Collision Localization and Classification on the End-Effector of a Cable-Driven Manipulator Applied to EV Auto-Charging Based on DCNN–SVM

Abstract

With the increasing popularity of electric vehicles, cable-driven serial manipulators have been applied in auto-charging processes for electric vehicles. To ensure the safety of the physical vehicle–robot interaction in this scenario, this paper presents a model-independent collision localization and classification method for cable-driven serial manipulators. First, based on the dynamic characteristics of the manipulator, data sets of terminal collision are constructed. In contrast to utilizing signals based on torque sensors, our data sets comprise the vibration signals of a specific compensator. Then, the collected data sets are applied to construct and train our collision localization and classification model, which consists of a double-layer CNN and an SVM. Compared to previous works, the proposed method can extract features without manual intervention and can deal with collision when the contact surface is irregular. Furthermore, the proposed method is able to generate the location and classification of the collision at the same time. The simulated experiment results show the validity of the proposed collision localization and classification method, with promising prediction accuracy.

Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix

“…Meanwhile, the kernel size remains consistent with ref. [3] in the other direction. Our experiments utilized a Windows-based system with Detailed hyperparameters for the feature extractor and classifier are provided in Table 2.…”

“…In our previous work, we explored collision localization models based on a CNN [3] and an ESN [4], respectively. Drawing inspiration from these two approaches, we proposed a collision localization model that integrates a CNN and an ESN as feature extractors.…”

“…Subsequently, a nonlinear ReLU activation function is used to process the features, enhancing the model's capacity for effective non-linear information processing. Notably, the Conv2D structure employed in this study differs from that of [3]. While the previous work involved symmetric 3 × 3 convolutional kernels, in this study, we adopt asymmetric kernels to maximally preserve temporally reasonable features extracted by the CNN for subsequent processing by the ESN layer.…”

“…The hyperparameters of the ESN and SVM are taken from [4], while the hyperparameters of the CNN remain consistent with those outlined in ref. [3], with the exception of the convolutional kernel aspect. Since the padding method is not utilized during the initial convolutional computation, it should ensure that the length of the input can be evenly divisible by the convolutional length along the temporal dimension.…”

“…In this case, implementing a collision classification protection system on the robotic arm ensures the safety of the vehicle and the robot. When the positioning deviation falls between the aforementioned scenarios, meaning that the charger can make contact with the charging port but cannot smoothly insert due to the presence of a visual positioning deviation, imparting a collision localization capability to the robotic arm can effectively correct the deviation caused by the visual positioning failure, serving as a supplementary localization strategy in the event of a visual failure [3,4].…”