Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS

ROS+Gazebo

ROS and Gazebo are the main tools of this experiment.

Gazebo is a 3D dynamic simulator that can accurately and efficiently simulate robots in complex indoor and outdoor environments. Gazebo uses a distributed architecture with independent libraries for physical simulation, user interfaces, communication and sensor generation.

Controllers

Advanced controllers

The two advanced controllers selected for this article are AIC and MRAC.

• AIC: the control scheme is based on Karl Friston’s free energy principle (FEP) (Friston, Mattout & Kilner, 2011). The active inference framework uses FEP to explain perceptions and actions, and the subject has a world model that is optimized using sensory input by predicting and interpreting its feelings. The main idea of active inference is that the brain can manipulate sensory input and brain state to minimize prediction errors (16). In this way, by changing the sensory input of behavior or modifying its beliefs about the state of the world, behavior and perception can be used to minimize free energy. AIC can be used as an adaptive control scheme for robotic arms, which is easily scalable to high DOF, and it maintains high performance even in the presence of large unmodeled dynamics.

• MRAC: model reference adaptive control is a direct adaptive strategy with some adjustable controller parameters and adjustment mechanisms for adjusting them (Jain & Nigam, 2013). Different from commonly used PID controllers, adaptive controllers are good at dealing with uncertain factors in unstructured environments. Usually, the adaptive controller consists of two loops for state feedback and parameter adjustment. The similarity between AIC and MRAC is that they both belong to the adaptive controller type.

Experimental scene

This experiment simulates that the robotic arm will grab an object from one bookshelf and place it on another bookshelf. It mainly simulates actions instead of actually grabbing objects. The purpose of this is to observe as much as possible the performance of the controller without interference from other external objects. The last forward lean motion is to test whether the joints are in good working condition when the robot arm moves forward. In addition, the purpose of the robot arm moving to the left and right is to obtain the state of different joints when they move in the opposite direction, such as whether the position data is completely opposite, etc. The diversity of data provides strong evidence for controller performance analysis. During the experiment, all actions are repeated multiple times. This is because during the exercise test, sometimes the robot arm will have abnormal movements that are not easy to attract attention. To make the experimental data more convincing, every time during the test, motion will be executed multiple times, and then the observation data will be used for subsequent data analysis. These tasks are to obtain the position data and force data output by the robot arm during operation. The position data is used to analyze the accuracy and control efficiency of the controller to control the robot arm, for example, by analyzing time from one position to another. It can be seen that the controller controls the operating efficiency of the robotic arm. Effort data is mainly used to analyze the jitter of the robotic arm. It is noted that the consideration behind is that the essence of the robot arm is to change the position of an object, including its own joint position, therefore, such a simple test environment was designed, and this test result can be used in some similar environments, however, the environment changes greatly, for instance, in some very cluttered scenarios, more tests are needed to illustrate the results.

Experiment on robustness

The robustness test of the controller is by applying an instantaneous external force to a specific joint of the robot arm, because the robot arm will have a position shift or jitter after receiving external force. Then, by collecting joint position data, the time required for the robotic arm to return to a normal state or whether the controller can still control the robotic arm to work normally after receiving external force can be obtained. The joints that accept the external force are selected based on the changes in the position of the joints in space. The more obvious the changes in the position of the joints in space, the easier it is to see the changes in the data after being subjected to the external force. On the contrary, if you choose joint 1 or 2 which has no spatial position change, it is not easy to see the running status of the robot arm after force in the simulation. Finally, in the robustness test, there is another point that needs to be explained. The direction of the robot arm’s instantaneous force is opposite to the direction of the robot arm’s movement. Figures 1 and 2 are all the tasks of this experiment. The three action tasks in Fig. 1 are mainly to complete the general test of the basic controller and advanced controller. The purpose of the test in Fig. 2 is to complete the robustness of the controller. From Fig. 2, it can be seen that the robot arm is running in the right direction, but the external disturbance is left. The purpose of this design is to clearly see the robot arm’s response to external disturbance. In addition, if the direction of force is the same as the direction of movement of the robot arm, the adjustment of the robot arm’s controller to external disturbance may not be clearly observed unless a very large force is applied.

