Robot Driver Research Articles

Intelligent proportional-integral-derivative control techniques for accelerator leg of robot driver

Intelligent proportional-integral-derivative control techniques for accelerator leg of robot driver

A multi-objective optimization design method of shift manipulator for robot driver using SA-PSA

A multi-objective optimization design method of shift manipulator for robot driver using SA-PSA

A nonlinear dynamic characteristic modeling method of shift manipulator for robot driver with multiple clearance joints

A nonlinear dynamic characteristic modeling method of shift manipulator for robot driver with multiple clearance joints

Deep Reinforcement Learning Based Dynamic Proportional-Integral (PI) Gain Auto-Tuning Method for a Robot Driver System

To meet the growing trend of stringent fuel economy regulations, automakers around the world are designing modules such as engines, motors, transmissions and batteries to be as efficient as possible. In order to verify the effect of these designs on the overall fuel efficiency of the vehicle, the vehicle equipped with each module is placed on the chassis dynamometer, driven to follow the target vehicle speed, and actual fuel efficiency is measured. These tests are traditionally performed by human operators, but are now being replaced by robots (physical or software) to ensure the accuracy and reliability of test results. Although the conventionally proposed proportional integral (PI)-based controller has a simple structure and is easy to implement, it requires the process of finding the optimal gain whenever the test conditions such as vehicle or drive cycle change, which is difficult and time consuming. In this study, we propose a proportional integral controller gain adjustment algorithm using deep reinforcement learning. The reinforcement learning agent learns to dynamically modify the PI gain value of the acceleration/deceleration pedal to better follow the target vehicle in a simulation environment. The perturbation is used in each training episode to reduce the difference between the simulation and real testing environment. Upon completion of the training process, the trained agent performs an adjustment process that generates a reference gain table. We then use this reference gain table to perform a real test. The performance of the proposed system was evaluated using Hyundai Tucson HEV (NX4) on an AVL chassis dynamometer. We also compared the performance of our proposed algorithm to traditional fuzzy logic-based PI controllers. The obtained experimental results show that the proposed control system achieved a performance improvement of aounrd 46.8% compared to the conventional PI control system in terms of root mean square error.

Development of a Simple Robotic Driver System (SimRoDS) to Test Fuel Economy of Hybrid Electric and Plug-In Hybrid Electric Vehicles Using Fuzzy-PI Control

Over the past decade, new models of hybrid electric vehicles have been released worldwide, and the fuel efficiency of said vehicles has increased by more than 5%. To further improve fuel efficiency, vehicle manufacturers have made efforts to design modules (e.g., engines, motors, transmissions, and batteries) with the highest efficiency possible. To do so, the fuel economy test process, which is conducted primarily using a chassis dynamometer, must produce reliable and accurate results. To accurately analyze the fuel efficiency improvement rate of each module, it is necessary to reduce the test deviation. When the test conducted by human drivers, the test deviation is somewhat large. When the test is conducted by a physical robot driver, the test deviation is improved; however, these robots are expensive and time-consuming to install and take up considerable amount of space in the driver’s seat. To compensate for these shortcomings, we propose a simple, structured robot system that manipulates electrical signals without using mechanical link structures. The controller of this robot driver uses the widely used PI controller. Although PI controllers are simple and perform well, since the dynamics of each test vehicle is different (e.g., acceleration response), the PI controller has a disadvantage in that it cannot determine the optimal PI gain value for each vehicles. In this work, the fuzzy control theorem is applied to overcome this disadvantage. By using fuzzy control to deduce the optimal value of the PI gain, we confirmed that our proposed system is available to conduct tests on vehicles with different dynamics.

