Abstract
This paper reviews the technical aspects of robotic charging for Electric Vehicles (EVs), aiming to identify research trends, methods, and challenges. It implemented the Systematic Literature Review (SLR), starting with the formulation of research question; searching and collecting articles from databases, including Web of Science, Scopus, Dimensions, and Lens; selecting articles; and data extraction. We reviewed the articles published from 2012 to 2022 and found that the number of publications increased exponentially. The top five keywords were electric vehicle, robotic, automatic charging, pose estimation, and computer vision. We continued an in-depth review from the points of view of autonomous docking, charging socket detection-pose estimation, plug insertion, and robot manipulator. No article used a camera, Lidar, or Laser as the sensor that reported successful autonomous docking without position error. Furthermore, we identified two problems when using computer vision for the socket pose estimation and the plug insertion: low robustness against different socket shapes and light conditions; inability to monitor excessive plugging force. Using infrared to locate the socket yielded more robustness. However, it requires modification of the socket on the vehicle. A few articles used a camera and force/torque sensors to control the plug insertion based on different control approaches: model-based control and data-driven machine learning. The challenges were to increase the success rate and shorten the time. Most researchers used commercial 6-DOF robot manipulators, whereas a few designed lower-DOF robot manipulators. Another research challenge was developing a 4-DOF robot manipulator with compliance that ensures a 100% success rate of plug insertion.
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The authors would like to thank the anonymous reviewers for their helpful comments.
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This work was conducted as a part of Universiti Teknologi Malaysia (UTM) and Badan Riset dan Inovasi Nasional, Indonesia (BRIN) collaborative research-based postgraduate program under grant number R.J130000.7351.4B734.
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Appendix A: Summary of the primary articles
Appendix A: Summary of the primary articles
No | CODE | Articles | Value proposition | Performance: Success rate/accuracy/time | Sensor | ConnectorAlignment | Platform type/processor/microcontroller/additional/other tools | Port type/other object | Method/algorithm; DOF |
|---|---|---|---|---|---|---|---|---|---|
Primary articles belong\ing to the autonomous docking (AD) cluster | |||||||||
1 | AD | Weng et al. (2016) | Category 1: Auto-docking of small-size mobile robots that use cameras and predefined marks. They have the platforms of four wheels drive (4WD) or two wheels drive (2WD) | CI method improves target estimation performance | RGB-D camera,Laser ranger | N/A | 4WD mecanum wheel, 2D SICK laser ranger, Bluetooth Low Energy (BLE) | N/A | Kinematic model, covariance union (CU), covariance intersection (CI), SIFT pattern |
2 | AD | Cortes and Kim (2018) | Effective power transfer of receiver position: ± 5 mm | Camera, rotary encoder | N/A | Differential-Drive Mobile Robot, Arduino mega, Raspberry Pi B | N/A | Llinear quadratic regulator (LQR), computer vision (OpenCV ver 3) | |
3 | AD | Du et al. (2021) | Circle Hough Transform (CHT) accuracy is 50%, while Circle detection accuracy is 20% higher than CHT | Camera, IMU (BNO05 Absolute orientation) | N/A | Tracked robot, NVIDIA Jetson Nano (OpenCV), Arduino Uno, Adafuit motorshield | Conductive metal pads (circle pattern) | Pre-Contour-Gradient (Color), color-based contour, Def-circle, PID control loop, Line detection: PHT, LSD, FPE | |
