Contact Distinction in Human-Robot Cooperation with Admittance Control

Transcription

1 Contact Distinction in Human-Robot Cooperation with Admittance Control Alexandros Kouris, Fotios Dimeas and Nikos Aspragathos Robotics Group, Dept. of Mechanical Engineering & Aeronautics University of Patras, Greece Abstract The emerging field of physical human-robot interaction raises the need to distinguish collisions over intended contacts in order to guarantee safe and seamless interaction. In this paper, a novel contact distinction method is proposed that monitors the externally applied forces/torques and is able to distinguish unexpected collisions from intended contacts during cooperative tasks. The method is based on a frequency domain analysis of the externally applied forces using the Fast Fourier Transform. Moreover, a tuning method is proposed to adjust the thresholds for the detection, according to the desired dynamic behavior of the admittance controller. The collision distinction method is evaluated experimentally in a human-robot cooperation task with multiple subjects using a 7DOF LWR manipulator. I. INTRODUCTION A precondition for the advancement of human-robot cooperation is the capability of the robot to safely interact with humans without any risk of injuries. Cooperative tasks usually require physical contact of the human with the robot [1]. Thus collision detection techniques should be able to distinguish intended contacts from unexpected collisions that may harm humans [2] and should react appropriately. Several techniques have been proposed in the literature to deal with the safety issues raised when a manipulator shares its workspace with humans. The collision detection problem is approached by focusing on the robot s perception. Computer vision has been proposed to prevent collisions, by monitoring the relative movements between the human and the robot and by re-planning the robot s trajectory to avoid unintended contacts [3]. However, machine vision algorithms are computationally heavy, particularly when dealing with dynamic environments, while blind spots can occur that reduce the reliability. Other approaches used capacitive skin [4] or proximity sensors placed on the robot s body [5] to detect and avoid upcoming contacts, but such sensors increase significantly the cost of the robot and are difficult to be incorporated to existing robots. Passive safety features, aiming to reduce the energy dissipated due to the applied force on the contact point during a collision, have also been proposed. These include the use of elastic joints, variable stiffness actuators (VSA) [6], [7], lightweight robot design [8] and covering of the robot links with impact absorbing materials [9]. Approaches that detect a collision during the contact and react appropriately before the forces build up haven been proposed in [9], [10], [11], [12]. These methods are based on /16/$31.00 c 2016 IEEE the monitoring of the external forces/torques that are applied to the robot body or the end-effector. Using a force/torque sensor at the end-effector, the measurement of external forces is straightforward. Alternatively, a model based observer can be used to estimate the external joint torques, often by using the joint torque sensors of a robot. In that way, external forces/torques applied to the entire robot body can be observed and not only at the end-effector. In order to avoid using the noisy -and often inaccurate- measurements of acceleration, an observer based on the generalized momentum of the robot was proposed in [13]. Most of the aforementioned approaches were based on the detection of external forces applied to the robot, but without being able to distinguish if they are caused by a collision or an intentional interaction and, as a result, their applicability to physical human-robot interaction is limited. A method was proposed in [14] to distinguish between intended contacts and collisions, based on the assumption that the external joint torques during a collision show a noticeably faster rate of change than the torques due to cooperation. By passing the joint torque signals through a bandpass filter, the high frequency collision components can be separated from the low-frequency interaction torques. However, when Cartesian compliance control is desired, the mapping of external forces into joint torques is configuration dependent and adaptive thresholds for each joint should be determined for accurate detection. Also, low order filtering in the time domain can be quite noisy, leading to unclear distinction between intended and unintended contacts. In this paper, a contact distinction method is proposed for human-robot cooperation, based on the observation that intended forces present different frequency characteristics from a collision. The proposed approach implements a distinction in the frequency domain and is able to separate the frequency components of the external forces/torques, reducing the errors and leading to an unambiguous distinction of the two cases. Moreover, with the introduced method, the distinction thresholds are tuned considering the desired dynamic behavior of the admittance controller. The self-tuning of the thresholds by means of linear interpolation improves seamless cooperation and reliable distinction of contacts. The rest of the paper is organized as follows. In Sec. II-A the contact distinction algorithm is presented. The effects of a Cartesian Compliance controller to the proposed method are

