IROS2019国际学术会议论文集2425

Pressure-Driven Body Compliance Using Robot Skin J. Rogelio Guadarrama-Olvera, Emmanuel Dean-Leon, Florian Bergner and Gordon Cheng AbstractSkin can provide rich multi-modal contact in- formation about the interaction forces of a robot with its environment. With this new way of sensing, a new generation of compliant controllers can be developed to enable different kinds of interactions. In this paper, a pressure-driven compliance controller is presented to generate virtual compliant forces depending on both external contact forces and their area of contact. The proposed controller can be adapted for any skin technology that provides a spatial distribution of force sensors over the surface of a robot and the geometry of the contact areas. This modality of body compliance is formulated as a task function which can be inserted in strict or non-strict hierarchical task execution policies. The controller was tested on the upper body of a Humanoid robot (16 DoF) covered with robot skin. I. INTRODUCTION A key element for new applications in robotics is the capability of physical interaction between robots, the envi- ronment, and human operators 1. In this sense, physical interactions should not only consider contacts with the end- effectors, as assumed in classical applications, but with any part of the robot. Clear examples are the nursery robots as 2. These robots are intended to handle human bodies which are heavy, bulky, and sensitive. In this kind of applications, interaction forces are high but shall not be concentrated in small areas because such condition may lead to injuries in the human body. The same condition applies for handling other kinds of heavy and bulky objects as in 3. Therefore, the commonly used force sensing technologies and techniques lack sensitivity for this kind of applications, and thus, new technologies must be considered for further development. Among the new technologies developed for physical inter- action, tactile sensors have received attention because they enable compliant capabilities to standard position or velocity controlled robots 4, 5. Tactile sensors caught the attention of the robotics community since the early 80s 6. Then, during the following decades, different sensing mechanisms were applied to develop the sense of touch for robots 7. Nowadays, advanced technologies for tactile sensing embody different capabilities for pressure, temperature, and even pre- touch sensing 8, 5. A. Contact forces measurement The estimation of the position and magnitude of external contacts is paramount for developing interaction controllers. All the authors are with the Institute for Cognitive Systems, DepartmentofElectricalandComputerEngineering,Technical UniversityofMunich.rogelio.guadarramatum.de, florian.bergnertum.de,deantum.de, gordontum.de,www.ics.ei.tum.de.Specialacknowledgement to the Mexican National Council of Science and Technology (CONACYT) for supporting the fi rst author. Fig. 1: Propagation of the pressure compliance into the kinematic chain of a robot. For these purposes, tactile sensors provide practical and fast solutions over classical Force-Torque (FT) sensor imple- mentations. The main advantage of tactile sensors over FT sensors is that tactile sensors provide information about the pose (position and orientation) and the magnitude of multiple contact forces applied to a body part, as well as complete information about the number, location, and geometry of the contact areas. In contrast, FT sensors provide higher accuracy and measurement speed but are constrained to measure the net wrench at the location they are mounted in a kinematic chain. Although there are methods for estimating the contact points from FT sensor data, many of them require the combi- nation of other sensors or constraints in the kinematics. One example is the combination of an FT sensor and an RGB-D camera in 9, where the positions of the contact points are estimated using visual information, and their magnitude is calculated using the net wrench from the FT sensor. This approach shows good results yet is limited by the natural constraints of the visual sensors (e.g. visual occlusions). The probabilistic methods in 10 use a combination of an FT sensor mounted at the end-effector and the dynamic model of the robot to estimate the link where a single contact occurs. These methods rely on an accurate estimation of the robot model and are constrained to 2-D end-effector trajectories. The adaptive tool-tip contact point estimator introduced in 11 approximates the contact point location and the normal of the interaction surface. This method has good accuracy, even when the tool is not rigidly attached to the end-effector. However, it is limited to a single interaction point at the end- IEEE Robotics and Automation Letters (RAL) paper presented at the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Macau, China, November 4-8, 2019 Copyright 2019 IEEE effector. With the inclusion of tactile sensors, the pose of the contact forces and areas can be acquired directly from the sensor systems, as well as other useful information such as temperature, pre-contact and accelerations, and they cannot be occluded 5. B. Body compliance for interaction Contacts have different meanings and purposes during physical interaction. Therefore, with more information about them, a system can generate better reactive behaviors and make smarter decisions. For example, contacts can be pro- duced by hard or soft collisions between the robot and the en- vironment. In such cases, the robot must behave compliantly as in 12 to prevent damage. Moreover, when the collision occurs with a human being, a robot shall distinguish the case and show a different compliant reaction. Different features help to classify contacts. For instance, the timing and frequency components of the force profi le are used to identify different