4 | AD | Romanov and Tararin (2021) | Accuracy: 5 cm, Min. distance: 58 cm | Camera, LiDAR | N/A | Wheeled mobile robots, ArUco markers | N/A | Differential kinematics, ROS implementations (Gazebo Simulator) | |
5 | AD | Vongbunyong et al. (2021) | Category 2: Auto-docking of small-size mobile robots that use non-cameras as the sensor, including light detecting and ranging (LIDAR), laser range finder, and infrared (IR) transmitter and receiver | < ± 20 mm position error & < 0.05 rad orientation error | LiDAR | Mechanical | AMR “CARVER”, RP- LiDAR A1, Sick LiDAR Tim-781 s, intel-NUC (ROS), BLDC motor | Connector (2P) | Geometrical marker (position & localisation) |
6 | AD | Rocha et al. (2020) | N/A | Odometry, laser-range finder, | N/A | Hospital car model, LRF SICK S300, Arduino mega, ROS | N/A | TEA, Perfect Match, Beacon-based Localisation, Kalman filter, | |
7 | AD | Acosta Calderon et al. (2014) | The connector moves freely at 30° to reduce misalignment | Infrared, odometry | N/A | Mobile robot | N/A | Mechanism methods; 1-DOF | |
8 | AD | Chang et al. (2018) | The infrared signal range is widened from 60° to 90° | Camera, infrared, encoder, ultrasonic | N/A | IRobot Create2, Arduino Uno, Rasberry pi 3, MPU-6050 | N/A | Neural network linear regression, Opcode (Open Interface control instruction) | |
9 | AD | Rao and Shivakumar (2021) | 95% success rate; stop charging > 12 V | IR Transceiver, Voltage | N/A | Arduino UNO, Bluetooth HC 05, ALCD Display, L293 Drivers | N/A | Locomotion Control and Docking Method | |
10 | AD | Petrov et al. (2012) | Category 3: Auto-docking of AEV with front wheels steering systems that use laser scanners and IR cameras | The precision is 10 cm in the longitudinal and lateral direction around the docking point | Infrared camera | N/A | Car | N/A | Kinematics-based control (lateral-longitudinal & path tracking), POSIT algorithm |
11 | AD | Klemm et al. (2016) | The precision is 2 cm in the longitudinal position | Laser scanners, Odometry | N/A | Car | Type-2 | SLAM, RRT-based approach, TOSDF, OSD, EKF | |
12 | AD | Fang et al. (2020) | Category 4: Battery charging/swapping of mobile robots with 2WD or with XYZ cartesian robot | N/A | Hall sensor | N/A | 5-wheel tracking car with manipulator, CPU | N/A | Unified deployment of charging socket, tracking positioning, trolley bus sliding |
13 | AD, RM | Behl et al. (2019) | N/A | Camera, LiDAR | N/A | TurtleBot 3 WafflePi, Raspberry Pi 3, ROS | Connector (3P) | Hough Transform, Template Matching, SLAM, PID, 4-DOF | |
14 | AD, RM | N/A | Sonar, Photoelectric | N/A | Mobile robot, PLC, CPU, AC motor, DMP position | N/A | 4-DOF | ||
15 | AD | Narvaez et al. (2021) | Category 5: Not relevant to ground vehicle autonomous docking | NDR | NDR | NDR | NDR | NDR | NDR |
Primary articles belonging to the port detection (PD) cluster | |||||||||
1 | PD | Sun et al. (2018) | Category 1: Using the camera to recognise the charging socket of EV | 99% accuracy of the socket recognition experiment | 3D Camera | N/A | Dataset, CPU Intel i7 7800, GPU nvidia, Camera | Type-2 | Convolutional Neural Network (CNN) |
2 | PD | Wang (2021) | 83.62% accuracy of the socket recognition experiment | RGBD Camera | N/A | N/A | All type | Very Deep CNN | |
3 | PD | Bang et al. (2022) | From experimental results, the EnT-GAN gave an Average Precision (AP) of 66.5, which is better than the state-of-the-art 63.6 | Intel RealSense Depth Camera D435i | N/A | Connector EV dataset (Chevrolet Bolt & Kia Niro) | Type-1 Combo (Chevrolet Bolt, Kia Niro) | DetectoRS: Detecting objects with recursive feature pyramid and switchable atrous convolution; Generative Adversarial Network (GAN); EnT-GAN; ForkGAN | |
4 | PD | Cernohorsky et al. (2019) | N/A | RGBD Camera with IR lens | N/A | UR3 robot, Intel Realsense D435i camera | Type-2 CCS (CCS2) | The Image Processing Toolbox in MATLAB; 6-DOF | |
