# A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling
## 2 Methodology
### Behavior Modeling
Thus, the range of values for this parameter is between 1.2 and 12. The threshold for detecting a collision is met if the end effector experiences an overall force magnitude greater than 10 N for at least 0.1 s. This was found to be the shortest duration of time needed to ensure that noisy force data did not excessively trigger collision detection mistakenly due to momentum from movement or from force imparted via the impact of the legs on the floor. The motivation for varying the \(\xi\) parameter is illustrated in Figure 3. Varying this variable typically balances the criteria of reducing overall model uncertainty and exploiting higher radiation regions. However, the motion planner used does not allow the arm to avoid obstacles, nor does the Gaussian process estimation of the map possess an awareness of said obstacles in the environment. This may result in the optimization algorithm selecting points that are impossible for the arm to reach. If the environment is obstacle-rich, regions where the algorithm may want to conduct measurements have a higher chance of being in an unreachable area. In order to counter this problem and maintain the ability to exploit high-radiation spots, the behavior of Figure 3 was developed. It was hypothesized that, if the robot enters a sampling failure loop trying to take measurements in unreachable areas,increasing the exploration tendency will aid the robot in leaving such a failure loop and continuing to develop the radiation map by sampling other points. Figure 3: Trajectory diagram showing a theoretical execution of way-points shown as red stars in a sequential order indicated by the numbers with and without modifying the parameter \(\xi\), according to Equation (5). The quadruped robot is shown as an orange rectangle, and environmental obstacles are shown as various white shapes. Figure 2: Behavior flowchart for robot showing key decisions for constructing a radiation map of the environment. To enhance the safety of the robot, a few measures were added, such as a return pose, making the force threshold time-dependent, and setting the robot to a slower speed. The return pose shown in Figure 4 allows the robot to start at an ideal position after each attempt is made to traverse towards a point. In Figure 5, the ROS2 communication for this application is described. Only select interfaces, such as topics and nodes, are displayed for clarity purposes, and the names have been simplified. The scanning behavior node works with the other three nodes in order to perform the behavior described in Figure 2. This includes monitoring the force on the manipulator and sending movement commands to the main Spot ROS2 wrapper node. It also requests for the radiation sensor node to take a measurement for a specified duration, returning the current count number as feedback and the final average as a result in counts per second. The same node also requests the Bayesian optimization node to update the Gaussian model after new measurements are taken and requests new points to move towards based on the most current model. Figure 4: Return pose that the robot returns to after each attempt at going to a point for measurement. Figure 5: ROS2 diagram showing how nodes (ovals) communicate with each other via the actions and services of topics (rectangles).