Swarm Of Autonomous Agents Research Articles

The impact of catastrophic collisions and collision avoidance on a swarming behavior

Swarms of autonomous agents are useful in many applications due to their ability to accomplish tasks in a decentralized manner, making them more robust to failures. Due to the difficulty in running experiments with large numbers of hardware agents, researchers often make simplifying assumptions and remove constraints that might be present in a real swarm deployment. While simplifying away some constraints is tolerable, we feel that two in particular have been overlooked: one, that agents in a swarm take up physical space, and two, that agents might be damaged in collisions. Many existing works assume agents have negligible size or pass through each other with no added penalty. It seems possible to ignore these constraints using collision avoidance, but we show using an illustrative example that this is easier said than done. In particular, we show that collision avoidance can interfere with the intended swarming behavior and significant parameter tuning is necessary to ensure the behavior emerges as best as possible while collisions are avoided. We compare four different collision avoidance algorithms, two of which we consider to be the best decentralized collision avoidance algorithms available. Despite putting significant effort into tuning each algorithm to perform at its best, we believe our results show that further research is necessary to develop swarming behaviors that can achieve their goal while avoiding collisions with agents of non-negligible volume.

EFFICIENT, SWARM-BASED PATH FINDING IN UNKNOWN GRAPHS USING REINFORCEMENT LEARNING

This paper addresses the problem of steering a swarm of autonomous agents out of an unknown maze to some goal located at an unknown location. This is particularly the case in situations where no direct communication between the agents is possible and all information exchange between agents has to occur indirectly through information “deposited” in the environment. To address this task, an e-greedy, collaborative reinforcement learning method using only local information exchanges is introduced in this paper to balance exploitation and exploration in the unknown maze and to optimize the ability of the swarm to exit from the maze. The learning and routing algorithm given here provides a mechanism for storing data needed to represent the collaborative utility function based on the experiences of previous agents visiting a node that results in routing decisions that improve with time. Two theorems show the theoretical soundness of the proposed learning method and illustrate the importance of the stored information in improving decision-making for routing. Simulation examples show that the introduced simple rules of learning from past experience significantly improve performance over random search and search based on Ant Colony Optimization, a metaheuristic algorithm.

Swarm Behavioral Inversion for Undirected Underwater Search

Coordinating the dynamics of large groups, or swarms, of autonomous underwater vehicles in order to search a given target area can be difficult due to the plurality of the system, environmental com- plications, and the prolonged and indefinite duration of the patrol. This paper examines the use of swarm inversion to optimize the behavioral dynamics of a swarm of autonomous agents in a patrol search with underwater morphological and environmental constraints. In partic- ular, the range of the forward-looking sensor range of agents varies spatially, requiring more search time in dark areas to maintain a high level of surveillance. This results in a tradeoff between the uniform coverage and surveillance frequency. The patrol fitness is determined via simulation feedback, and particle swarm optimization is used to invert and refine the behavior of the swarm. The tradeoffs between high search frequency and search uniformity are examined, as well as the evolved swarm's adaptability to varying environmental conditions and robustness of agent numbers. Results demonstrate that swarm inver- sion can yield effective agent behaviors for maintaining a presence in a given target zone despite stochastic navigation and an anisotropic environment.