5 | PD | Zhang and Jin (2016) | Category 2: Using the camera to detect and locate the socket position in the x–y plane | 100% accuracy of the charging port localisation | CCD Camera | N/A | Halcon CV; CCD Camera, TOSHIBA PORTEGE M909 computer | Type-2 | HSI (hue, saturation, intensity) color model; Mathematical morphology, Canny operator, Tukey weight function |
6 | PD, RM | Long et al. (2019) | Category 3: Using a camera and IR-US range finder to detect and locate the socket position in the x–y-z | Only conceptual design; No experiment result | 2D monocular Camera, IR-US range finder, UV light source | N/A | Ring ultraviolet light | GB/T | The minimum distance iteration algorithm; The split wide-angle range based on IR-US range finder technology; 6-DOF |
7 | PD, RM | Category 4: Using the camera to detect and locate the socket position in the x–y-z & orientation Rx-Ry-Rz | 94.80% success rate of the socket pose estimation experiment | 3D Camera, Light source | N/A | GBT 20234.3–2011 DC, AUBO-i5 6-DOF, MER-125-30GM/C-P Mercury Gig PoE, M0814-MP2 | GB/T (DC) | Hough circle and Hough line detection; Canny operator; Quadratic Curve Standardization (QCS); Perspective-n-point (PNP) algorithm; Direct Linear Transform (DLT); 6-DOF | |
8 | PD, RM | Miseikis et al. (2017) | Category 5: Using only cameras as the sensor to detect and locate the charging socket and insert the charging plug into the charging socket | 8/10 success rate of the plug-in experiment | Stereo Camera | N/A | Halcon CV | Type-1, Type-2 | Shaped-based template matching (SBTM) method; Perspective transformation based on the least squares (LS) fit method; 6-DOF |
9 | PD, RM | Walzel et al. (2019) | 42/42 success rate of the insertion experiment | Stereo Camera | N/A | Halcon CV | Type-2 CCS (CCS2) | SBTM method; Perspective transformation based on the LS fit method; 6-DOF | |
10 | PD, RM | Walzel et al. (2021b) | Not available | Stereo Camera | N/A | Halcon CV | Type-2 CCS (CCS2) | SBTM method; Perspective transformation based on the LS fit method; 6-DOF | |
11 | PD, RM | Hirz et al. (2021) | Not available | Stereo Camera | N/A | Halcon CV | Type-2 CCS (CCS2) | SBTM; Perspective transformation based on the least squares fit method; 6-DOF | |
12 | PD, RM | Walzel et al. (2021a) | 42/42 success rate of the plug-in experiments using the same vehicle; 75% to 95% success rates of socket position detection with five different EVs | Stereo Camera | N/A | Halcon CV | Type-2 CCS (CCS2) | SBTM method; Perspective transformation based on the LS fit method; 6-DOF | |
13 | PD, RM | Pan et al. (2020) | 9/10 success rate of insertion experiment at the best illumination (1618–6151 lx) | Monocular Camera | N/A | MER-125-30UM camera, M0814-MP2 camera lens, LED | GB/T (AC) | CNN; HSI color model, Otshu method; Canny operator; Hough transform ellipse detection; geometric method; 6-DOF | |
14 | PD, RM | Insertion experiment success rates: indoor 99%, outdoor 93% | Camera | N/A | GBT 20234.3–2011 DC, MER-125-30GM, Camera lens M0814-MP2 | GB/T (DC) | Canny operator; cluster template matching algorithm (CTMA); EPnP algorithm; Direct Linear Transform (DLT); 6-DOF | ||
15 | PD, RM | Zhou et al. (2021) | 100% success rate of the socket detection experiment; 92% success rate of the plug-in experiment | 3D Camera with ToF technique | N/A | MiR 200 mobile platform, PMD 3D camera, Robotiq 85-F two finger gripper | Plug charger | The PointVoxel-Recursive Convolutional Neural Network (PV-RCNN); KITTI Vision Benchmark Suite; 6-DOF | |
16 | PD, RM | Lv et al. (2019) | Category 6: Using the camera and F/T sensor to detect and locate the charging socket and insert the charging plug into the charging socket | 92% success rate of socket pose estimation experiments; | 2D Camera, F/T sensor | N/A | UR5 robot arm, type-2 charging plug, monocular camera | Template picture | Speeded Up Robust Features (SURF) algorithm, Perspective-n-point (PnP) algorithm, SVM; 6-DOF |
