.png){kind=icon}
Return
Active Learning-Based Model Predictive Coverage Control
.png){kind=icon}
OA
AI
DOI：10.1109/TAC.2024.3365569.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The problem of coverage control, i.e., of coordinating multiple agents to optimally cover an area, arises in various applications. However, coverage applications face two major challenges: 1) dealing with nonlinear dynamics while respecting system and safety critical constraints and 2) performing the task in an initially unknown environment. We solve the coverage problem by using a hierarchical framework, in which references are calculated at a central server and passed to the agents' local model predictive control (MPC) tracking schemes. Furthermore, to ensure that the environment is actively explored by the agents a probabilistic exploration-exploitation tradeoff is deployed. In addition, we derive a control framework that avoids the hierarchical structure by integrating the reference optimization in the MPC formulation. Active learning is then performed drawing inspiration from Upper Confidence Bound (UCB) approaches. For all developed control architectures, we guarantee closed-loop constraint satisfaction and convergence to an optimal configuration. Furthermore, all methods are tested and compared on hardware using a miniature car platform.
Keywords:
Agents and autonomous systems
cooperative control
coverage control
machine learning
NL predictive control
Agents and autonomous systems
cooperative control
coverage control
machine learning
NL predictive control
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
Organization
Cited Papers
Luteinizing Hormone-Releasing Hormone (LHRH) Biosynthesis and Secretion in Embryonic LHRH NeuronsOAAI
OAAIShareSave
ShareSaveShareSaveUsing an isolated population boom to explore barriers to recovery in the keystone Caribbean coral reef herbivore Diadema antillarum.png)
Coral Reefs
Coral ReefsIF0
PREAI
ShareSaveModel Predictive Control of Nonholonomic Mobile Robots Without Stabilizing Constraints and Costs
PREAI
ShareSaveShareSaveShareSave
View more