Marcelo Becker

Autonomous miniature flying robots: coming soon! - Research, Development, and Results

The exponential growth of the interest and investigations in UAVs is strongly pushing the emergence of autonomous MFRs. This article presented some developments in the ASL-MFR project. A new design methodology was introduced and applied to a quadrotor and a coaxial helicopter enhancing appreciably the robots characteristics by allowing 100% thrust margin and 30 min autonomy (respectively, 40% and 20 min for CoaX). An original concept of hybrid active and passive control is introduced for CoaX. A simulation software permitting rapid MFR reconfiguration and various testing conditions was shown. Finally, a simulation of an obstacle avoidance controller was presented. The numerous developments presented in this article reinforce our conviction in the emergence of autonomous MFRs.

Robotic forklifts for intelligent warehouses: Routing, path planning, and auto-localization

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 key-routines 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.

Battery state estimation for applications in intelligent warehouses

When it comes to AGVs (Automated Guide Vehicles) working in intelligent warehouse systems it is necessary to take into account that the use of batteries may impact the performance of the overall system. They need to be recharged or changed, and the time required to execute these operations might interfere in the AGV availability. Therefore, it is necessary to carry out a battery management procedure to ensure that the batteries have sufficient charges to perform the desired tasks. This paper describes a method based on the Extended Kalman Filter (EKF) to estimate the Batteries State of Charge (SOC). The estimated consumption is compared with the SOC obtained by the EKF. A series of experiments using mini-robotic forklifts were performed to evaluate the method. The experimental results have shown its effectiveness using resistive loads. This methodology allowed estimating the battery consumption for a certain route of the mini-robotic forklift in the warehouse and verifying the load capacity available for the mini-robotic forklift to accomplish a task assigned by the warehouse routing system.

A New Control Architecture for Robust Controllers in Rear Electric Traction Passenger HEVs

It is well known that control systems are the core of electronic differential systems (EDSs) in electric vehicles (EVs)/hybrid HEVs (HEVs). However, conventional closed-loop control architectures do not completely match the needed ability to reject noises/disturbances, especially regarding the input acceleration signal incoming from the drivers commands, which makes the EDS (in this case) ineffective. Due to this, in this paper, a novel EDS control architecture is proposed to offer a new approach for the traction system that can be used with a great variety of controllers (e.g., classic, artificial intelligence (AI)-based, and modern/robust theory). In addition to this, a modified proportional-integral derivative (PID) controller, an AI-based neuro-fuzzy controller, and a robust optimal H∞ controller were designed and evaluated to observe and evaluate the versatility of the novel architecture. Kinematic and dynamic models of the vehicle are briefly introduced. Then, simulated and experimental results were presented and discussed. A Hybrid Electric Vehicle in Low Scale (HELVIS)-Sim simulation environment was employed to the preliminary analysis of the proposed EDS architecture. Later, the EDS itself was embedded in a dSpace 1103 high-performance interface board so that real-time control of the rear wheels of the HELVIS platform was successfully achieved.

Traversability analysis using terrain mapping and online-trained Terrain type classifier

Path estimation is a big challenge for autonomous vehicle navigation, especially in unknown, dynamic environments, when road characteristics change often. 3D terrain information (e.g. stereo cameras) can provide useful hints about the traversability cost of certain regions. However, when the terrain tends to be flat and uniform, it is difficult to identify a better path using 3D map solely. In this scenario the use of a priori knowledge on the expected roads visual characteristics can support detection, but it has the drawback of being not robust to environmental changes. This paper presents a path detection method that mixes together 3D mapping and visual classification, trying to learn, in real time, the actual road characteristics. An on-line learning of visual characteristics is implemented to feedback a terrain classifier, so that the road characteristics are updated as the vehicle moves. The feedback data are taken from a 3D traversability cost map, which provides some hints on traversable and non-traversable regions. After several re-training cycles the algorithm converges on a better separation of the path and non-path regions. The fusion of both 3D traversability cost and visual characteristics of the terrain yields a better estimation when compared with either of these methods solely.

FVMS: A novel SiL approach on the evaluation of controllers for autonomous MAV

The originality of this work is to propose a novel SiL (Software-in-the-Loop) platform using Microsoft Flight Simulator (MSFS) to assist control design regarding the stabilization problem found in ©AscTec Pelican platform. Aerial Robots Team (USP/EESC/LabRoM/ART) has developed a custom C++/C# software named FVMS (Flight Variables Management System) that interfaces the communication between the virtual Pelican and the control algorithms allowing the control designer to perform fast full closed loop real time algorithms. Emulation of embedded sensors as well as the possibility to integrate OpenCV Optical Flow algorithms to a virtual downward camera makes the SiL even more reliable. More than a strictly numeric analysis, the proposed SiL platform offers an unique experience, simultaneously offering both dynamic and graphical responses. Performance of SiL algorithms is presented and discussed.