17 | PD, RM | Guo et al. (2021) | Insertion accuracy 97%; Insertion time 9 s; Initial position error is limited to 5 mm with no orientation error | RGB-D Camera, ft300 F/T sensor | N/A | UR5 robot arm, robotiq ft300 F/T Sensor, Intel D435i RGB-D camera | Type-2 | Visual servo control; Hue saturation value (HSV); Reinforcement Learning (RL) position control; Proportional Integral (PI) force control; PyBullet simulation; 6-DOF | |
18 | PD, RM | Liu et al. (2021) | 94% success rate of insertion experiments;Insertion time since the first contact: 8 s; Time from the initial position to the first contact: 15 s; Insertion initial position error is 1 cm with orientation error. Accuracy of classifier is 92.59% | 3D Camera, F/T sensor | N/A | AUBO robot, AT-S1000-06C camera, Axia80-M8 F/T sensor | GB/T | Point-cloud template matching algorithm; Space Vector Machine (SVM); 6-DOF | |
19 | PD, RM | Bucher et al. (2021) | Category 7: Using an endoscope camera with an IR-pass filter to detect and locate the socket and insert the charging plug | 97% success rate of insertion experiment | Endoscope camera with IR-pass filter; 4 IR-LEDs as active markers | Elastic compensator | Endoscope camera: CMOS 1/6-inch camera sensor, ROS (Ver. Melodic Morenia on Ubuntu 18.04), IR-LED pattern | Type-2 CCS (CCS2) | Four infrared (IR)-LEDs marker and an endoscope camera with an IR-pass filter; A 4-DOF ACD consists of a 3-DOF articulator, a translational platform, and an elastic compensation unit |
20 | PD, RM | Bdiwi et al. (2015) | Category 8: Using camera and F/T sensor but focus on force or impidance control for charging plug insertion into the socket | Success rate: NA; Insertion time: 80 s; Initial position error is limited to 1 cm without orientation error | Camera; F/T sensor | N/A | KUKA KR6/2 robot, FT Delta SI-660–60 force sensor | IEC 60309 splug-socket | Integral force control with the force error as the input and position correction as the output; Parallel position/force control using a spiral motion; 6-DOF |
21 | PD, RM | Jokesch et al. (2016) | 100% success rate of insertion experiments; Insertion time: 13 s; Initial position in front of the socket 30 mm with a maximum error of 7.35 mm; Insertion initial orientation error is 5.1° | 3D Camera; F/T sensor | Torque sensor in every joint | KUKA LWR iiwa 7 R800 | Type-2 | Impedance control; Compliant blind-search strategy; Phase shifted Lissajous path; 7-DOF | |
22 | PD, RM | Zhang et al. (2022) | 99% success rate of insertion experiments; Insertion time: 6–8 s. Insertion initial position in front of the socket: 10 mm | Camera, F/T sensor | Impedance control | UR5e robot, L515 camera | Type-2 | D-H coordinate system; Impedance control; Active Remote Centre Compliance (ARCC)-based insertion strategy; 6-DOF | |
23 | PD | Park et al. (2021) | Category 9: Using a camera with the help of a squared unique marker or QR code. Do not explicitly address charging socket localisation and plug insertion | N/A | Stereo Camera | N/A | NA | N/A | Geometry |
24 | PD, RM | Harik (2021) | N/A | RGB-Camera | NA | Superdroid mecanum robot, UR5e robot, two fingered Robotiq 2F-85 gripper, NVDIA Jetson TX2 (ROS), Logitech C920 web-camera | Connector (2P) | The high-level computer runs Ubuntu with ROS as the middleware. The main node subsribes and publishes from/to different nodes: usb-cam ROS package, aruco-ros ROS package, and rosserial_python ROS package; 6-DOF | |
25 | PD | Gungor and Kiyak (2021) | N/A | Camera | N/A | N/A | QR code | Calculate the difference between the center of the QR code and the center of the camera in the x–y plane; calculate the QR code’s width to control the plug-in z direction | |
26 | PD, RM | Shen et al. (2012) | Category 10: Battery swapping for EVs | NDR | NDR | NDR | NDR | NDR | NDR |
27 | PD | Wu et al. (2012) | NDR | NDR | NDR | NDR | NDR | NDR | |
28 | PD | Jiang et al. (2019) | NDR | NDR | NDR | NDR | NDR | NDR | |
