# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 5 Simulations, Experiments, and Results
### Effect of Coverage Competency  
We validate the inclusion of coverage competency in the Gazebo-ROS simulation environment and present our findings in Table 2. The table demonstrates that the inclusion of coverage competency reduces search time by an average of 25% as shown in bold. The results validate that we see a significant performance boost in our metric of interest when coverage competency is considered. Each trial is set with different initial conditions to demonstrate that including coverage competency outperforms in any general setting; however, for a fair comparison, identical initial conditions are considered for each individual  
Figure 5: Block diagram detailing the implementation of the proposed method for simulation and real robot experiments. CONCERTS generates the initial set of plans via the search server and ultimately delivers a path to each agent. Each agent manages a full autonomy stack and performs search over the environment. Figure 6: This figure depicts a side-by-side of the home outdoor environment with three-robot searching. Yellow regions are those that have been covered by the agents. Dots in RGB represent the waypoints assigned to each agent. In (**a**) the search begins with three agents. The blue agent fails after a short time, then a re-planning step occurs. (**b**) depicts the results of the re-planning. Figure 7: The scenario with prior target information. Yellow regions are those that have been covered by the agents. Colored dots and lines represent the waypoints and paths assigned to each agent. Agent current position at that moment is marked by the white costmap overlayed on the yellow regions. (**a**) The Rviz interface of the four robots performing search. Red particles represent contexts, or particle filters, to represent possible target locations. (**b**) A top-down view of the region of destroyed homes where the target is found. The target location is given by the cyan circle in (**a,b**). trial with and without coverage competency. In all cases, it is confirmed that the search time is significantly reduced by the H-Clustering step. Additionally, we investigate \(WAF\) to ensure that CONCERTS is effective at incorporating coverage competency. In Table 2, it is shown that \(WAF\), as expected, is reduced with the inclusion of coverage competency. WAF equal to zero indicates that the waypoints were distributed perfectly with respect to coverage competency of the team. We note that in Figure 8 that WAF slightly increases as the number of agents increases. This is expected as a standard deviation, or \(WAF\) will tend to increase as the number of samples, or number of agents, increases. Figure 8 displays WAF with and without consideration of coverage competency during the H-Clustering stage. As expected, the WAF is lower, or the distribution is fairer, when agent heterogeneity is included. It is clear that regardless of the number of agents, the WAF will be lower when coverage competency is considered and that the overall mission time will be reduced. This implies that leveraging robot characteristics for motion planning increases productivity from the perspective of the entire team. Ultimately, these results indicate that, while CONCERTS outperforms than the state of the art in area coverage with a homogeneous team, performance is further improved by including coverage competency when the team is heterogeneous. \(WAF\) allows measurement of the effectiveness of the inclusion of coverage competency, and it is clear based on the results that by including the term that the efficiency of the search is improved. The term validates the inclusion over 10 separate trials with randomized initial conditions, suggesting that this is a general result.