Self-Organized Aggregation Behavior Based on Virtual Expectation of Individuals with Wave-Based Communication

Abstract

In this study, a microscopic model for a swarm of mobile robots is developed to implement self-organized aggregation behavior. The proposed model relies on the concept of subjective expectation, which is defined as the “minimum wished cluster size” of a robot in the swarm. During the whole process, a robot’s expectation constantly changes based on context awareness. This awareness is obtained by employing a low-cost communication system commonly found in swarm robot studies: infrared-based communication. Robots can make their own decisions by comparing their expected and estimated observed cluster sizes, which leads to macroscopic swarm aggregation. However, due to the limitations of local communication and mobility, robots are restricted in their ability to perceive global information, particularly regarding cluster size. Inspired by the slime mold aggregation process, a wave-based communication mechanism is implemented to help robots estimate a cluster size. Moreover, each transmission includes a modulated message that allows robots to explicitly share their knowledge with others. In this way, despite the fact that the robot may not belong to that cluster due to its perception range, it can easily grasp the cluster size when passing the cluster. Once a robot detects a desired cluster, it can approach this cluster with its direction determined by using average origin of wave (AOW) method. The performance of the aggregation algorithm was tested both in simulation and with a real swarm robot. Dispersion metrics and cluster metrics were employed to evaluate the proposed algorithm’s performance. The results show that the proposed microscopic model utilizes collective behavior which aggregates all randomly distributed robots into a single aggregate cluster with a reasonable swarm density and evaluation time.

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

“…Additionally, Table 2 displays papers about remote sensing navigation, focusing on LiDAR, laser ranger, and infrared sensors. The tasks include exploration [ 63 , 64 , 66 , 71 ], wheelchair [ 78 ], transport [ 65 , 73 , 74 , 75 ], fire services or search and rescue [ 70 ], floor cleaning [ 68 ], tracking [ 76 ], education [ 72 ], and caring and service [ 79 ]. Some approaches are combined with path planning algorithms, including metaheuristic algorithm, potential field, and A* for navigation [ 66 , 68 , 70 , 78 ].…”

“…The issues of the optimization of the navigation algorithm, decision-making strategy, energy consumption, and safety are essential for deployment [ 66 , 73 ]. The swarm size, behavior, and coordinated movements must also be considered [ 75 ].…”

“…Real-world experiments remain a challenge [ 50 , 75 , 109 ]. Future research should test and validate approaches in different complex environments, not just be restricted to a specific or simple environment.…”

“…The robots used ultrasonic, infrared sensors, and light-dependent resistance. Gia Luan and Truong Thinh [ 75 ] implemented a wave-based communication mechanism inspired by slime mold aggregation to measure the cluster size for a swarm of robots with infrared sensors. The robot could grasp the cluster size and detect a desired cluster, then approach the cluster by the average origin of wave method.…”