PID controller adjustment

PID controller is composed of a proportional unit, integral unit and derivative unit. Adjust the performance of the PID controller by adjusting the three parameters. There are many ways to adjust PID, which can be adjusted manually or dynamically adjusted by machine learning. In this article, manual tuning parameters is used. The adjustment method is to adjust the proportional unit P first, then adjust the integral unit I, and finally adjust the differential unit D if necessary. In general, only adjust the parameters of P and I. In addition, the PID controller is just a simple controller with limited performance, so it cannot solve all control problems. In this article, there are two basic controllers that will be affected by PID parameters: joint Velocity Controller and Joint Position Controller. Taking the Joint Velocity Controller as an example, the comparison chart before and after PID parameter adjustment is shown in Fig. 3. Before the PID controller is adjusted, the robot arm cannot reach the set pose and there will be many poses that are not set. This means that the robot arm cannot be effectively controlled by the controller. In Fig. 3, before the PID controller is adjusted, the rotation angle of joint 2 often exceeds the given range, and its maximum value even exceeds that of joint 1. After adjusting the PID controller, the performance of the robot arm is obviously better. If the PID controller is not adjusted, the performance of the two basic controllers cannot meet the needs of the experiment, because the robot arm cannot reach the set posture during the simulation.

Base controller benchmarking

Figure 4 shows the position data of the three basic controllers. It is clear to see from the figure that the data of the joints with the most obvious data changes are basically the same between different controllers. In the simulation process, these three controllers can also control the robot arm to reach the accurate position, and there is no too abnormal data, such as unable to reach the accurate position. But this result is after proper adjustment of PID parameters. It can also be seen from Fig. 4 that the robot arm takes the same time to complete the task designed in Fig. 1. In addition, the data output by the robot arm is the same whether it is at the inflexion point or at the maximum value. However, there are also joints with the significantly different performance data output, such as joint 2 and joint 4. Joint Position Controller has very obvious data anomalies between 0 and 3 s. Although Joint Velocity Controller also has this performance, it is far less obvious than Joint Position Controller. Furthermore, there are obvious data anomalies between 7–13 and 20–23 s, which caused the joints to shake at the target position during the simulation. However, the Joint Effort Controller does not have such abnormal data. Figure 5 shows the effort data of the three basic controllers.

It can be clearly seen in Fig. 5 that the Joint Effort Controller has no jitter data, and all the data is normal, but the other two controllers have obvious jitter data. During the simulation, the jitter of the robot arm controlled by the other two controllers can be seen from the simulation animation, but because the amplitude is not very large, it does not look obvious. However, the effort data clearly shows the jitter. Although both the Joint Position Controller and the Joint Velocity Controller are jittering, the Joint Velocity Controller does not make some joints shake significantly at certain times. For example, there is no serious jitter problem for joint 2 between 2 and 7 s. The jitter problem of Joint Position Controller basically lasts for the entire task process, and all joints have jitter. Since displaying the status of 7 joints at the same time is too confusing for the data of the Joint Velocity Controller and Joint Position Controller, then joint 4 is selected for specific data display. Since the position data shows that the degree of change of joint 4 is not very large, the jitter value will be relatively small. If a joint with a very large value is selected, it will be difficult to read the jitter data.

The last test is a robustness test. Figure 6 shows the result. The purpose of this test is to observe the time it takes for different controllers to respond to external interference before recovering the interference, and whether the robot arm can return to a normal state. In order to be more intuitive, Table 1 records the response time of the three basic controllers and the time of accept external force. “Accept external force” means the time when the robot arm is subjected to an external force in the experiment. In the experiment, the robot arm is accepted to two times external forces. “Response time” means how long it takes for the controller to restore the robot arm to its previous state when the position of the joint changes. According to the final result, the Joint Position Controller takes the shortest time, and the Joint Effort Controller takes the longest time. Of course, this does not mean that the Joint Position Controller is better than the Joint Effort Controller. The specific reasons will be explained in the data analysis section. In addition, even if the time is different, the difference is only 0.1 s, so the robustness test of the three controllers is equivalent to obtaining relatively good results. First, the three controllers restore the robot arm to the normal state and the time it takes is all short, secondly, these three basic controllers have the ability to restore the robot arm to normal state. The accuracy of the robotic arm can be determined by observing whether each joint of the robotic arm reaches the correct position. By observing the force of each joint, it can be seen whether the robot arm is running stably.