human-robot interaction cases in 13, the contact force magnitude is used to distinguish the interaction of the robot with mobile or fi xed obstacles in 14, and the thermal pattern is used to identify contacts with persons or objects in 15. Contacts can also be used to learn the dynamic parameters of the robot and the environment as in 16. As described above, contact forces (and their different meanings) can be directly or indirectly detected using a combination of sensors. However, for Human-Robot Interac- tion/Collaboration (HRI/HRC), more specialized information is required to provide a better understanding of the contact conditions i.e. contact context. In particular, one important variable that needs to be measured is how the forces are distributed within the contact areas, i.e. contact pressure. C. Contact pressure measurement Pressure sensing is an important feature of human skin that allows us to interact with the environment and protect ourselves during physical interaction. Our skin is capable of detecting concentrated contact forces, identify them as potentially harmful interactions, and trigger different re- actions according to this tactile information. This natural reactive capability is also important and needed in robotic applications. In general, pressure sensing has been mainly applied to small end-effector areas due to limitations for large-scale deployment of tactile sensors. For example, tac- tile sensors mounted on grippers and hands improved the dexterous manipulation of complex objects using tactile- based pressure sensing in 17, 18. Pressure sensors in a skin array were mounted on the soles of a biped robot in 19. These sensors were used to estimate the Center of Pressure (CoP) of the feet during walking. One example of a whole-body tactile sensor cover for industrial robots is the AIRSKIN pads from Blue Danube Robotics 20. These covers can detect when contacts occur on the whole robot to take safety reactions such as emergency stop. However, the accuracy of the sensors cannot provide information about LED ProximityNormal Force TemperatureAcceleration 16.3 mm Microcontroller Size of silicone capsule (a)(b) Skin Lattice Fig. 2: Robot skin developed in our lab 5. (a) sensors mounted on every cell. (b) microcontroller and dimensions of the cell electronics and the silicone capsule. the location of the contacts on a link or the area of the contacts. Furthermore, to the best of our knowledge, pressure sensing has never been applied to generate whole-body active compliant behaviors in robots. D. Contribution Using our robot skin (see Fig. 2), the contact pressure can be acquired at every part of the body. In this paper, we propose a pressure-based controller to enable compliant behaviors on robots. This compliance controller depends on the contact pressure (force and area) rather than completely on the contact force, making robots capable of reacting to potentially harmful physical interactions. Then, the proposed controller will react faster to highly concentrated contact force distributions than to non-concentrated distributions. This controller was evaluated experimentally on a redundant humanoid robot (H1), largely covered with robot skin 21. To illustrate the controller design, we utilize the hexagonal lattice of our robot skin. Furthermore, the same principle can be applied to other robot skin technologies with different lattice shapes 22, 23. II. PRESSURE-DRIVEN COMPLIANCE CONTROLLER WITH ROBOT SKIN The system requirements to realize the proposed controller are: The areas of the robot body where interactions are expected are covered with robot skin. The robot skin must provide information about the position and magnitude of contact forces measured by minimal elements (cells). The skin cells may have different sizes, but the areas must be known. The kinematic model of the robot is known, including link geometry and kinematic parameters, as well as the locations of the skin cells over the robots links. These requirements can be fulfi lled by some of the current robot skin technologies which use self-calibration algorithms to defi ne the pose of all the cells on the kinematic chain of the robot, e.g. 24,25. A. Pressure-driven compliance wrench Knowing the position of a cell on a link of the kinematic chain, we can defi ne a coordinate frame Ocellwhere the force sensors are located. In a neighborhood (patch) of k Fig. 3: Contact pressure measurement with robot skin. Not only the position p of the CoP can be estimated but also the contact area where each force is applied. cells, the cell coordinate frame of the i th skin cell is defi ned as Oi. In Oi, the force measured by the i th cell of area Ai is defi ned as Fi R3. If the cells only provide the magnitude of the normal force fn R, the contact force can be constructed as Fi= 0,0,fi. Then, to organize a neighborhood of cells, we defi ne a patch coordinate frame Oas a common reference frame for all the k cells. Consequently, a transformation Ti can be defi ned between Oiand O. With this relation, we can transform the forces applied in Oiinto a wrench in Owhich depends on the contact pressure w i = Ri 1 Ai Fi pi Ri 1 Ai Fi (1) where Ri SO(3) represents the orientation of Oiwith respect to O, and pi R3is the position of the origin of Oiin O. A similar calculation can be done for all the cells in the patch to build a general patch wrench w= k X i=1 w i (2) where wis equal to the wrench produced by the sum of all the forces in the k cells applied to the CoP of the contact as shown in Fig. 3. Then, we can scale wwith a gain with units of area, e.g. ?m2?, to adjust the sensitivity of the compliant controller, and to match the units for the subsequent computations. The patch wrench (2) can then be scaled as, wP= ? FP P ? = w(3) where wPis a virtual wrench depending on both the contact force and area, which will be used to implement the pressure- driven