# A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling
## 1 Introduction
### Related Work  
Various strategies are used to create radiation maps with mobile robots in order to accomplish diverse goals. In some cases, the maps involve multiple radiation sources [6; 7; 8], and in others, only a single source is present [9; 10]. Sometimes the platform tries to find the location of radioactive sources [11], and sometimes it attempts to reduce the radiation dose while mapping [12]. The purpose can vary from emergency response [13; 14] to routine characterization [15; 16]. In some research efforts, the radiation being detected can be both alpha and beta [12]; in others, only gamma is measured [9]. The platforms used can be single or multiple [17] robots of various types, such as aerial [18] and ground platforms [11]. The approach can often be exhaustive when a high quantity of measurements are taken across an area [9; 10; 11; 18] or adaptive when fewer measurements are selected via an optimization algorithm [19]. Within the literature on robots that utilize adaptive sampling, the choice of the prediction model can vary, but it is usually a Gaussian process [19], and the choices of the information function, number of robots, goal of exploration or exploitation, and other factors are all variable. Overall, using autonomous robots to perform some form of radiation mapping is a large and diverse field of research that spans a wide range of goals and challenges that are addressed in many differing ways. The recent literature on autonomous radiation mapping with mobile robots explores various mapping methods, robot form factors, and obstacle-handling techniques (Table 1). In this work, we use Bayesian optimization, a common strategy for mapping, including radiation mapping. Our platform is a quadruped robot, which, despite being less energy-efficient, offers superior traversability across diverse terrains. To avoid obstacles, we rely on a novel soft collision behavior, which, to our knowledge, is unique within the literature. In contrast, the study [6] takes a more traditional approach to both obstacle avoidance and the robot form, using a differential wheeled robot. Their system integrates a costmap with local and global planners for navigation. For mapping, they employ receding horizon planning, an iterative strategy that balances the exploration-exploitation trade-off, similar to Bayesian optimization. However, some advantages of their approach over Bayesian optimization include its real-time updates and its being better suited for handling multiple radiation sources, as seen in this paper. In [7], a small differential wheeled robot equipped with a laser range finder and other sensors constructs a costmap for obstacle avoidance. Their approach employs Bayesian inference, a robust probabilistic mapping method. However, rather than using an iterative approach, they predefine initial measurement points and infer the radiation distribution based on that data. The study [9] also employs a costmap-based approach with a TurtleBot, a small differential wheeled robot. Instead of adaptive sampling, they use a unique algorithm to systematically measure each feasible grid cell from the costmap, making it a more exhaustive mapping approach. In [12], a small wheeled robot also uses a costmap for obstacle detection. Their approach combines exhaustive coverage mapping with an adaptive strategy based on radiation readings, allowing the robot to avoid contaminated areas dynamically. Similarly, in [18], different exhaustive mapping strategies, including traversal search, binary search, and successive approximation, are explored. However, obstacle detection is not a concern in their work, as the drone operates at an altitude above all possible obstacles. The study [20] also employs a drone but incorporates obstacle awareness using a 3D grid classification system. Their mapping strategy is based on field estimation and propagation, rather than traditional Bayesian inference or exhaustive search.