# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 6 Conclusions  
This article explores online search for a general heterogeneous multi-agent system. First, we perform a clustering method which assigns some region of space to each agent given their _coverage competency_ then solves the TSP for each of those agents. We employ an information-theoretic utility function for sampling-based optimization, which results in a path for each of \(n\) agents.  
Figure 10: Snapshots of three robot search experiments in the Aerospace Engineering Building. (**a**) This row shows the HSR (Agent 1) at different instances performing its search for the target. (**b**) A1 with Velodyne Puck LiDAR (Agent 2) and in the final frame, it finds the target object (the last frame). (**c**) The bottom row depicts the other A1 with RPLidar (Agent 3) performing a search.  
We perform baseline testing versus a state-of-the-art algorithm for CPP and verify that our proposed method generates plans faster and results in a quicker overall area coverage time by 9%. We then validate CONCERTS by deploying and executing search in a high-fidelity Gazebo simulation of three different settings to demonstrate how our method works with and without prior target information. Furthermore, we perform Gazebo simulation tests to validate the inclusion of coverage competency for heterogeneous teams. We observed that including coverage competency reduced search completion time by 25%. In addition, as the number of team members of the robot increases, so does the impact of including coverage competency. Specifically, when the team comprises 50 heterogeneous robots then the impact of including coverage competency is doubled, as measured by \(WAF\). The distribution of waypoints is two times closer to a perfect distribution than the case without coverage competency. Finally, we demonstrate the efficacy of this proposed method with real robot experiments in an indoor setting. Overall, results validate the effectiveness and robustness of the proposed algorithm.  
Several future works remain; however, current efforts are towards including a target motion model for dynamic targets and performing continuous coverage. Additional future works should involve outdoor field testing of CONCERTS with the addition of planning using an RGB-D camera FoV to replace the square type. Finally, ongoing work should include curiosity regions, where agents are encouraged to explore regions of the FoV that do not match with static mapped obstacles.  
Conceptualization, M.K., R.G. and L.S.; methodology, M.K.; software, M.K. and R.G.; validation, M.K. and R.G.; formal analysis, M.K.; investigation, M.K. and R.G.; data curation, M.K. and R.G.; writing--original draft preparation, M.K. and R.G.; writing--review and editing, M.K., R.G. and L.S.; visualization, M.K. and R.G.; supervision, L.S.; project administration, M.K. and R.G.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
Research was sponsored by the Army Research Office and was accomplished under Cooperative Agreement Number W911 NF-19-2-0333. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. This work was also in part supported ONR Grant #N000141512507.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
CONCERTS  Coverage cCompeteNCy-basEd taRgeT Search  
TSP  Traveling Salesman Problem  
CPP  Coverage Path Planning  
FoV  Field of View  
SAR  Search and Rescue  
WAF  Waypoint Allocation Factor  
H-Clustering  Heterogeneous Clustering