Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

In this paper, an AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. The modified RRT integrating multiple heuristics can search a safer, optimal and faster converge global path within a short time, and the improved TEB can optimize both path smoothness and path length. The method is composed of a global path planning procedure and a local path planning procedure, and the Receding Horizon Planning (RHP) strategy is adopted to fuse these two modules. Firstly, the MH-RRT is utilized to generate a state tree structure as prior knowledge, as well as the global path. Then, a receding horizon window is established to select the local goal point. On this basis, an improved two-step TEB is designed to optimize the local path if the current global path is feasible. Various simulations both on static and dynamic environments are conducted to clarify the performance of the proposed MH-RRT and the improved two-step TEB. Furthermore, real applicative experiments verified the effectiveness of the proposed approach.

Analysis and Optimization of the Robotic Mobile Fulfillment Systems Considering Congestion

With the development of the social economy and the improvement of the consumption concept, a new business model combining offline and online has been promoted. The warehousing system is one of the important links of commodity production and circulation, which involves storage, sorting, and distribution. It has a significant impact on the operation cost and the efficiency of the whole logistics system. The progress of robot technology, the Internet of things, and artificial intelligence technology promotes the automation and intelligence of storage systems. The Robotic Mobile Fulfillment Systems (RMFS), which takes the automatic guided vehicles (AGVs) as the way of handling and picking, greatly improves the space utilization, operation efficiency, and flexibility of the system. This paper studies the RMFS with fixed shelves and establishes the performance evaluation model of the picking system considering the AGVs congestion by establishing the queuing network. The effectiveness of the model is verified by simulation, and the optimization of system parameter configuration is further discussed according to the experimental data.

Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises

This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.

A Novel Scheduling Algorithm for Common Rail Dual Automatic Guided Vehicles Particle Filtering Algorithm for Industrial Process Control

This paper explores deep into the collaborative scheduling of common rail dual automatic guided vehicles (AGVs). Firstly, a dual AGV scheduling model was constructed to minimize the overall time of material distribution. Then, a novel scheduling algorithm was developed to dynamically plan the orders based on time windows. To effectively minimize the distribution time, heuristic algorithms were adopted to initialize the distribution order of materials. On this basis, the collaboration between the two AGVs was innovatively designed based on dynamic planning and time windows, making up for the defects of traditional methods in AGV cooperation. This greatly shortens the running time of the entire system in material distribution. The computing results fully demonstrate the efficiency and rationality of our algorithm. Finally, our algorithm was proved to be superior to the benchmark method through experiments on actual industrial instances.

Mechatronic Modelling of Industrial AGVs: A Complex System Architecture

Automatic guided vehicles (AGVs) are unmanned transport vehicles widely used in the industry to substitute manned industrial trucks and conveyors. They are now considered to play a key role in the development of the Industry 4.0 due to their temporal and spatial flexibility. However, in order to deal with the AGV as a potential mobile robot with high capacities and certain level of intelligence, it is necessary to develop control-oriented models of these complex and nonlinear systems. In this paper, the modelling of this vehicle as a whole is addressed. It can be considered composed of several interrelated subsystems: control, safety, driving, guiding and localization, power storage, and charging systems. The kinematics equations of a tricycle vehicle are obtained, and a controller is proposed. An extended hybrid automata formalism is used to define the behaviour of the safety and the control systems, as well as their interaction. In addition, the electrical equivalent circuit of the batteries, charger, and the motors is studied. The architecture of the holistic model is presented. Simulation results of the AGV in a workspace scenario validate the model and prove the efficiency of this approach.

Energy Shaping Control for Wireless Power Transfer System in Automatic Guided Vehicles

This paper proposes an energy shaping controller of a DC/DC converter for automatic guided vehicles (AGVs) wireless power transfer (WPT). A transformer is inserted after the LCC topology to improve the transfer power, and the DC/DC boost converter is added before this topology to obtain desired systematic power dynamically. The system power transfer model is derived based on the idea of voltage transformation and the desired power can be implemented indirectly through regulating desired output voltage of DC/DC converter. With the proposed controller, this WPT system will have a much better dynamic performance and the effective working time can be increased significantly. Furthermore, this paper proposes dynamical regulation strategy for output power to get real time target power according to the charging curve of the battery. Simulation and experimental results verified the control performance of the proposed control scheme. A WPT prototype with power up to 1.65 kW was built, and 92.12% efficiency from DC power source to battery load is achieved, which is 4% higher than that obtained by the conventional PID method.