Persistent Monitoring for Indoor Farming using Static and Mobile Sensors

A Two-Layer Control Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Network

“…An example of the considered scenario is the monitoring of a field, where the grids correspond to areas that each static sensor needs to cover [25]. However, coverage holes (unmonitored areas) may appear, e.g., due to malfunction or lack of sensors [29], [30].…”

“…where τ ij denotes the pheromone concentration of pair ij, η k ij denotes the heuristic function of pair ij for ant k, and α and β denote the importance of the pheromone and the heuristic value respectively. Here, allowed k is the set of robots and regions that can be chosen by ant k. In our previous work [25], allowed k is defined as the robots that have not been allocated to any region and the regions that still have an "uncovered" area. Here, we modify the definition of allowed regions in allowed k into two conditions: 1) If there are still regions that have not been paired with any robot, then the regions that can be selected are the regions that have not been assigned with any robot; 2) If all regions have been allocated with at least one robot, then the regions that can be selected are the regions that have not been fully covered.…”

“…In this paper, we propose a novel two-layer control framework to persistently monitor the targeted locations in a large field, here formulated as a set of targeted grids, to improve the coverage performance and reduce the robots' travel distance. Specifically, the first layer (task allocation strategy) encapsulates the targeted grids as several smaller regions via Kmeans clustering [23], [24] followed by assigning the robots to monitor the regions accordingly via a modified ant colony optimization (M-ACO) [25]. Then, the second layer (per-sistent monitoring algorithm) models the importance of the area in each smaller region as a density function and further generates the control input for each robot based on a persistent coverage control algorithm [20], [21].…”

“…• We propose a modified ant colony optimization (M-ACO) to find the most suitable agents (robots) to cover the tasks (regions), where the earlier version is presented in [25]. The proposed M-ACO in [25] prioritizes the compatibility of the task area with the total capability of the agent(s) allocated to that particular task, resulting in a limitation where task(s) with a small area may not be covered by any agent. In this work, modifications to the M-ACO are introduced to guarantee that each task is covered by at least one agent.…”

“…The design is also a novel feature of our coverage algorithm, as most of the literature on coverage control assumes that the density function is given. Furthermore, compared to our previous framework in [25], we improve the encapsulation of the targeted grids as a convex hull (formerly a box rectangle) which can further improve the coverage performance. Collision avoidance between the robots is also explicitly considered by imposing additional constraints using the control barrier function approach [6], [26]- [28].…”