compliance. FPand Pare the virtual force and torque, respectively. B. Pressure-driven compliance controller After computing the virtual wrench (3), we can propagate it through the kinematic chain with the transposed Jacobian matrix. Therefore, knowing the kinematic parameters of the robot and the transformations from the contact point to the base link O0, the torques for the joints of the kinematic chain can be computed as P=0J (q) wP (4) where P Rnis the joint torque vector, and 0J R6n is the geometric Jacobian mapping joint velocities q onto Cartesian velocities of Owith respect to O0. Prepresents the joint-torques generated by the virtual force to produce a pressure-based compliant reactive behavior in the robot. The transformations to locate a skin patch on the kinematic chain as shown in Fig. 1 are normally computed during the calibration procedure of the robot skin systems 26, 24, 25. If multiple contacts are applied to different skin patches on the robots body, they can be accounted in the same way. Then, for a set of k contact areas, the composite pressure compliance torque is computed as: P= ?0J 1 0J 2 .0J k ?w P1w P2.w Pk ? (5) C. Implementation with other controllers The pressure-driven compliance controller (5) can be implemented alongside with other controllers to produce dif- ferent smart behaviors in robotic systems. There are different task-fusion methods to combine tasks such as gravity com- pensation, cartesian end-effector controllers, postural body impedance, self-collision avoidance controllers, and the pro- posed pressure-driven compliance controller. In the following subsections, a few methods are accounted to implement the pressure-driven compliance controller as a task for fi xed base manipulators and fl oating base systems. 1) Fixed base manipulators: Consider a robotic system in the form M(q) q + C(q, q) + g(q) = (6) where q, q and q are the joint state vector and its fi rst and second order derivatives, M(q) is the positive defi nite inertia matrix, C(q, q) is the matrix of Coriolis and centrifugal effects, g(q) the vector of gravity effects and the joint torque input of the system. If the system has enough degrees of freedom, a set of m tasks can be executed with strict or non-strict hierarchy using different null-space projection methods as shown in 27. The output of the composite output can be defi ned as = N1(1+ N2(2+ . + Nmm.)(7) where N1,2,.,mcan be weight matrices for a pondered sum or dynamically consistent null-space projectors for strict hierarchical execution as in 28. In both cases, the pressure- driven compliance can be inserted in the task set and will directly account for external forces applied to the system bodies. For the null-space projection methods, as a body compliance task, the pressure-driven compliance shall be ex- ecuted in the null-space of the primary tasks and its Jacobian matrix used to compute the projectors of the subsequent tasks in the hierarchy array. 2) Floating base systems: For free-fl oating base systems in the form M(x) + C(x,) + g(x) = S + J supportFsupport (8) where x is the state vector describing both the fl oating base position and orientation and the joint states. Rn+6is the complete velocity coordinates, containing the fl oating bases linear and rotational velocities, and joint velocities. Rn+6is the time derivative of . M(x), C(x,) and g(x) are the inertia matrix, coriolis matrix and gravity effects vector respectively. Jsupportand Fsupportare the Jacobian matrix and the force vector of the supporting contact points. S = O I is the actuated joint selection matrix. Note that x not necessarily has all its elements in R and x 6= . In this kind of systems with naturally unstable and con- strained dynamics, the pressure-driven compliance controller must be executed in the null-space of the hard constraints and the balance controllers (ground reaction force, zero moment point and centre of mass controllers) to guarantee the postural stability of the system as shown in 29 and 30. Then, the control of the system should be computed in the form = const+ Nconst(1+ N2(2+ . + Nmm.)(9) where constis the torque vector generated by the constraints and highest priority tasks and Nconstis the dynamically consistent null-space projector to execute the lower priority tasks in the null-space of the constrained tasks. With this scheme, the pressure-driven skin compliance can take the place of any task from 1 to m in order to keep the stability of the system, adopting the priority that the application requires. III. EXPERIMENTAL EVALUATION We evaluated the performance of the pressure-driven com- pliance controller in a full-size humanoid robot covered with robot skin 21. The pressure-driven compliance controller was inserted in a strict hierarchical task policy where 1 stands for the highest priority. Likewise, the task with priority m is executed in the null-space of the tasks with higher priority as described in (9). The pressure-driven compliance controller outputs a joint- torque vector. However, a torque resolver as 5 enables its implementation for position or velocity commanded robots (see Fig. 4). In this case, the torque resolver module com- putes dynamically consistent smooth trajectories for the joints, imposing desired dynamics to the robot. Two test scenarios were designed to highlight the capabilities of the pressure-driven compliance controller. A. Whole Body Pressure-Driven Compliance in a Task Set In the fi rst scenario, the task priority arrangement is as follows: 1) ZMP/CoM balance controller; 2) Pressure-driven compliance controller; 3) Posture and gravity compensation controller. The robot must keep the balance all the time while reacting to the contact pressure with the whole upper-body. The posture task holds the lowest priority to provide postural stability to the remaining DoFs after the execution of the fi rst Hardware Interface (Posit