Automatic Floor Cleaning Robot Using Arduino and Ultrasonic Sensor

The entire floor cleaning robot is divided into several parts, namely consisting of an Ultrasonic Sensor, Motor Shield L298, Arduino Uno microcontroller, Servo, and Dc Motor. This tool works when the Arduino Uno microcontroller processes the ultrasonic sensor as a distance detector and a DC motor as a robot driver, then the DC motor is driven by the Motor Shield L298. When an ultrasonic sensor detects a barrier in front of it, the robot will automatically look for a direction that is not a barrier to the floor cleaning robot. The distance value on the sensor has been determined, that is, when the distance read by the ultrasonic sensor is below 15 cm. The results of testing the value of the ultrasonic sensor distance found different conditions that occur. In a distance of> 15 cm, the condition of the prototype cleaning robot for the road floor cleaning is obtained, while the distance <15 cm, the condition for the prototype of the street floor cleaning robot has stopped.

Implementation of rover tank firefighting robot for closed areas based on arduino microcontroller

<span>Recently, communication via the wireless systems become promising, since the wireless communication system provides several benefits such as the disposal of the wire usage, provides long-distances communication, and the disposal of periodic maintenance of data transmission lines. In robotic science, the wireless system unit is the main part of the mobile robot. Whereas, only wireless communication module responsible for transmitting commands from the transmitter (e.g. mobile or PC) to the receiver (i.e. robot brain) and vice versa. With the increase in population growth and the construction of the urban, fires have become widely spread. It is worth mentioning here that the lives of firefighters are in permanent danger where they may be exposed to the combustion or inhalation of toxic gasses associated with the combustion process. Therefore, become necessary to find effective ways to reduce these risks. In this paper, a rover tank robot is implemented for fire extinguishing in closed areas based on the Arduino microcontroller, flame sensor, and motion sensor. The robot has a small water pump installed on a servo motor to pump water, the first flame sensor placed on the right of the robot's front and the second to the left of the robot's front. When a fire is explored through a flame sensor, the water pump sprays water to the location of the fire. The robot is driven wirelessly utilising the Bluetooth HC-06 module and the Wi-Fi camera utilised for driving purposes. Besides, a passive infrared (PIR) sensor is used to detect the presence of any living organism inside the fire site to alert the robot driver that an organism requires a rescue.</span>

Parameter design of biped robot motion system based on multi-objective optimization

In recent years humanoid robots have been widely used in toy, performance, education and other service industries, but most biped robots walk slowly and have poor stability. The reason is that the driver parameters of the robot cannot properly match the walking gait algorithm, and the insufficient performance of the robot driver leads to the poor motion capability of the robot. In this paper, the optimization design process of biped robot parameters is studied and expounded, and its motion capability is improved by optimizing the driving parameters of the robot. Firstly, the contradiction between walking speed, stability and driver performance of biped robot is analysed. The performance evaluation functions of the three are further established, and the optimal parameter design to a certain extent is realized based on the multi-objective optimization method. Finally, combining with the physical simulation engine, the design parameters are simulated and checked, and the robot design process is completed through the guidance of simulation results.

Driving with Robots: Mind perception and propensity for aggressive driving

Mind perception, or the tendency to ascribe agency (i.e., the ability to plan and act) and experience (i.e., the ability to sense and feel) to others, is an important design consideration for human-robot inter-action since an agent’s mind status affects how we interact with it and how we interpret its behavior. The current study examines whether observable behaviors of robot-piloted autonomous vehicles are interpreted differently, lead to different emotional reactions and trigger different behaviors of the ob-server as a function of the robot driver’s perceived mind status. We expect that aggressive behavior of robot drivers perceived to be high in agency would be interpreted as more intentional, and as such would lead to stronger negative reactions and retaliatory behaviors. Consistent with our expectations, the robot driver high in agency was perceived as more intentional and elicited more irritation in partici-pants.