2 described in Sec. II-B and the tuning to the compliance control gains is analyzed in II-C. An experimental evaluation of the proposed method is conducted in Sec. III. II. COLLISION DISTINCTION IN COOPERATION A. The proposed collision distinction method When humans cooperate with a robot, the applied forces are generally smooth because the operators are aware of the movement characteristics of the robot such as direction and speed, so they can anticipate eminent contacts. On the other hand, a collision is a result of an unexpected contact between the human and the robot. Humans do not have the ability to react smoothly to unexpected contacts and the external forces are developed more rapidly, as it can be seen in Fig. 1. The amplitude of the force cannot be used as a collision indication because the contact forces are within the same order of magnitude. When transforming the force signal into the frequency domain, forces with higher rate of change, like collisions, correspond to high frequency components. Contrariwise, cooperative forces present slower rate of change and include lower frequency components. Ideally, the collision force can be approximated as a Dirac delta function, the Fourier transform of which is composed of an infinite number of sine waves, each of the same amplitude and thus contains equally all of the frequency components. The generalized external forces F h applied to the robot (including forces and torques) due to a collision, as well as due to normal cooperation, can be measured using a F/T sensor, which is very common in physical human-robot cooperation tasks, or they can be estimated from the robot s joint torque sensors [15]. The main challenge is to define frequency thresholds of the force F h to distinguish between intended contacts and unexpected collisions. A filtering of this force in the time domain, to reduce frequency components caused by cooperative forces, presents some limitations mainly because of sensor noise, low accuracy and delays. To distinguish between intended contacts from unexpected collisions, instead of using a simple disturbance observer in the time domain [14], we consider a specific high-frequency window of these disturbances. By processing the signal in the frequency domain, the noisy filtering and the phase shifting introduced by low-order filters of the time domain can be avoided and a more accurate distinction can be achieved. To transform from the time to the frequency domain, the Fast Fourier Transform (FFT) is applied on a sliding window of measurements of the external force F h, which will be shifting through the time axis whenever a new measurement is available, as shown in Fig. 2. The size N of the window and the sampling frequency are strongly connected with the accuracy of the detection algorithm and should be carefully determined. The magnitude F ω of the Discrete Fourier Transform of the time series of the i th direction of F h is (with x = F i h ): N 1 Fω m = x n e 2πimn/N m = 0,..., N 1. (1) n=0 Fig. 1: Force in time and frequency domain, during normal cooperation and collision. Fig. 2: Fourier Transform applied on a sliding window of the latest measurements. To detect collisions we focus on a specific range of frequencies of F ω, as shown in Fig. 3. This range is defined as [ω min, ω max ], where ω min and ω max values are experimentally determined. To obtain the magnitudes of F ω within this frequency range, a pointwise multiplication in the frequency domain of the vector F ω with a rectangular window w(ω min, ω max ) is implemented. Observing the force magnitude within that window, the criterion for detecting a collision is obtained as: F ω w(ω min, ω max ) F th (2) where... is the norm and each element k of w is w k {0, 1}. As cooperative forces may contain some low amplitude frequency components within the collision band, an amplitude threshold F th for this metric should also be determined, as it is shown in Fig. 3. In practice, from Eq. 2 we can detect if there are frequency components (attributed to a collision) within a specific range of frequencies and above a certain magnitude F th. The selection of the appropriate thresholds is of major importance. As it is shown in Fig. 4, the thresholds should not be set to tolerant values, as this may cause failures or Fig. 3: Partitioning of frequency domain plain, for distinguishing cooperative from collision forces and removing noise.

