Collective motion is an omnipresent, fascinating phenomenon. Swarming individuals aggregate, coordinate and move, utilizing only local social cues projected by conspecifics in their vicinity. Major theoretical studies assumed perfect information availability, where agents rely on complete and exact knowledge of inter-agent distances and velocities. However, the sensory modalities that are responsible for the acquisition of environmental information were often ignored. Vision plays a central role in animal perception, and in many cases of collective motion, it is the sole source of social information. Here we investigate a vision-based collective motion model inspired by locust marching bands. We address two major challenges: the evaluation of distance and velocity and visual occlusions. We compare three strategies an agent can use to interpret partially occluded visual information. These differ in the visual cognition capabilities of the agent and the respective computational requirements.In silicoexperiments conducted in various geometrical conditions show that the three approaches display different rates of convergence to an ordered state: The least computationally-demanding approach, in which no peer recognition takes place, shows slower convergence in geometrically-constrained environments. Our findings provide insights into the visual processing requirements from biological as well as artificial swarming agents in complex settings.

Collective motion (CM) takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during CM the individuals of the group avoid collisions. These CM and collision avoidance (CA) behaviors are based on input from the environment such as smell, air pressure, and vision, all of which are processed by the individual and defined action. In this work, a novel vision-based CM with CA model (i.e., VCMCA) simulating the collective evolution learning process is proposed. In this setting, a learning agent obtains a visual signal about its environment, and throughout trial-and-error over multiple attempts, the individual learns to perform a local CM with CA which emerges into a global CM with CA dynamics. The proposed algorithm was evaluated in the case of locusts’ swarms, showing the evolution of these behaviors in a swarm from the learning process of the individual in the swarm. Thus, this work proposes a biologically-inspired learning process to obtain multi-agent multi-objective dynamics.

Collective motion takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during collective motion the individuals of the group avoid collisions. These collective motion and collision avoidance behaviors are based on input from the environment such as smell, air pressure, and vision, all of which are processed by the individual and defined action. In this work, a novel vision-based collective motion with collision avoidance model (i.e., VCMCA) that is simulating the collective evolution learning process is proposed. In this setting, a learning agent obtains a visual signal about its environment, and throughout trial-and-error over multiple attempts, the individual learns to perform a local collective motion with collision avoidance which emerges into a global collective motion with collision avoidance dynamics. The proposed algorithm was evaluated on the case of locusts’ swarms, showing the evolution of these behaviors in a swarm from the learning process of the individual in the swarm. Thus, this work proposes a biologically-inspired learning process to obtain multi-agents multi-objective dynamics.