Nonlinear dynamics analysis of shift manipulator for robot driver considering multiple revolute clearance joints and variable load

Clearance joints and variable load have a great influence on the dynamic performance of shift manipulator for robot driver. In this article, a dynamics model of clearance joint and a simulation method of variable load are proposed to analyze the influence of clearance joint and variable load on the dynamic performance of shift manipulator. Firstly, a hybrid nonlinear contact force model is established to describe the normal contact force of revolute clearance joint, and a modified Coulomb friction model is used to describe the tangential contact force of revolute clearance joint. On this basis, a virtual prototype dynamics model of shift manipulator with multiple revolute clearance joints is established. Then a power transmission model of shift manipulator including the variable load model at the end of shift manipulator is established. The time-varying and nonlinear characteristics of the load in gear engaging direction are simulated by neural network. Finally, the influences of different clearance dimension and variable load on the dynamic performance of shift manipulator are analyzed. Experiment and simulation results show the validity of the presented analysis, which provides a basis for further structure optimization of shift manipulator.

Research on compliant configuration of hydraulic manipulator with passive following characteristic

To decrease the injury caused by an accidental collision between the hydraulic manipulator and human, a hydraulic rotating angle self-servo compliant robot driver joint is designed. Based on this joint, a variable stiffness passive servo-hydraulic rotary joint is improved. The passive follow-up characteristics of the improved servo-hydraulic rotary joint enable it to enter the low stiffness mode by itself when it encounters collisions beyond the force limit. Combining this characteristic, the research method of the hydraulic mechanical arm flexible configuration is proposed. Firstly, the joint motion mode is defined by the Euler angle, and a variety of configurations conforming to the motion characteristics of the manipulator are determined. Then, the virtual prototype and collision environment are built, and the dynamic characteristics of the end effector of the arm under high and low stiffness are measured. The results show that the flexible configurations with passive follow-up characteristics can significantly suppress the collision force. Finally, the visual method is employed to solve, draw, and compare the workspace of the selected compliant configuration, to obtain the optimal compliant configuration with both flexibility and motion, which provides theoretical guidance for the subsequent design of the compliant manipulator.

RDE Plus - A Road to Rig Development Methodology for Complete RDE Compliance: Road to Chassis Perspective

<div class="section abstract"><div class="htmlview paragraph">To aid Original Equipment Manufacturers (OEM) in meeting future Real Driving Emissions (RDE) regulation criteria [<span class="xref">1</span>] across extended environmental conditions, a Road to Rig (R2R) whole vehicle development, calibration and verification approach, currently known as RDE Plus (RDE+), has been developed at HORIBA.</div><div class="htmlview paragraph">In this paper, the methodologies required for replicating real RDE road tests on a chassis dynamometer are discussed in depth. These include application of robot driver for successful cycle replication and repeatability and use of HORIBA’s Multi-function Efficient Dynamic Altitude Simulation (MEDAS) system in conjunction with a temperature and humidity controlled test cell to replicate the engine, vehicle and environmental conditions.</div><div class="htmlview paragraph">For RDE replication using a robot driver, two methods are presented. The first, is a speed matching method whereby a robot driver maintains the recorded vehicle speed via its own closed-loop control whilst road load is implemented via a Road Load Reconstruction (RLR) technique. The second is a “Torque Matching” (TM) method whereby robot driver pedal inputs are controlled in an open-loop mode and vehicle speed is implemented using Automatic Speed Regulation (ASR) of the chassis dynamometer. The former focuses on how robot driver controller tuning is used to successfully replicate real world driving if accurate road load components are known. The latter is presented as an elegant way to achieve a 1:1 road load match of a previously driven RDE test, or any other arbitrary road drive, with minimal prior information.</div><div class="htmlview paragraph">Considerations relating to vehicle preparation, robot driver tuning, and the application of HORIBA MEDAS will also be covered, including potential minor modifications to vehicles in pursuit of successful replication outcomes.</div><div class="htmlview paragraph">The RLR method showed good agreement in work done and CO<sub>2</sub> emissions for the real-world drives presented (approx. 2-3% difference). However, the TM method was able to demonstrate much less than 1% difference in these parameters when matching a previously driven test at the same conditions. This improved precision would be beneficial for minimizing the amount of back to back testing required to characterize the effect of powertrain calibration changes. NOx and PN emissions are also shown correlating well with the real road test values (< 10% difference).</div><div class="htmlview paragraph">This paper is the third in a series of ongoing technical reports from HORIBA on vehicle and engine development and calibration methodologies for RDE compliance. It is twinned with a concurrent SAE paper by the same authors covering Engine-in-Loop (EiL) methodologies and follows SAE Technical Paper 2019-01-0756 presented at WCX2019 [<span class="xref">2</span>, <span class="xref">3</span>]. Additionally, there are patent applications arising from the techniques demonstrated [<span class="xref">4</span>].</div></div>

Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior

Automated vehicles can change the society by improved safety, mobility, and fuel efficiency. However, due to the higher cost and change in business model, over the coming decades, the highly automated vehicles likely will continue to interact with many human-driven vehicles. In the past, the control/design of the highly automated (robotic) vehicles mainly considers safety and efficiency but failed to address the “driving culture” of surrounding human-driven vehicles. Thus, the robotic vehicles may demonstrate behaviors very different from other vehicles. We study this “driving etiquette” problem in this paper. As the first step, we report the key behavior parameters of human driven vehicles derived from a large naturalistic driving database. The results can be used to guide future algorithm design of highly automated vehicles or to develop realistic human-driven vehicle behavior model in simulations.

Design of High-speed Cable Driven Robot with Unlimited Rotation of Hand

Design of High-speed Cable Driven Robot with Unlimited Rotation of Hand

Automating Shift-Scheduling Calibration by Using Bionic Optimization and Personalized Driver Models

Shift-scheduling calibration is important to the automobile industry, but it is repetitive and time-consuming; it is thus desirable to have a robot driver to automate this process. In this paper, we propose automating the calibration of shift-scheduling by using bionic optimization, i.e., particle swarm optimization (PSO), to guide the searching process and to integrate the driving styles into the calibration by equipping the robot drivers with personalized driver models. The personalized driver model is established by imitating the human driving behavior and is employed as a robot driver to conduct the driving cycle test, i.e., FTP-72 or US06, for candidate shifting schedules. The shifting performance is evaluated online via the computed performance index and/or AVL-Driver, regarding both driveability and fuel economy. Guided by PSO, candidate schedules are generated, tried, and evaluated until an optimal or near-optimal solution is obtained through iterations. Numerical experiments are presented to verify the feasibility and effectiveness of the proposed scheme. The shifting performance is improved by about 2% in computed performance index when compared with the base map. It is also suggested that personalized calibration is preferred if economically feasible.

Adaptive speed control method for electromagnetic direct drive vehicle robot driver based on fuzzy logic

To solve the shortcomings of existing control methods for an electromagnetic direct drive vehicle robot driver, including large speed tracking error and large mileage deviation, a new adaptive speed control method for the electromagnetic direct drive vehicle robot driver based on fuzzy logic is proposed in this paper. The electromagnetic direct drive vehicle robot driver adapts an electromagnetic linear motor as its drive mechanism. The control system structure is designed. The coordinated controller for multiple manipulators is presented. Moreover, an adaptive speed controller for the electromagnetic direct drive vehicle robot driver is proposed to achieve the accurate tracking of desired speed. Experiments are conducted using a Ford FOCUS car. Performances of the proposed method, proportional–integral–derivative, and fuzzy neural network are compared and analyzed. Experimental results demonstrate that the proposed control method can accurately track the target speed, and it can inhabit the change of speed caused by interference under different test conditions, and it has small mileage deviation, which can meet the requirements of national vehicle test standards.