3 Fig. 4: Collision forces contain higher frequency components than normal cooperation force. Equal triangles A and B, are used to ensure that ω min and F th threshold values are neither too tight, nor too relaxed in the distinction. introduce crucial delays in the contact distinction and may put the operator at risk. On the other hand, it is also desired that the thresholds are not set to values that may disturb normal operation, in case for example that the operator applies a slightly more abrupt force to the manipulator. B. The compliance controller of the robot The impedance and admittance control schemes are widely used in human-robot cooperation, enabling the robot compliance to externally applied forces, by regulating the dynamic relation between external forces and motion of the robot [16]. Cartesian compliance control implements a relationship of virtual mass-spring-damper system to the end-effector (assuming only translational components) that can be expressed as: F h = M d ẍ + C d ẋ + K d x, (3) where x R 3 is the Cartesian position vector and F h R 3 is the external force vector. F h can be either measured directly using a 3-DOF force sensor attached to the robot end-effector, or can be estimated by the joint-torque sensors of a robot and the Jacobian matrix that relates the joint torques to the robot s tool forces. The gains M d, C d, K d R 3 3 are positive definite diagonal matrices that are user defined. The Cartesian compliance control of Eq. (3) can also be implemented similarly at the joint space. In order to impose the desired dynamic behavior in a motion or torque controlled manipulator, the admittance or the impedance control framework is used respectively. In both cases, the response of the robot to external forces depends on the selection of the gains M d, C d, K d. The higher the damping C d is, the more resistive the robot is to external forces, particularly at high velocities. An increased inertia M d makes the robot to resist in changes of it s state of motion. The stiffness term K d also affects the behavior, but it is usually set to null when free movement of the end-effector is desired. In order to perform the same movement of the end-effector with different virtual mass or damping values, different forces must be applied by the operator. Following that, we can understand that the distinction between intended and unintended contact forces should be determined with respect to the desired characteristic of the end-effector s dynamic behavior. C. Threshold tuning based on the parameters of the compliance controller The Cartesian compliance controller imposes a desired dynamic behavior to the robot s end-effector that can be set by the operator according to the cooperation task. Since a free movement of the robot in its workspace is considered, the stiffness term of Eq. (3) is omitted. In general, high damping and mass reduce the speed but improve the accuracy of the robot in the cooperation. On the other hand, low damping and mass are proven to help the robot to accelerate fast and make it easy to move, but the accuracy is downgraded [17]. The selection of the appropriate parameters depends on the performed tasks, whereas online adaptation of the parameters is also possible [18]. As a result, the contact distinction thresholds cannot be set to predefined values, but should be tuned in accordance to the desired characteristics of the robot s dynamic behavior. By performing a cooperative, time constraint, point to point motion, we observe the effect of varying damping and mass in the frequency content of the measured force. 1) Effect of virtual damping: Damping directly relates the applied force to the robot velocity. So, for a given and constant force applied by the operator to the robot, the velocity will be inversely proportional to the virtual damping. As the operator is trying to carry out the desired task in cooperation with the robot, the higher the damping, the higher the external force the operator should apply. However, increased damping is used to reduce the oscillations that can be caused by sensor noise and improve the precision of the positioning. The magnitude of the force signal is increased in proportion to the damping. Thus the overall magnitude of the cooperation force is expected to be amplified throughout the frequency range when high damping values are set, as it is shown in Fig. 5. This will also apply to the previously negligible cooperation forces that are present within the collision bandwidth. So the threshold F th should be increased. During a collision with high damping, the contact force builds up more abruptly because the robot is less responsive. Consequently, the collision detection thresholds must be suitably adjusted, by increasing the lower bound of the frequency window ω min. Fig. 5: External force applied on the robot by the operator for the same task is generally increased in proportion to virtual damping.

