# Dual-Arm Peg-in-Hole Assembly Using DNN with Double Force/Torque Sensor
## 2 Related Work  
For solving the peg-in-hole with a dual arm robot, Ref. [17] describes the problem of insertion as a search problem of the hole, where force/torque (F/T) sensor data was used to identify the different assembly stages using thresholds. The challenge with thresholds is the continuous tuning due to changes in the assembly conditions. In this research we compare the threshold method with machine learning methods for contact states classification.  
In [13] is proposed a pool of assembly primitives and maintaining an unilateral contact constraint, where the need of more advanced force control is needed to prevent the abrupt changes in contact forces. Our approach considers quasi-static motion and discrete events, which excludes complex models of the force feedback.  
In [7] there is a definition of a master-slave strategy and a cooperative strategy to evaluate the assembly of the peg-in-hole with one and two arms. The advantage of such work is the use of a compliant robot with low accuracy, which can manage contact forces with less risk of damage. It also defines thresholds as trigger functions in the assembly sequence. In [8] a dual arm robot is also used using a leader-follower strategy and a sensor-less and active compliance admittance method with real time trajectory generation.  
Double F/T sensors in robotics have been reported in [24; 25] where two robots are used for a bi-manual peg-in-hole assembly task. Discrete event systems (DES) are used to model the assembly process. The F/T data is processed separately for each robot and compared with defined thresholds of forces and torques; qualitative values are defined due to the high variability of force/torque signals in every execution. This eliminates the need of tuning constantly.  
Another dual arm system with double sensor is presented in [9], where the main focus is the manipulation of objects in a coordinated way using the F/T signals as the feedback for the impedance controller. There, the authors designed their own software platform. Our approach was using ROS, a developing platform in order to transfer knowledge to different platforms.  
The single arm peg-in-hole assembly has been broadly studied in recent years. In [22] a safe learning mechanism is used to avoid damage to the system. This idea was taken for our work to prepare the robot for uncertain and unexpected conditions. In [26] the authors express their concern about their classical programming methods and how position control-based methods are not suitable for high precision assembly. In contrast, in this work we consider that the classical position control-based method can be improved by integration of machine learning.  
In [27] a combination of different methods such as Principal Component Analysis (PCA), Hidden Markov Models (HMM) and time series for contact state recognition is used. The authors' approach of analysing the contact states recognition as a classification problem is suitable for our project. Expected Maximization and Gaussian Mixture Models (EM-GMM) for monitoring contact states during the peg-in-hole assembly is presented in [28], where the authors present a clear notation of the contact state classification problem that is adopted in this work.  
Different searching techniques are proposed such as spiral search [29], attractive region in environment (ARIE) [30], random search in a plane [7]. Those approaches are not suitable in our project due to the high stiffness of our robot, which can be unsafe during constrained motion.