A Fuzzy Sliding Mode Controller for Planar 4-Cable Direct Driven Robot

Cable Direct Driven Robots (CDDRs) are a special class of parallel robots but they are formed by replacing all the supporting rigid links with cables. Compare with traditional robots, these robots are good candidates for performing a wide range of potential applications. A Planar CDDR model is considered in this paper since no rotational move and no moment resistance are required on the end-effector, all 4 cables convene in a single point and the end-effector is modeled as a point mass. The main goal of this paper is to present a new approach in control by developing a Sliding Mode Controller (SMC) with a Fuzzy-PI as sliding surface using Fuzzy logic toolbox in Matlab/Simulink. The tests performed were Step change reference test and Tracking trajectory test to observe the behavior of the cables during the trajectory and the end-effector movement. Simulation was carried out on Planar 4-Cable CDDR to prove the effectiveness of the proposed control law and the results were compared with a PI Controller and a conventional SMC in terms of integral square error (ISE) index. Only the kinematic model of Planar 4-Cable CDDR is considered in this paper.

PID Control for a Manipulator Robot Using Internet Networking and Matlab-based

This paper presents the design to the implementation of the proportional, integral and derivative (PID) remote control system of robot manipulator using Matlab-based Internet network. These studies seek the solution of distance operations, time constraints, and monitoring the operation response of robot manipulator motion as robot that possible to carry dangerous objects which is should be monitored remotely during the operation. PID control system that is controlled remotely by the client and server systems. The monitoring process of robot manipulator is Matlab-based. Testing is done by doing observation to the speed response of the DC motor as the robot’s axis driver on open loop system condition, and then doing observation using PID control system. The test of axis robot driver has also been carried out with a variety of no-load and load conditions. The test results showed the motor driver of robot will work optimally when using PID control system. The data acquisition and the stability of data communication between the client and server are affected by the condition of the existing data transfer on the network and also the distance from the server to the plant.

A Novel Method for Predicting Vehicle State in Internet of Vehicles

In the fields of advanced driver assistance systems (ADAS) and Internet of Vehicles (IoV), predicting the vehicle state is essential, including the ego vehicle’s position, velocity, and acceleration. In ADAS, an early position prediction helps to avoid traffic accidents. In IoV, the vehicle state prediction is essential for the required calculation of the expected reliable communication time between two vehicles. Many approaches have emerged to perform this vehicle state prediction. However, such approaches consider limited information of the ego vehicle and its surroundings, and they may not be very effective in practice because the real situation is highly complex and complicated. Moreover, some of the approaches often lead to a delayed prediction time due to collecting and calculating the substantial history information. By assuming that the driver is a robot driver, which eliminates distinct driving behaviors of different persons when facing the same situation, this paper creates a decision tree as a new quick and reliable method adapted to all road segments, and it proposes a new method to perform the vehicle state prediction based on this decision tree.

Neural network–based speed control method and experimental verification for electromagnetic direct drive vehicle robot driver

The article presents an application of neural network to an electromagnetic direct drive vehicle robot driver. To achieve the accuracy and adaptability of speed control for driving vehicle with manual transmission and automatic transmission in various test conditions, a new neural network–based speed control method of the direct drive vehicle robot driver is presented. The mechanical actuators of the direct drive vehicle robot driver are directly driven through electromagnetic linear actuator. The coordinated control system structure of the direct drive vehicle robot driver is described. The mechanical actuators include the driving mechanical legs and the shift manipulator of the direct drive vehicle robot driver. The driving mechanical legs are consisted of the throttle mechanical leg, the brake mechanical leg, and the clutch mechanical leg. The displacement of the mechanical actuators represent the displacement of the driving mechanical legs, and the displacement of the shift manipulator. The number of the network hidden node is five, and a tangent transfer function is used as the neurons transfer function. The actual vehicle speed is as the variable of the network output layer, and a linear transfer function is as the neurons transfer function. The neural network training algorithm adopts the Levenberg–Marquardt training algorithm. The vehicle tests manipulated by the robot driver in different driving conditions are conducted. The proposed control method of the robot driver is verified and compared with the performances of other control method and human driver.