4 Fig. 7: Tuned ω min and F th threshold values, with respect to robot s dynamic behavior characteristics (determined after experimental procedure). Fig. 6: External force applied on the robot by the operator for the same task contains higher frequency components as virtual mass is increased. 2) Effect of virtual inertia: The inertial forces are perceived by the operators when the robot s velocity changes. By increasing that inertia, the robot becomes more difficult to accelerate or decelerate but presents more smooth motion. On the contrary, low inertia makes the robot more responsive but may cause vibrations and lead to instability. Depending on the operators experience, increased inertia will introduce more rapidly changing and higher amplitude forces during normal cooperation and mainly during acceleration and deceleration. Unlike damping, increasing the inertia does not affect the external force at constant velocity. To perform the same task, the required force is significantly increased during acceleration or deceleration of the robot. Due to the robot s tendency to maintain its state of motion, these forces will be quickly escalated when the inertia is increased, as it can be seen in Fig. 6. Keeping the same thresholds will cause unclear distinction between collisions and normal cooperative forces, therefore tuning of the collision detection thresholds is necessary to address these issues as well. The previous discussion leads us to turn into higher frequency range to check for collisions, by increasing the ω min threshold. The amplitude of high frequency components that appear will be decreased compared to the amplitude in the lower frequency windows that was examined before. Thus, in case of high inertia, sliding the critical frequency window into higher values, will lead to decreasing the F th threshold. The threshold behavior is examined experimentally for some sampled pairs of virtual damping and mass. By using linear interpolation, we can obtain thresholds for every possible combination of the compliance control parameters, within some predefined limits that ensure the robot s stability and are appropriate for human-robot cooperation. To conclude with, an increment of damping leads the distinction method to focus on higher frequency components and to higher force thresholds within this window. On the other hand, an increment of the virtual mass, causes a significant increase in the frequency range of interest, but this shifting requires from the method to reduce the force threshold that distinguishes collision from cooperation. Fig. 8: Admittance Controller for Physical Human-Robot Cooperation III. EXPERIMENTAL EVALUATION The proposed method is evaluated experimentally in a human-robot cooperation task with a 7-DOF LWR4+ manipulator. The external forces F h are measured with a force sensor, that is attached between the robot and a handle, with sampling rate equal to 1KHz. The FFT window size N is set to 1024 samples, leading to a resolution of 0.98Hz at the result of the transformation. The robot is controlled using the Cartesian admittance control scheme, as it is shown in Fig. 8. Without loss of generality, only one axis of the end-effector is enabled, by keeping constant orientation and the rest axes locked. Seven volunteers acted as operators by physically cooperating with the robot by applying force for a point to point task along a single axis of the Cartesian frame attached to the endeffector. The desired dynamic behavior is initially set to low values: C d = 20Ns/m, M d = 2kg and the thresholds are then set equal to ω min = 8Hz, F th = 0.57N, as described in Fig. 7. The upper frequency of the rectangular window w in Eq. (2) is constant for all experiments and equal to ω max = 100Hz. A laser pointer attached to the robot projects the end-effector s position on a highlighted surface in front of the operator, in order to assist him during the point to point task. Initially, each operator performs the task undisturbed, whereas on the second part of the experiment a number of collisions are simulated during the cooperation by another operator, who causes a few arbitrary contacts at the point of interaction. Our purpose is to distinguish the collisions from the cooperation quickly and reliably. No reaction strategy is implemented on the detection, as it is beyond the scope of this work. A. Results Experiments showed that collisions are clearly distinguished from cooperative forces, for all subjects, ensuring both safe