Advanced controller benchmarking

Figure 7 shows the position data of the experimental results of the advanced controller benchmarking of this article. Through this data, the accuracy and control efficiency of the advanced controller AIC and MRAC to control the robot arm is obtained. Figure 7 also shows that it takes less time for the MRAC to control the robot arm to reach the designated position than the AIC to control the robot arm to reach the designated position. The AIC controller takes 30 s but the MRAC only takes 20 s. Furthermore, the turning point of each line in the figure is the position data of the joint when the robot arm reaches the specified position, so whether it is in the simulation animation or the joint output data can prove that the two advanced controllers can control the robot arm to the specified position because the value of the turning point is the same. However, the joint 2 data in Fig. 7 has obvious differences. The fluctuation of joint 2 of the robot arm controlled by MRAC is more obvious than that of AIC. For the convenience of observation, the data of joint 2 is taken out separately. Since the data of joint 4 in Fig. 7 is not obvious, the data of joint 4 is also taken out.

Figure 8 is the Effort data figure of AIC and MRAC. It can be seen from the figure that the jitter of the robotic arm controlled by the MRAC controller is much more serious than the AIC, which is not obvious in the position data. Furthermore, when the AIC controlled robotic arm has data fluctuations, the MRAC controlled robotic arm also has data fluctuations, such as at 6 and 12 s. The difference is that MRAC is sometimes earlier than AIC. This may be because the robot arm controlled by MRAC runs faster. For MRAC, the problem of joint 2 is more serious, and the jitter of joint 2 in the simulation animation is indeed more obvious. Although other joints also jitter, the gap is still relatively large compared with joint 2. Compared with MRAC, there is almost no jitter in the robot arm controlled by AIC, and the data change law of AIC is smoother, and the data change of MRAC is more chaotic.

Figure 9 shows the robustness test results of the advanced controller AIC and MRAC. The direction and position of the robot arm bearing the external force have been shown in Fig. 2. Furthermore, the movement direction of the robot arm is to the right, and the robot arm will receive an external force to the left. Furthermore, it is not difficult to see from Fig. 9 that AIC reacts more smoothly to external interference, while MRAC is more chaotic. This phenomenon should be because the jitter of MRAC is more obvious than that of AIC, which leads to more chaotic MRAC data output. Another phenomenon is worth noting. The time interval between two data fluctuations of the robot arm controlled by AIC is about 5 s, for example, between 1 and 6 s. The time interval of MRAC is 2 s, for example, 5–7 s. This phenomenon should be caused because the robot arm controlled by AIC runs slower than the robot arm controlled by MRAC. In order to better display the experimental results, relevant experimental data are recorded in Table 2.

The final result shows that the robot arm controlled by the AIC controller needs 0.3 s to recover to the previous state after receiving the external force, while the MRAC only needs 0.1 s. In addition, the output data value of the MRAC after the external force is smaller than the value of AIC a lot of. However, this does not mean that the robustness of MRAC is better than that of AIC controller. This experimental result can only prove that the two advanced controllers can adjust the robot arm to the state before the external force is applied. The data size and response time output by the robotic arm may also be affected by other factors, such as jitter. More analysis will be explained in the data analysis section.

Based controller

According to the experimental results of the basic controller, several conclusions can be drawn:

• The basic controller can control the robot arm to the designated position, and the time spent is the same;

• The basic controller has jitter, but the jitter of the Joint Effort Controller is the least obvious, and the other two basic controllers have obvious jitter;

• All three basic controllers can restore the robot arm interfered by an external force to the state before the interference, but the response time of the controller is different.

