# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 5 Simulations, Experiments, and Results
### Real Robot Environment  
The team is comprised of two Unitree A1 quadrupeds with different sensor suites and a Toyota HSR. One A1 quadruped is equipped with a Velodyne Puck 3D LiDAR and a RealSense D435. The other is equipped with a RPLidar A3 2D LiDAR and a Realsense D435. Both quadrupeds have Intel NUC Mini PCs on board which communicate with the robot hardware. The RPLidar A3 has a smaller FoV than the Velodyne Puck LiDAR and the use of different sensors is to introduce heterogeneity within the team for two agents that would otherwise have identical coverage competency scores. The HSR is equipped with Hokuyo 2D LiDAR, RGB-D camera sensing and has an onboard Jetson TK1. All robots run Episodic non-Markov Localization [33]. The central search server runs on a remote laptop, and we use Robofleet [34] for efficient communication between the server and agents. The search server receives pose and sensing information from each agent to compute potential paths. Each agent decides the final path based on its own onboard path planner to avoid un-mapped obstacles. The search experiment is performed in the Aerospace Engineering Building at the University of Texas at Austin while looking for a static volleyball. Figure 9a shows the initial positions of the robot team members and corresponding waypoints. A snapshot of the experiment is depicted in Figure 9b and actual paths are visualized in Figure 9c. Figure 10 gives snapshots at five different moments during the three-robot search in the Aerospace Engineering Building. The top row depicts the HSR, or Agent 1 in Figure 9.  
Figure 9: (**a**) Experimental validation for a three-robot search in the Aerospace Building fourth floor. The red, green, and blue markers represent the clusters for each agent. (**b**) Mission completion with Agent 2 converging to the target of interest. (**a**) yellow regions are those which have been explored, while the white areas are regions of uncertainty. Target location is shown as a red box. The object recognition is performed using the YOLO [35] algorithm to detect a target. (**c**) Trajectories of the robot team are visualized in the map. The second row is the A1 Quadruped with Velodyne Puck LiDAR and is Agent 2 in Figure 9. This agent is the teammate that finds the target volleyball. Finally, the bottom row is the other A1 Quadruped with the RPLidar A3 and is Agent 3 in Figure 9. The entire framework is developed in ROS Melodic, and the search server is implemented using ROS actionlib library. At initial planning or during re-planning, the search server generates a set of paths for a robot team and then sends those to each agent (see Figure 5). Video of experimental validation and simulations can be found at our project website [[https://sites.google.com/utexas.edu/concerts-coverage-competency/home](https://sites.google.com/utexas.edu/concerts-coverage-competency/home)]([https://sites.google.com/utexas.edu/concerts-coverage-competency/home](https://sites.google.com/utexas.edu/concerts-coverage-competency/home)) (accessed 28 August 2022). Experiments indicate that search performance is significantly impacted by the initial setting. The main reason for this is that H-Clustering is computed using the initial positions of the robots. For example, if the starting points of all robots are gathered, most waypoints will be allocated to an agent has high coverage competency, leading to uneven distribution. To overcome this undesirable situation, the proposed clustering algorithm flexibly tries to find new centroids for certain agents as shown in the Equation (6). For example, we use only one robot's position as a fixed centroid for H-clustering out of three robots, and clustering can be performed to find new centroids for the other two robots by setting \(K=3\), \(K=1\). However, in this experiment, robots are distributed evenly throughout the search region and, as a result, the distribution of waypoints neatly reflects coverage competency scores. The proposed method has room for improvement using path planning with various FoV shapes.