# A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling
## 4 Discussion  
The results of these experiments indicate that the modeled behavior involving dynamically changing the optimization parameter based on soft collisions offers utility for long-term surveillance applications. In all trials where obstacles were present and the explore-exploit variable \(\xi\) was static, the robot ended up in a failure loop, trying to sample inside an unreachable space. Every other trial, the robot could successfully take forty samples using the radiation sensor. Further, the successful trials all converged to the control map from the first trial, except for one trial where the \(\xi\) value was set low. The data do not reveal, among the successful trials, which \(\xi\) values performed better due to the fact that the robot could converge faster purely from chance if it decided to take its first sample closer to the radiation source. For the two unsuccessful trials, a low and a high \(\xi\) value both resulted in a failure to escape when sampling an unreachable region. Additionally, for both trials involving mapping with a dynamic \(\xi\) value in a cluttered environment, the arm never collided multiple times consecutively, except for one time where three consecutive collision triggers occurred, and this was due to the legs stomping the ground too aggressively. Thus, to reduce false positives, we should further experiment with the relationship between the robot manipulator and the impulse it experiences. Additionally, we may examine whether foot stomping affects the directional force components on the arm, though this analysis may be more complex. Based on the results, according to Equation (5), the value of \(\xi\) never exceeded approximately 1.5, making the total range across the trials between 1.2 and 1.5, a relatively narrow range in this application, according to observations during this research. These facts suggest that merely changing the explore-exploit parameter, even by a little bit, is more important for escaping an unreachable zone than the value of \(\xi\) itself. This could be an important detail for larger environments where you would want the optimization parameters to be tuned based on finding radiation, as opposed to obstacle avoidance. Overall, there is limited information we can derive due to the small test bed size and number of trials. The dominant factor was the qualitative change from a static to a dynamic \(\xi\), rather than the specific numerical value. To further test whether the rate of changing the \(\xi\) value affects the convergence rate or overall mapping efficiency (particularly in escaping failure loops), future work could involve larger environments  
Figure 14: Mean square error of radiation plots relative to the control trial’s fitted Gaussian plot for each sample of every successful trial. and more extensive sampling per trial. In such experiments, large spaces free of obstacles would serve as a control for measuring the Bayesian optimization system's convergence rate. Then, an obstacle-rich environment could be introduced to determine how both the qualitative and quantitative aspects of \(\xi\) adjustments influence performance. Although the experiment only involved a small space, rather than a larger area, we can estimate the battery life implications were we to expand this to a nuclear facility. The specifications were obtained from Boston Dynamics' website [28]. Each radiation measurement was taken over a 15-s period, and for safety purposes, the robot moved at a speed slower than its maximum nominal speed of 1.6 m/s. Assuming the robot operates consistently at 0.8 m/s and that the average runtime for a single battery charge is approximately 90 m, taking 50 measurements would require 750 s (12.5 m). Under these conditions, the robot could traverse a square perimeter of approximately 68 m on a single full charge. Although nuclear facilities can be larger, the robot is capable of autonomously docking and undocking from a charging station; therefore, the battery limitation can be mitigated by strategically placing charging stations.