/abs/2406.11506},
	doi = {10.48550/arXiv.2406.11506},
	abstract = {To efficiently deploy robotic systems in society, mobile robots need to autonomously and safely move through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby obstacles. However, the limited computation power available on typical embedded robotic systems, such as quadrotors, poses a challenge to running MPC in real-time, including its most expensive tasks: constraints generation and optimization. To address this problem, we propose a novel hierarchical MPC scheme that consists of a planning and a tracking layer. The planner constructs a trajectory with a long prediction horizon at a slow rate, while the tracker ensures trajectory tracking at a relatively fast rate. We prove that the proposed framework avoids collisions and is recursively feasible. Furthermore, we demonstrate its effectiveness in simulations and lab experiments with a quadrotor that needs to reach a goal position in a complex static environment. The code is efficiently implemented on the quadrotor’s embedded computer to ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC formulation, this allows us to increase the planning horizon by a factor of 5, which results in significantly better performance.},
	language = {en},
	urldate = {2024-12-02},
	publisher = {arXiv},
	author = {Benders, D. and Köhler, J. and Niesten, T. and Babuška, R. and Alonso-Mora, J. and Ferranti, L.},
	month = nov,
	year = {2024},
	note = {arXiv:2406.11506 [cs]},
	keywords = {Computer Science - Robotics},
}

To efficiently deploy robotic systems in society, mobile robots need to autonomously and safely move through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby obstacles. However, the limited computation power available on typical embedded robotic systems, such as quadrotors, poses a challenge to running MPC in real-time, including its most expensive tasks: constraints generation and optimization. To address this problem, we propose a novel hierarchical MPC scheme that consists of a planning and a tracking layer. The planner constructs a trajectory with a long prediction horizon at a slow rate, while the tracker ensures trajectory tracking at a relatively fast rate. We prove that the proposed framework avoids collisions and is recursively feasible. Furthermore, we demonstrate its effectiveness in simulations and lab experiments with a quadrotor that needs to reach a goal position in a complex static environment. The code is efficiently implemented on the quadrotor’s embedded computer to ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC formulation, this allows us to increase the planning horizon by a factor of 5, which results in significantly better performance.

@inproceedings{tsolakis_colregs-aware_2022,
	title = {{COLREGs}-aware {Trajectory} {Optimization} for {Autonomous} {Surface} {Vessels}},
	url = {paper=https://r2clab.com/wp-content/uploads/2022/08/COLREGs_aware_Trajectory_Optimization_for_Autonomous_Surface_Vessels_final.pdf video=https://www.youtube.com/watch?v=bsmOtoxRZKU},
	abstract = {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 trajectory optimization algorithm that guarantees safe navigation in mixed-traffic conditions,
that is, in traffic environments with human operated vessels. The choice of an optimizationbased approach enables the formalization of abstract verbal expressions, such as traffic rules,
and their incorporation in the trajectory optimization algorithm along with the dynamics and
other constraints that dictate the system’s evolution over a sufficiently long receding horizon.
The ability to plan considering different types of constraints over a long horizon in a unified
manner leads to a proactive motion planner that mimics rule-compliant maneuvering behavior.
The efficacy of the derived algorithm is validated in different simulation scenarios.},
	booktitle = {14th {IFAC} {Conference} on {Control} {Applications} in {Marine} {Systems} ({CAMS})},
	publisher = {IFAC},
	author = {Tsolakis, A. and Benders, D. and de Groot, O. and Negenborn, R. R. and Reppa, V. and Ferranti, L.},
	year = {2022},
	note = {Finalist for the IFAC CAMS Young Author Award},
	keywords = {key\_collision\_avoidance, key\_maritime, key\_motion\_planning, key\_mpc},
}

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 trajectory optimization algorithm that guarantees safe navigation in mixed-traffic conditions, that is, in traffic environments with human operated vessels. The choice of an optimizationbased approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long receding horizon. The ability to plan considering different types of constraints over a long horizon in a unified manner leads to a proactive motion planner that mimics rule-compliant maneuvering behavior. The efficacy of the derived algorithm is validated in different simulation scenarios.