Next Article in Journal
Wind Turbine Technology Trends
Previous Article in Journal
sEMG-Upper Limb Interaction Force Estimation Framework Based on Residual Network and Bidirectional Long Short-Term Memory Network
Previous Article in Special Issue
Enhanced Hybrid Ant Colony Optimization for Machining Line Balancing Problem with Compound and Complex Constraints
 
 
Article

CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams

by 1,2,*, 2,3 and 2,3,*
1
Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
2
Human Centered Robotics Laboratory, The University of Texas at Austin, Austin, TX 78712, USA
3
Department of Aerospace Engineering, The University of Texas at Austin, Austin, TX 78712, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Pedro Castillo Garcia, Martin Saska and Alexandre Brandão
Appl. Sci. 2022, 12(17), 8649; https://doi.org/10.3390/app12178649
Received: 11 July 2022 / Revised: 24 August 2022 / Accepted: 25 August 2022 / Published: 29 August 2022
(This article belongs to the Special Issue Multi-Robot Systems and Their Applications)
CONCERTS is aimed at Search and Rescue and related applications in urban and indoor environments where mission completion time is critical.
This paper proposes CONCERTS: Coverage competency-based target search, a failure-resilient path-planning algorithm for heterogeneous robot teams performing target searches for static targets in indoor and outdoor environments. This work aims to improve search completion time for realistic scenarios such as search and rescue or surveillance, while maintaining the computational speed required to perform online re-planning in scenarios when teammates fail. To provide high-quality candidate paths to an information-theoretic utility function, we split the sample generation process into two steps, namely Heterogeneous Clustering (H-Clustering) and multiple Traveling Salesman Problems (TSP). The H-Clustering step generates plans that maximize the coverage potential of each team member, while the TSP step creates optimal sample paths. In situations without prior target information, we compare our method against a state-of-the-art algorithm for multi-robot Coverage Path Planning and show a 9% advantage in total mission time. Additionally, we perform experiments to demonstrate that our algorithm can take advantage of prior target information when it is available. The proposed method provides resilience in the event of single or multiple teammate failure by recomputing global team plans online. Finally, we present simulations and deploy real hardware for search to show that the generated plans are sufficient for executing realistic missions. View Full-Text
Keywords: multi-robot systems; target search; multi-robot search; coverage path planning; exploration multi-robot systems; target search; multi-robot search; coverage path planning; exploration
Show Figures

Figure 1

MDPI and ACS Style

Kim, M.; Gupta, R.; Sentis, L. CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams. Appl. Sci. 2022, 12, 8649. https://doi.org/10.3390/app12178649

AMA Style

Kim M, Gupta R, Sentis L. CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams. Applied Sciences. 2022; 12(17):8649. https://doi.org/10.3390/app12178649

Chicago/Turabian Style

Kim, Minkyu, Ryan Gupta, and Luis Sentis. 2022. "CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams" Applied Sciences 12, no. 17: 8649. https://doi.org/10.3390/app12178649

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop