# Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction
## 2 Literature Review  
Area coverage methods using multiple agents can be applied to detect randomly distributed pucks in the workspace. In [4; 5; 6; 7], the authors handled how to make multiple agents perform area coverage in a 2D workspace. In [8; 9], the authors utilized 2D Voronoi tessellations for making agents expand the agent network based on information sharing with neighbors. However, [PERSON]. [8; 9] did not consider obstacles in the workspace. The authors of [10] considered multiple agents that are instructed to cover a field of interest (Fol). After agents complete a task at one Fol, they move to a new FoI, which may be far away from the current FoI. According to [10], agents can automatically adjust their deployment density in the new FoI based on the requirements of various tasks or regions. However, coverage strategies for an 3D cluttered unknown workspace were not considered in these papers. Moreover, the authors did not handle multi-agent foraging, which involves multiple agents collecting randomly distributed pucks and carrying them back to the base. There are many papers on multi-agent foraging strategies inspired by nature, e.g., ant, bee, and physarum polycephalum [2; 3; 11; 12; 13; 14; 15; 16]. Inspired by nature, [PERSON]. [17; 18] showed that random searches relying on puck distribution can be optimized by considering individuals in foraging. The superdiffusive property of Levy walks, in which the mean squared displacement increases superlinearly [12; 19], is a known mechanism that nature uses to handle low information levels from sensory systems. Inspired by the behaviour induced by pheromones released by ants, navigation methods based on landmarks or markers such as RFID tags were addressed in [15; 20; 21]. Even an extremely sensor-poor and processing-poor robot can perform navigation when accessing markers [21] or RFID tags [20]. Inspired by the pheromone trails of ants, [PERSON]. [2; 3; 22; 23] addressed robotic systems mimicking ants' foraging. In [16], the author developed a virtual pheromone (VP) algorithm inspired by the foraging behavior of ants, with the exception that the agents themselves act as pheromone locations (beacons) and these virtual pheromones are laid and detected through direct local agent-agent communication. The authors of [14] proposed foraging algorithms inspired by bees. In [13], the foraging process (search and contraction) of physarum polycephalum, a unicellular and multi-headed slime mold, was simulated utilizing multi-agent systems. However, mimicking natural behaviors cannot guarantee that every puck is detected by an agent within a finite time. In other words, mimicking natural behavior is not provably complete for searching all pucks. Moreover, it is not clear what hardware can perform the functionality of a pheromone. In our paper, we specify the sensor systems required for the proposed foraging strategy. To cover a workspace with large uncertainty, random searches may be beneficial; however, random maneuvering can easily lead to a lost agent in cases where GPS is not available. For maintaining network connectivity, each agent needs to move while considering the maximum sense-communication range of an agent. This inspired us to develop a multi-agent foraging strategy which relies on local sensors with the maximum sense-communication range. In our paper, the proposed search strategy is provably complete in detecting all pucks in a 3D bounded workspace which is not known in advance. Advances in reinforcement learning have recorded success in various domains. Considering a 2D small workspace with no obstacles, [PERSON]. [24] formulated multi-robot learning for learning foraging behavior in a group of four ground robots. In [25], the author investigated the application of reinforcement learning as a rehearsal (RLaR) for the multi-agent foraging problem. However, the reinforcement learning approach in [25] cannot assure that every puck is found under the subsequent search strategy.