Search

Automated guided vehicles play a crucial role in transportation and industrial environments. This paper presents a proposed Bio Particle Swarm Optimization (BPSO) algorithm for global path planning. The BPSO algorithm modifies the equation to update the particles' velocity using the randomly generated angles, which enhances the algorithm's searchability and avoids premature convergence. It is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Transit Search (TS) algorithms by benchmark functions. It has great performance in unimodal optimization problems, and it gains the best fitness value with fewer iterations and average runtime than other algorithms. The Q-learning method is implemented for local path planning to avoid moving obstacles and combines with the proposed BPSO for the safe operations of automated guided vehicles. The presented BPSO-RL algorithm combines the advantages of the swarm intelligence algorithm and the Q-learning method, which can generate the globally optimal path with fast computational speed and support in dealing with dynamic scenarios. It is validated through computational experiments with moving obstacles and compared with the PSO algorithm for AGV path planning. [ABSTRACT FROM AUTHOR]

This paper considers a class of distributed online aggregative optimization problems over an undirected and connected network. It takes into account an unknown dynamic environment and some aggregation functions, which is different from the problem formulation of the existing approach, making the aggregative optimization problem more challenging. A distributed online optimization algorithm is designed for the considered problem via the mirror descent algorithm and the distributed average tracking method. In particular, the dynamic environment and the gradient are estimated by the averaged tracking methods, and then an online optimization algorithm is designed via a dynamic mirror descent method. It is shown that the dynamic regret is bounded in the order of O (T) . Finally, the effectiveness of the designed algorithm is verified by some simulations of cooperative control of a multi-robot system. [ABSTRACT FROM AUTHOR]

In this article, the distributed nonlinear placement problem for a class of multicluster Euler–Lagrange systems is considered. The problem is first converted into a time-varying noncooperative game. A distributed Nash equilibrium seeking algorithm composed of an auxiliary double-integrator system and a coordinated-tracking observer is designed to solve the problem. The convergence results are established by an iterative approach and the small gain theorem. The effectiveness of the algorithm is demonstrated via simulations. [ABSTRACT FROM AUTHOR]

The distributed economic dispatch problem for power generation in electricity grids is studies. A simple centralised algorithm is designed to obtain the solution of the dispatch problem without the external disturbances. Inspired by the centralised algorithm, a distributed algorithm is presented using the distributed average tracking algorithm. As far as we know, the proposed algorithm is the first one that is capable of time‐varying loads and external disturbances while the tracking errors are asymptotic convergent. Simulations on the IEEE 14‐BUS system are provided to validate the theoretical results. [ABSTRACT FROM AUTHOR]