Robust control in 4×4 hybrid-converted touring vehicles during urban speed steering maneuvers

The present work is focused on the synthesis and the analysis of robust control techniques for rear electric traction control in 4×4 hybrid-converted CVs (Conventional Vehicles) at urban speed limits (lower than 60 Km/h). This set represents a practicable alternative for the automotive industry, improving vehicular performance and reducing considerably fossil fuel air pollution. Our goal is to design an electromechanical controlled system that can replace the conventional rear wheels in touring cars with a pair of electric wheels with a minimal level of adaptation, preserving the original combustion engine. We consider the synthesis of an H∞ robust controller and also the neurofuzzy approach. An optimized PID controller was also designed for the final analysis and evaluation. Based on Ackerman Geometry and the reading of the steering front angles, it was possible to estimate the maneuver radius from turning center. Thus, all three proposed control approaches must adjust the rear wheels individual angular speeds by means of the current control of the two electrical motors linked to them, so that the car presents an appropriate behavior during all possible maneuvers. Finally, computation models were run in order to compare the three controllers.

A Robotic System for Rehabilitation of Distal Radius Fracture Using Games

This work integrates robotics and electronic games with the objective of producing more motivating and attractive therapeutic activities in distal radius fracture rehabilitation (wrist region). The proposed robotic system allows the reliable measurement of all wrist angular motion amplitudes. To this end, a framework is proposed to allow the full integration of the designed game to the developed hardware. The framework stores data from the game and from the robot movements for further analysis. The prototype was tested in healthy subjects, and a questionnaire was used to produce qualitative impressions on the system.

2D laser-based probabilistic motion tracking in urban-like environments

All over the world traffic injuries and fatality rates are increasing every year. The combination of negligent and imprudent drivers, adverse road and weather conditions produces tragic results with dramatic loss of life. In this scenario, the use of mobile robotics technology onboard vehicles could reduce casualties. Obstacle motion tracking is an essential ability for car-like mobile robots. However, this task is not trivial in urban environments where a great quantity and variety of obstacles may induce the vehicle to take erroneous decisions. Unfortunately, obstacles close to its sensors frequently cause blind zones behind them where other obstacles could be hidden. In this situation, the robot may lose vital information about these obstructed obstacles that can provoke collisions. In order to overcome this problem, an obstacle motion tracking module based only on 2D laser scan data was developed. Its main parts consist of obstacle detection, obstacle classification, and obstacle tracking algorithms. A motion detection module using scan matching was developed aiming to improve the data quality for navigation purposes; a probabilistic grid representation of the environment was also implemented. The research was initially conducted using a MatLab simulator that reproduces a simple 2D urban-like environment. Then the algorithms were validated using data samplings in real urban environments. On average, the results proved the usefulness of considering obstacle paths and velocities while navigating at reasonable computational costs. This, undoubtedly, will allow future controllers to obtain a better performance in highly dynamic environments.

Determining forbidden steering directions for a passenger car in urban environments based on the velocity obstacle approach and use of trackers

One of the most challenging tasks for autonomous passenger cars in urban-like environments is to select the ideal steering angle to avoid collisions with pedestrians and other vehicles. In order to determine the set of steering angles that must be avoided it is necessary to track mobile obstacles that surround the vehicle. This task is especially difficult in urban environments where a great variety of obstacles may induce the vehicle embedded controllers to take erroneous decisions. They need as much information as possible concerning the obstacle positions and speeds (direction and magnitude) in order to plan evasive maneuvers that avoid collisions. Unfortunately, obstacles close to vehicle embedded sensors frequently cause blind zones behind them where other obstacles could be hidden. In order to overcome this problem we developed an obstacle tracking module based only on 2D laser scan data. Its main parts consist of obstacle detection, obstacle classification, and obstacle tracking. In addition to this, we implemented a modified version of the velocity obstacle approach to determine the set of forbidden steering angles for a certain time window. The real data samplings were acquired in an urban-like environment in our University Campus using our test vehicle. The tests proved the applicability of the algorithms in urban-like environments.