29 | PD | Pingpittayakul and Mitsantisuk (2022) | NDR | NDR | NDR | NDR | NDR | NDR | |
30 | PD, RM | Peng et al. (2019) | Category 11: Aviation chargingpeng (Peng et al. 2019); Obstacle avoidance of a small mobile robot (Sarker et al. 2020); A design concept of ACS (Luo and Shen 2020); Robot arm for electronic components disassembly (Farhan et al. 2021); Concept of robotic plugging (Cernohorsky et al. 2022); Underground inspection robot (Xi et al. 2022) | NDR | NDR | NDR | NDR | NDR | NDR |
31 | PD | Sarker et al. (2020) | NDR | NDR | NDR | NDR | NDR | NDR | |
32 | PD, RM | Luo and Shen (2020) | NDR | NDR | NDR | NDR | NDR | NDR | |
33 | PD, RM | Farhan et al. (2021) | NDR | NDR | NDR | NDR | NDR | NDR | |
34 | PD, RM | Cernohorsky et al. (2022) | NDR | NDR | NDR | NDR | NDR | NDR | |
35 | PD, RM | Xi et al. (2022) | NDR | NDR | NDR | NDR | NDR | NDR | |
Primary articles belonging to the robot manipulator (RM) cluster | |||||||||
1 | RM | Yuan et al. (2020) | Category 1: A design of a hybrid serial-parallel robot with 6-DOF movements to provide a larger payload and cable arrangement convenience | N/A | N/A | N/A | N/A | N/A | The charging robot has nine actuating DOFs and achieves six DOFs movements –kinematic and static models; optimization of the manipulator configuration |
2 | RM | Lou and Di (2020) | Category 2: A 4-DOF cable-driven automatic charging robot (CDACR) with a 3-DOF cable-driven manipulator and a moving platform. The robot can control the position and pitch angle of the end-effector (Lou & Di 2020); A collision localisation and classification method based on machine learning for the CDACR (Lin et al. 2022) | Insertion success rate: NA; From experiments by varying displacement along the x-axis from -78 to 123 mm and a yaw angle from -5 to 5 degrees, the forces could satisfy the requirement while all cable tension kept positive | Camera, ATI F/T sensor, Encoders | Flexible plug with elastic element | Cable-driven auto-charging robot (CDACR) | CHAdeMO | A flexible plug at the end-effector to accommodate small rotational elastic deformation due to angular errors of the end-effector relative to the charging socket; Machine vision; PI Controller; 4-DOF |
3 | RM | Lin et al. (2022) | From simulated experiments, they claimed the method could simultaneously provide collision localisation and classificattion | Camera, ATI F/T sensor, Encoders, IMU | Elastic compensator | CPU: Intel Core i7-10700 K @ 3.80 GHz, GPU: NVIDIA GeForce RTX 3080 | GB/T (DC) | Machine learning, CLC, DCNN–SVM, CNN and LSTM; PI Controller; 4-DOF | |
4 | RM | Chablat et al. (2022) | Category 3: A 3-DOF robot for charging EVs at home with the socket on its front side | Insertion success rate: NA. The insertion experiment can be seen at: https://youtu.be/P5wCgRqSyDQ | Camera, QR code | N/A | Planar parallel robot, Rasberry Pi 4, Arduino board, 42BYG Geared Stepper Motor, OpenCV | DC connector | The robot comprised parallel links that move the reference point in the x–y plane and a linear actuator that moves the charging plug in a line constrained in the y–z plane; 3-DOF |
5 | RM | Okunevich et al. (2021a) | Category 4: A parallel link structure is installed on a 2WD mobile robot for charging small mobile robots. It uses CCN to perceive tactile that predicts misalignment | The system could predict the angle, vertical, and horizontal values of end effector misalignment with an accuracy of 95.46%, 98.2%, and 86.9%, respectively | Tactile sensor | Tactile perception system | Inverted Delta Mechanism, Three Dynamixel MX64, Intel NUC comp. + OpenCM 9.04 Dynamixel Contr | Delta mechanism | CCN-based tactile perception; The electrode’s misalignment in 3 directions can be compensated by controlling the angle of each link using the corresponding motor; Machine Learning, deep learning; 3-DOF |
