# A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling
## 1 Introduction  
Robots are ideally deployed when a task is mundane, dangerous, or otherwise undesirable for humans. Their deployment can enhance the safety of workers by minimizing their presence at a scene and increase productivity by achieving greater efficiency and repeatability compared to human workers. The radiation surveillance of nuclear facilities can benefit from such automation due to the mundane and dangerous nature of the task [1], the lack of trained radiation experts in the field [2], and the high cost of deconstruction and decommissioning (D & D). Despite these advantages, robots deployed in unstructured environments such as nuclear facilities face numerous challenges that limit their ability to operate effectively [3]. Physical limitations such as size or having wheels sometimes prevent a robot from being able to reach or traverse complicated areas. However, even when platforms have more flexible physical characteristics, such as quadruped (four-legged) robots, their intelligence becomes the major limiting factor against successful deployment and the completion of tasks [4]. To overcome this, some solutions use teleoperation, through whicha human enters the loop and remotely controls the platform [5]. However, this removes some advantages, such as not requiring human labor to perform mundane tasks.  
Further challenges are faced when addressing radiation surveillance using autonomous systems. Due to the size of facilities and the amount of time it takes to take readings with radiation sensors, an exhaustive approach to mapping, through which every reachable point on a grid is measured, becomes too costly. This is further exacerbated by the limited battery capacity for a typical mobile robot.  
To address challenges regarding both robot intelligence and mapping costs, we propose a system that performs adaptive sampling using highly adaptable behavior inspired by the behavioral robotics philosophy using a robot with good traversability. The novelty of this paper comes from the approach to the problem and the novelty of the system. The unique approach involves performing Bayesian optimization with a dynamic change to an optimization parameter based on the collision of a robot manipulator. This is intended to overcome the complexities involved with using a mobile manipulator planner that can avoid obstacles. The system itself is novel in its use of a manipulator-equipped quadruped robot to perform radiation mapping via adaptive sampling. The primary finding of this study is that employing a dynamic exploration-exploitation optimization parameter significantly aids the robotic system in avoiding failure loops caused by attempting to repeatedly sample unreachable regions. By adjusting this parameter based on the number of consecutive soft collisions with the arm, the optimizer can shift its decision boundary away from the unreachable region. Our experiments showed that at least minimal changes to the parameter were sufficient to escape these local failure loops, whereas static values resulted in continued sampling in the infeasible areas. Although the approach proved effective, the limited size of the test bed and the number of trials prevented us from deriving deeper quantitative insights into the numeric relationship between the optimization outcomes and the dynamic parameter changes.