Autonomous Multi-agents Research Articles

Fault-Resilient Collective Ternary-Hierarchical Behavior to Smart Factories

Smart factories are introducing new technologies to improve production and expand flexibility, which denotes the integration of intelligent, autonomous, and interconnected agents. The conceptual transition to dynamic multiple agents generates some dilemmas, mainly regarding the occurrence of unexpected situations. This paper aims to discuss the collective behavior of multi-agent systems in smart factories for achieving fault resilience. The proposed approach is based on three hierarchical plans: imposition, negotiation, and consensus. Fault restoration is achieved through the collective behavior that manages the ternary decisions made in these plans. The approach can help the smart factories that employ autonomous multi-agents improve their production, reliability, and robustness to failure. The proposed method was evaluated using a virtual warehouse logistics but employing real scenarios. Experiments were performed through logistic tasks to prove the collective behavior implemented in the approach for fault resilience. Quantitative analysis of the experiments shows the efficiency of the approach under various situations.

Autonomous Population Regulation Using a Multi-Agent System in a Prey–Predator Model That Integrates Cellular Automata and the African Buffalo Optimization Metaheuristic

This research focused on the resolution of a dynamic prey–predator spatial model. This model has six life cycles and simulates a theoretical population of prey and predators. Cellular automata represent a set of prey and predators. The cellular automata move in a discrete space in a 2d lattice that has the shape of a torus. African buffaloes represent the predators, and the grasslands represent the prey in the African savanna. Each buffalo moves in the discrete space using the proper motion equation of the African buffalo optimization metaheuristic. Two types of approaches were made with five experiments each. The first approach was the development of a dynamic prey–predator spatial model using the movement of the African buffalo optimization metaheuristic. The second approach added the characteristic of regulating the population of buffaloes using autonomous multi-agents that interact with the model dynamic prey–predator spatial model. According to the obtained results, it was possible to adjust and maintain a balance of prey and predators during a determined period using multi-agents, therefore preventing predators from destroying an entire population of prey in the coexistence space.

Morphological approach for autonomous and adaptive system: The construction of three-dimensional artificial model based on self-reconfigurable modular agents

Abstract This paper presents a decentralized approach, inspired by biological cells, for the automatic construction of user-defined three-dimensional structures. Using high-level specification as an input, the proposed system enables the guaranteed construction of user-specified structures. By investigating the evolutionary aspects of morphogenesis, which is regulated by the interplay of the cell processes such as differential cell adhesion, gene-regulation, and inter-cellular signaling, an approach was developed that allows for the construction of an arbitrary structure via swarms of identical, independent, and autonomous multi-agents.

Distributed nonlinear control of mobile autonomous multi-agents

This paper studies the distributed nonlinear control of mobile autonomous agents with variable and directed topology. A new distributed nonlinear design scheme is presented for multi-agent systems modeled by double-integrators. With the new design, the outputs of the controlled agents asymptotically converge to each other, as long as a mild connectivity condition is satisfied. Moreover, the velocity (derivative of the output) of each agent can be restricted to be within any specified neighborhood of the origin, which is of practical interest for systems under such physical constraint. The new design is still valid if one of the agents is a leader and the control objective is to achieve leader-following. As an illustration of the generality and effectiveness of the presented methodology, the formation control of a group of unicycle mobile robots with nonholonomic constraints is revisited. Instead of assuming the point-robot model, the unicycle model is transformed into two double-integrators by dynamic feedback linearization, and the proposed distributed nonlinear design method is used to overcome the singularity problem caused by the nonholonomic constraint by properly restricting the velocities. Simulation results are included to illustrate the theoretical results.

An Accelerated Resource Utilization in Autonomous Stochastic Mobile Multi-Agents

This paper describes how to coordinate autonomous mobile multi-agents, whose agents stochastically move over a finite resource consisting of cells in accordance with transition probabilities. We assume there are interactions or coordination among agents such that (1) each agent cannot move to a destination cell occupied by agents more than the agents of a current cell and (2) their agents have time-lag. Then it is ideal that every cell is always occupied by agents, because of the efficient use of resources, and it is desirable to be the fewest expected number of cells not occupied by agents. The first result shows that the resource utilization of accelerated mobile multi-agents with appropriate average moving speed becomes higher than the resource utilization of mobile multi-agents without average moving speed. Then the resource utilization can be accelerated to maximum by giving appropriate moving speed. The second result shows that there are the best moving speed of system-specific optimum to utilize the resources of autonomous stochastic mobile multi-agents.

A KNOWLEDGE BASE FOR DYNAMIC PATH PLANNING OF MULTI-AGENTS

A fuzzy rule base is proposed to navigate multi-agents from initial positions to target positions in unknown environments. The proposed fuzzy rule base determines the highest priority of nine possible heading directions. The fuzzy rule base has been developed employing genetic algorithms as an approach to dynamic path planning of autonomous multi-agents in unknown environments. Paths which satisfy some optimization criteria with respect to moving distance, smoothness, and clearance of obstacles was obtained from the fuzzy rule base. The fuzzy rule base was obtained from off-line navigation with precise sensor modeling and applied to various simulated on-line navigation. The performance of the fuzzy rule base in different unknown environments is acceptable and shown in simulation results.