Adaptive Force Controller for Contact-Rich Robotic Systems using an Unscented Kalman Filter

In multi-point contact systems, precise force control is crucial for achieving stable and safe interactions between robots and their environment. Thus, we demonstrate an admittance controller with auto-tuning that can be applied for these systems. The controller's objective is to track the target wrench profiles of each contact point while considering the additional torque due to rotational friction. Our admittance controller is adaptive during online operation by using an auto-tuning method that tunes the gains of the controller while following user-specified training objectives. These objectives include facilitating controller stability, such as tracking the wrench profiles as closely as possible, ensuring control outputs are within force limits that minimize slippage, and avoiding configurations that induce kinematic singularity. We demonstrate the robustness of our controller on hardware for both manipulation and locomotion tasks using a multi-limbed climbing robot.Comment: Submitted to IROS 202

Human-Robot Collaboration for Kinesthetic Teaching

Recent industrial interest in producing smaller volumes of products in shorter time frames, in contrast to mass production in previous decades, motivated the introduction of human–robot collaboration (HRC) in industrial settings, as an attempt to increase flexibility in manufacturing applications by incorporating human intelligence and dexterity to these processes. This thesis presents methods for improving the involvement of human operators in industrial settings where robots are present, with a particular focus on kinesthetic teaching, i.e., manually guiding the robot to define or correct its motion, since it can facilitate non-expert robot programming.To increase flexibility in the manufacturing industry implies a loss of a fixed structure of the industrial environment, which increases the uncertainties in the shared workspace between humans and robots. Two methods have been proposed in this thesis to mitigate such uncertainty. First, null-space motion was used to increase the accuracy of kinesthetic teaching by reducing the joint static friction, or stiction, without altering the execution of the robotic task. This was possible since robots used in HRC, i.e., collaborative robots, are often designed with additional degrees of freedom (DOFs) for a greater dexterity. Second, to perform effective corrections of the motion of the robot through kinesthetic teaching in partially-unknown industrial environments, a fast identification of the source of robot–environment contact is necessary. Fast contact detection and classification methods in literature were evaluated, extended, and modified to use them in kinesthetic teaching applications for an assembly task. For this, collaborative robots that are made compliant with respect to their external forces/torques (as an active safety mechanism) were used, and only embedded sensors of the robot were considered.Moreover, safety is a major concern when robotic motion occurs in an inherently uncertain scenario, especially if humans are present. Therefore, an online variation of the compliant behavior of the robot during its manual guidance by a human operator was proposed to avoid undesired parts of the workspace of the robot. The proposed method used safety control barrier functions (SCBFs) that considered the rigid-body dynamics of the robot, and the method’s stability was guaranteed using a passivity-based energy-storage formulation that includes a strict Lyapunov function.All presented methods were tested experimentally on a real collaborative robot

Towards sensor-based manipulation of flexible objects

International audience— This paper presents the FLEXBOT project, a joint LIRMM-QUT effort to develop (in the near future) novel methodologies for robotic manipulation of flexible and deformable objects. To tackle this problem, and based on our past experiences, we propose to merge vision and force for manipulation control, and to rely on Model Predictive Control (MPC) and constrained optimization to program the object future shape. Index Terms— Control for object manipulation, learning from human demonstration, sensor fusion based on tactile, force and vision feedback. I. CONTEXT This abstract does not present experimental results, but aims at giving some preliminary hints on how flexible robot manipulation should be realized in the near future, particularly in the context of the FLEXBOT project, jointly submitted to the PHC FASIC Program 1 by LIRMM and QUT researchers. The objective of FLEXBOT is to solve one of the most challenging open problems in robotics. In fact, we aim at developing novel methodologies enabling robotic manipulation of flexible and deformable objects. The motivation comes from numerous applications, including the domestic, industrial, and medical examples 2 shown in Fig. 1. Many difficulties emerge when dealing with flexible manipulation. In the first place, the object deformation model (involving elasticity or plasticity) must be known, to derive the robot control inputs required for reconfiguring its shape. Ideally, this model should be derived online, while manipulating , with a simultaneous estimation and control approach, as commonly done in active perception and visual servoing. Hence perception, particularly from vision and force, will be indispensable. This leads to a second major difficulty: deformable object visual tracking. In fact, most current visual object tracking algorithms rely on rigidity, an assumption that is not valid here. A third challenge will consist in generating control inputs that comply with the shape the object is expected to have in the near future. In the next section, we provide a brief survey of the state of art on flexible object manipulation. We then conclude by proposing some novel methodologies for addressing the problem