5 Fig. 9: Human-Robot Cooperative task Fig. 11: Experiment results (cooperation and collision) with Cd = 50N s/m, Md = 2kg. Tuned thresholds succeed in distinction, whereas previously defined thresholds lead to false collision detections. It is indicative that collision forces are showing significantly higher rate of change than cooperative forces. Fig. 10: Spectrogram indicating that collision forces contain much higher frequency components that cooperative forces, ensuring the distinction between them by our method. and seamless cooperation. As it can be seen in a representative spectrogram from a subject in Fig. 10, small force amplitudes exist in the collision area during cooperation, but they are considered negligible and are ignored due to Fth threshold. During the normal cooperation, no false positive detections were observed. Moreover, all collisions that occurred were successfully identified by clearly exceeding the defined thresholds, regardless of the end-effector s velocity and its relative direction with the collision force. Examining a representative collision in contrast with a relative snapshot of the FFT window during normal cooperation using the aforementioned dynamic characteristics (Table I), we experimentally confirm that although the amplitude of the applied force during a cooperative movement and a collision may be the same, the duration within which the force escalates in a collision is much shorter leading to higher frequency components. 1) Increasing Damping: During the experiments it was confirmed that by keeping the thresholds at the same levels when damping is increased, will cause multiple false positives, disturbing the performed task and downgrading the performance. This issue was addressed by tuning the thresholds with the proposed method, as it can be seen in Fig. 11. The tuned thresholds are able to successfully detect all of the collisions. For the above experimental results the desired dynamic behavior characteristics were set equal to Cd = 50N s/m, Md = 2kg and thresholds are updated to the appropriate values: ωmin = 14Hz, Fth = 0.63N, as described in Fig. 7. In Fig. 11, it is also shown that the magnitude of the collision force does not exceed the magnitude of cooperative forces. On the other hand, the escalation rate is remarkably higher, which results in the distinction using the proposed method. 2) Increasing Mass: In Fig. 12 it is confirmed that keeping the thresholds at the same levels and increasing the virtual mass, will lead to wrong collision detections as well. By updating the threshold values in accordance to what has been presented in Sec. II-C, collisions can be successfully distinguished from intended contacts. Although collision and cooperative force amplitude are still comparable, cooperative forces in this case show a noticeably faster rate of change, compared to the previous ones. This leads to the selection of an increased ωmin threshold. However, the collision force is still escalating faster, and thus collisions are clearly distinguished. For the above experimental results, the desired dynamic admittance gains are set to Cd = 20N s/m, Md = 14kg and the thresholds are updated to the appropriate values: ωmin = 19Hz, Fth = 0.38N, as it is illustrated in Fig. 7. IV. C ONCLUSION In this paper a novel contact distinction method is introduced that can be used to distinguish intended contacts from

6 tuning of thresholds with respect to robot s dynamic behavior is proved to be necessary in order to avoid false positive or negative collision detections. V. ACKNOWLEDGMENTS Fotios Dimeas is funded by IKY fellowships of excellence for postgraduate studies in Greece - Siemens program. REFERENCES Fig. 12: Experiment results (cooperation and collision) with C d = 20Ns/m, M d = 14kg. High frequency components are present in collaboration, and thus tuning the thresholds is required to successfully distinguish them from collisions. The rate of change of the collision force is further increased and distinction is succeeded by shifting to higher frequency bandwidth. TABLE I: Force amplitude and duration in cooperation and collision for varying dynamic behavior characteristics of robot. C d = 20Ns/m, M d = 14kg Max(F)-Min(F) Duration Cooperation 7.7N 997ms Collision 7.6N 17ms C d = 50Ns/m, M d = 2kg Max(F)-Min(F) Duration Cooperation 19.0N 908ms Collision 19.1N 21ms C d = 20Ns/m, M d = 14kg Max(F)-Min(F) Duration Cooperation 28.1N 259ms Collision 26.4N 12ms unexpected collisions and can be used during physic humanrobot cooperation to ensure human safety. By monitoring the externally applied force/torques and by implementing the distinction in the frequency domain, a very accurate detection is achieved. Furthermore, a tuning of detection thresholds is examined in order to deal with the varying dynamic characteristics of the admittance controller during the cooperation. An experimental analysis shows that this tuning is required, as the dynamic behavior of robot affects the bandwidth of the developed forces. 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