Based on the previous introduction to the basic controller, it is not difficult to see that the two controllers that perform poorly (with obvious jitter) are affected by the PID parameters, so the adjustment of the PID parameters will directly affect the performance of the basic controller. It is challenging to obtain very good results based on the adjustment of PID parameters because there are seven joints that need to adjust PID parameters in this robot arm, and the joints will affect each other during the adjustment process. For example, when the PID parameters of joint 1 are adjusted to a satisfactory condition, the PID parameters of joint 2 are adjusted, but during the adjustment process, it is found that the data output of joint 1 has changed again. Therefore, in this experiment, adjust the PID parameters as much as possible. The result is that each joint can work, and every PID parameter adjustment is not forced to obtain very good results. So PID parameters affect the performance of the two basic controllers. In addition, the poor performance of the controller may also due to the controller itself. The Joint Velocity Controller and Joint Position Controller accept speed and position values as input data, and then output as Effort commands through PID mapping. First, that also proved the conclusion of the experiment, that is, PID parameters affect the performance of the basic controller, and besides, because the data needs to be mapped and converted, this may also cause problems with the controller, as the data conversion may cause errors. The better-performing Joint Effort Controller does not need to consider these issues. Because the input and output of the Joint Effort Controller are both Efforts data, it does not need to map input and output data, nor is it affected by PID parameters, so this may be a reason of the better performance of the Joint Effort Controller. Although the performance of the two basic controllers is not very good, it is only reflected in the occurrence of jitter. More importantly, the three basic controllers can spend the same time controlling the robot arm to the designated position.

This test also did a robustness test on the controller, the purpose is to observe whether the controller is capable of restoring the controller interfered by external forces to the state before the interference. From the results, the three controllers all meet the requirements. The difference is that the response time and output value of the controller are different. This should be caused by the jitter problem of the robot arm itself. It can be seen from the Effort data output by the robotic arm that there is jitter between the joints. When the robot arm is subjected to external force, the force generated by the jitter and the jitter direction will affect the magnitude of the external force. As a result, the controller’s response and time to external force interference in different results. Although the result data is different, this robustness test proves that the basic controller has the ability to deal with simple external disturbances and restore the robot arm to the state before the disturbance.

Advanced controller

Active inference controller and MRAC are the advanced controllers selected for this controller benchmarking. From the results, both controllers can control the robot arm to reach the preset posture, but they take different time. MRAC is obviously faster to complete the task than AIC. According to the characteristics of the AIC controller, some parameters need to be adjusted adaptively within the AIC. Most of the parameters of MRAC are preset, so this may be one of the reasons that cause the MRAC controller to execute faster than AIC. In addition, the jitter problem of MRAC controller is obviously more obvious than that of the AIC controller. According to the understanding of MRAC, this may also be due to the parameter setting of MRAC itself. In Fig. 1, it can be seen that the data output by the plant will be input to the controller, which is MRAC, which compares the data with the reference model internally. This process will produce errors, which will be passed to the adaptive algorithm module, and then the recalculated result is still with error, and finally, the result of the controller output is with error, this result is passed to Actuator and Plant. Since the model parameters are set in advance, any inappropriate situation occurs, the internal parameters of the controller will not be changed. Therefore, if the MRAC jitter problem is solved, the MRAC parameter setting and the reference model setting should be very accurate. But in general, the results of this benchmark test can show that these two controllers can meet the basic requirements of controlling the operation of the robotic arm. Although the time is different, the two advanced controllers can control the robotic arm to reach the specified pose.

This benchmarking also designed a simple robustness test for the advanced controller. According to the results, it can be seen that the AIC response to the external force is smoother, and it took more time to restore the robot arm to the state before the external force interference. Like the basic controller, although MRAC can control the robotic arm to return to the state before the interference in a short time, due to the jitter problem of MRAC itself, it does not mean that MRAC is more robust than AIC. If the further testing is needed, the jitter problem should be solved first, whether it is a basic controller or an advanced controller, this is necessary, the purpose of this is to minimize the impact of other uncertainties on the experiment.