6 | RM | Okunevich et al. (2021b) | Electrode localisation success rate: 83 out of 10; execution time: 60 s | Tactile, RGB-D camera, LiDAR | Tactile sensor | Mobile robot, Intel NUC7i5BNK, OpenCM 9.04, RPLiDAR A3, RealSense D435 camera | Delta mechanism | CNN, OpenCM 9.04 Dynamixel; 3-DOF; The contact force is controlled by the motor current | |
7 | RM | GS and PS (2022) | Category 5: A concept of a rigid-flexible manipulator for conductive charging | Insertion success rate: NA; Only provided joint angle tracking and the end effector 2D trajectory; | Camera | Rigid-flexible arms | Rigid- flexible manipulator, | Type-2 | Euler–Bernoulli beam theory; Adaptive Fault-tolerant control (AFTC); PD controller; 4-DOF |
8 | RM | Hu et al. (2020) | Category 6: A passive, compliant mechanism for the automatic charging robot end-effector | Insertion success rate: NA; It could insert the plug reliably with a maximum insertion force of less than 100 N | Binocular camera, F/T sensor | N/A | UR5 robot arm, F/T sensor | N/A | The passive complaint mechanism was developed based on Stewart parallel mechanism |
9 | RM | Barzegaran et al. (2017) | Category 7: Adaptive robot for inductive charging of EV | N/A | Camera, ultrasonic | N/A | N/A | N/A | Inverse kinematics, mathematical adaptive: extremum seeking; 3-DOF |
10 | RM | Category 8: Robot mechanisms for EV battery swapping (Sun et al. 2014; J. Wang et al. 2012a, b; Wang and Wang 2014); Battery swapping for electric buses (Lin et al. 2012; Wang et al. 2015); A robot for small mobile robot battery swapping (Dandan et al. 2012); Robots for small UAV battery-swapping systems (Barrett et al. 2018; Dong et al. 2018) | NDR | NDR | NDR | NDR | NDR | NDR | |
11 | RM | Sun et al. (2014) | NDR | NDR | NDR | NDR | NDR | NDR | |
12 | RM | Wang and Wang (2014) | NDR | NDR | NDR | NDR | NDR | NDR | |
13 | RM | Lin et al. (2012) | NDR | NDR | NDR | NDR | NDR | NDR | |
14 | RM | Wang et al. (2015) | NDR | NDR | NDR | NDR | NDR | NDR | |
15 | RM | Dandan et al. (2012) | NDR | NDR | NDR | NDR | NDR | NDR | |
16 | RM | Dong et al. (2018) | NDR | NDR | NDR | NDR | NDR | NDR | |
17 | RM | Barrett et al. (2018) | NDR | NDR | NDR | NDR | NDR | NDR | |
Primary articles belonging to the robot manipulator (RM) cluster Category 9: | |||||||||
18 | RM | Jiang et al. (2017) | A manipulator for the maintenance of HV transmission lines | NDR | NDR | NDR | NDR | NDR | NDR |
19 | RM | Akbaripour and Masehian (2016) | Semi-lazy probabilistic roadmap (SLPRM) for motion planning of industrial manipulators | NDR | NDR | NDR | NDR | NDR | NDR |
20 | RM | Zhou et al. (2022) | A 3D point-cloud technology is adopted to measure the shapes and depth of targeted objects in SME production | NDR | NDR | NDR | NDR | NDR | NDR |
21 | RM | Di Lillo et al. (2018) | An underwater vehicle manipulator system | NDR | NDR | NDR | NDR | NDR | NDR |
22 | RM | Fleischer et al. (2021) | A flexible disassembly system for drive train components of EVs | NDR | NDR | NDR | NDR | NDR | NDR |
23 | RM | Afaq et al. (2015) | A customizable system for implementing the control algorithm of 5 DOF manipulator | NDR | NDR | NDR | NDR | NDR | NDR |
24 | RM | Khonji et al. (2017) | A robotic rover with a 2D lidar to localize a drone and a robotic arm with an inductive charging pad | NDR | NDR | NDR | NDR | NDR | NDR |
25 | RM | Zhang and Huang (2020) | A control strategy for multiple unmanned ground vehicle-manipulator systems | NDR | NDR | NDR | NDR | NDR | NDR |
26 | RM | Sujati et al. (2021) | A charging system is installed on a mobile manipulator | NDR | NDR | NDR | NDR | NDR | NDR |
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Saputra, H.M., Nor, N.S.M., Rijanto, E. et al. A review of robotic charging for electric vehicles. Int J Intell Robot Appl 8, 193–229 (2024). https://doi.org/10.1007/s41315-023-00306-x
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DOI: https://doi.org/10.1007/s41315-023-00306-x