A coverage path planning approach for autonomous radiation mapping with a mobile robot

Abstract

In nuclear and radiation-related industries, it is crucial to ensure that the radiation dose exposure to the radiation worker is maintained below the permissible dose limit. A radiation map is a useful tool for visualizing the radiation distribution across the work area and for coordinating activities involving the hotspots (high radiation areas). The goal of this work was to design and implement a coverage path planning approach for autonomous radiation mapping carried out by a mobile robot. Given a 2D occupancy map, a method to generate uniformly distributed sampling points was proposed. The geometry of the region of interest, the radiation detector module, and the radiation measurement parameters were considered in formulating the sampling positions. Next, the coverage path planning planner integrates the nearest neighbor and depth-first search algorithms to create a continuous path that enables the robot to visit all the sampling points. The K-means clustering algorithm is added for systematic coverage of a large number of sampling points. The clustering provides options to partition the region of interest into smaller spaces, where the robot would perform the mapping cluster by cluster. Finally, the method of building the radiation map from the acquired data was also presented. The approach was implemented in ROS using a commercial mobile robot equipped with a Geiger–Muller detector. The performance and reliability of the proposed approach were evaluated with a series of simulations and real-world experiments. The results showed that the robot is able to perform autonomous radiation mapping at various target areas. The accuracy of the generated radiation map and the hotspots classifications were also compared and evaluated with conventional manual measurements. Overall, the theoretical frameworks and experiments have provided convincing results in the automation of hazardous work and subsequently toward improving the occupational safety of radiation workers.

Preliminary results of the autonomous radiation mapping in Malaysian Nuclear Agency

“…Following that, Autonomous Mobile Robot for Accurate Radiation Detection and Mapping (AMoRA) were developed to map the distribution of radiation in the area [5]. AMoRA ensures measurement accuracy by indicating hotspots on the physical map [6]. This autonomous radiation measurement capability offered by AMoRA can effectively decrease exposure time to radiation.…”

Autonomy in UAV Civilian Applications

“…One proposed solution to this problem is to separate the area into regions, each corresponding to a single-UAV task. Authors in [51] created regions sized to the energy autonomy of one vehicle by discretizing the area, to be scanned, into a grid of cells and clustering obstacle-free cells using the k-means clustering algorithm. The coverage path is computed using a depth-first search algorithm on the cells of the assigned region.…”

Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning

“…Another noteworthy offline multi-robot CPP algorithm is the one developed by Rahman et al [24] for autonomous radiation mapping using a mobile robot. The primary objective of this algorithm is to create coverage paths that a single robot can traverse to conduct radiation mapping in a designated area.…”

“…The complexity of CPP intensifies when we move from single-robot systems to multirobot systems. Multi-robot CPP involves devising paths for multiple robots to ensure comprehensive and efficient coverage of a larger or more complex area [17][18][19][20][21][22][23][24][25][26]. A pressing issue in multi-robot CPP is the division of the operational area among the robots, a problem known as the 'area division problem'.…”