Today, robotic systems are bridging the gaps between global economy, social needs, and logistics focusing on sustainable development solutions. Everyday new robotic applications can be found in literature and media. Some of them are basically entertainment toys. Nevertheless, the great majority of them is used inside industries, performing several tasks (painting, welding, moving materials, etc.). In a scenario of global economy growth, any sustainable solution that can reduce the product final cost is welcome. This article presents researches on robotic forklifts for intelligent warehouses developed at the Mechatronics Lab at USP-EESC in Brazil. We show three keyroutines that determine the Automated Guided Vehicle (AGV) behavior: the routing algorithm (that computes the overall task execution time and the minimum global path of each AGV using a topological map of the warehouse), the local path planning algorithm (based on A* it searches for the local minimum path between two nodes of the warehouse topological map), and an auto-localization algorithm (that applies an Extended Kalman Filter -EKF -to estimate the AGVs actual positions). In order to validate the algorithms developed, several tests were carried out using the simulation software Player/Stage. The results obtained were encouraging and the router developed was able to solve traffic jams and collisions, before sending the final paths to the robots. In a near future all algorithms will be implemented using mini-robotic forklifts and a scaled environment built in our lab.
In the context of robotic forklift, battery management is essential and may be considered a key issue in the logistic system management for intelligent warehouses, where goods must be delivered on time according to the monitoring battery State of Charge (SOC) applied to routing system is a tendency to be considered in the planning of warehouses. Based on this scenario, this paper describes a method based on the use of Extended Kalman Filter (EKF), which uses the cell combined model to estimate the battery SOC. Tests were performed to evaluate the estimated battery consumption considering Open Voltage Circuit (OCV) and SOC EKF method applied in a mini robotic forklift. It was possible to verify the battery consumption needed to execute a determined task path and assign a route for the robotic forklift considering the actual SOC.
Advanced land‐vehicle navigation commonly uses integrated systems to counteract global navigation satellite system (GNSS) solution degradation. This occurs mainly in urban environments due to blockage of the satellite signals. This paper presents a loosely coupled inertial navigation system/GNSS navigation system that combines an attitude and heading reference system (AHRS) device with a dual‐frequency dual‐antenna GNSS heading receiver. The integrated navigation system is aided by a low‐cost odometer which replaces external wheel speed sensors usually installed in autonomous vehicles. The proposed odometer extracts the anti‐lock braking system‐generated pulses of rear wheels from vehicle controller area network messages. Following, it converts them into software‐generated signal pulses which are sent to the AHRS device through the serial port. The system platform uses a mobile internet data link to get differential GNSS corrections in real‐time from a public Ntrip—networked transport of Radio Technical Commission for Maritime Services via internet protocol—broadcaster in order to allow the GNSS receiver to operate in differential global positioning system/real‐time kinematic (RTK) modes. Thus, the integrated navigation system provides centimeter‐level positioning accuracy at 100 Hz. Since the positioning accuracy is severely affected by numerous factors, this work proposes a replicated 24 full factorial design with the purpose of evaluating the in‐field obtained positioning performance under different factors combinations. The experimental design chosen allows to know under which conditions it is feasible to replace the available GNSS velocity by the proposed odometry solution, when they are used as navigation aids, and knowing that the proposed odometry has a low resolution. The analysis of 32‐runs factorial design results, using a significance level of .05, demonstrated that the proposed odometry can overcome GNSS/RTK velocity.
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