Whole-body Control (WBC) has emerged as an important framework in locomotion control for legged robots. However, most of WBC frameworks fail to generalize beyond rigid terrains. Legged locomotion over soft terrain is difficult due to the presence of unmodeled contact dynamics that WBCs do not account for. This introduces uncertainty in locomotion and affects the stability and performance of the system. In this paper, we propose a novel soft terrain adaptation algorithm called STANCE: Soft Terrain Adaptation and Compliance Estimation. STANCE consists of a WBC that exploits the knowledge of the terrain to generate an optimal solution that is contact consistent and an online terrain compliance estimator that provides the WBC with terrain knowledge. We validated STANCE both in simulation and experiment on the Hydraulically actuated Quadruped (HyQ) robot, and we compared it against the state of the art WBC. We demonstrated the capabilities of STANCE with multiple terrains of different compliances, aggressive maneuvers, different forward velocities, and external disturbances. STANCE allowed HyQ to adapt online to terrains with different compliances (rigid and soft) without pre-tuning. HyQ was able to successfully deal with the transition between different terrains and showed the ability to differentiate between compliances under each foot.
In many constrained robotic tasks, accurate identification of contact parameters by online estimation algorithms is beneficial, for purposes such as control law adaptation or environment mapping. Stiffness estimation is a key problem for tasks with low dynamics, as incorrect stiffness parameterization in control laws can induce significant deviations from the desired behavior, up to the point of instability. Sensorial requirements for traditional stiffness identification methods include precise knowledge of object positions w.r.t. the robot, which can be difficult to obtain in practice. Accurate dynamic models of the manipulator are also often necessary. This motivates the search for alternative approaches. In this paper, we propose ANNE (Artificial Neural Network Estimator), that addresses the stiffness identification problem without the need for explicit modeling and using only force based inputs. ANNE is validated in teleoperation experiments involving WAM robot interactions with real and virtual objects. Online environment dynamic estimates are often used for the control of robots, telerobots, and haptic systems. The nonlinear Hunt–Crossley (HC) model, which is physically consistent with the behavior of soft objects with limited deformation at a single point of contact, is being increasingly used in robotic control systems. The HC model can be identified online using a single-stage log linearization technique; however, the accuracy and applicability of the existing method is limited. We propose a two-stage polynomial identification method, which uses a quadratic approximation in the first stage to generate a linearly parameterized model of the HC dynamics (Quad-Poly). The coefficients of the Quad-Poly model are then used in the second stage to extract the HC parameters using a lookup table and recursive least squares parameter estimation. The proposed method is experimentally assessed against a previous natural logarithm linearization method, and further tested for time-varying environment dynamics and human-generated trajectories and for robustness against uncertainties in the measured data and system parameters. Abstract In this paper we propose an online stiffness estimation technique for robotic tasks based only on force data, therefore, not requiring contact position information. This allows estimations to be obtained in robotic tasks involving interactions with unstructured and unknown environments where geometrical data is unavailable or unreliable. Our technique – the Candidate Observer Based Algorithm (COBA) – uses two force observers, configured with different candidate stiffnesses, to estimate online the actual target object stiffness. COBA is embedded in a force control architecture with computed torque in the task space. The theoretical presentation of the algorithm, as well as simulation tests and experimental results with a lightweight robot arm are also presented. Abstract The nature of the constitutive contact force law utilized to describe contact–impact events in solid contact interfaces plays a key role in predicting the response of multibody mechanical systems and in the simulation of engineering applications. The goal of this work is to present a comparative study on the most relevant existing viscoelastic contact force models. In the sequel of this process, their fundamental characteristics are examined and their performances evaluated. Models developed based on the Hertz contact theory and augmented with a damping term to accommodate the dissipation of energy during the impact process, which typically is a function of the coefficient of restitution between the contacting solids, are considered in this study. In particular, the identified contact force models are compared in the present study for simple solid impact problems with the sole purpose of comparing the performance of the various models and examining the corresponding system behavior. The outcomes indicate that the prediction of the dynamic behavior of contacting solids strongly depends on the selection of the contact force model. This paper proposes a method to realize desired contact normal forces between humanoids and their compliant environment. By using contact models, desired contact forces are converted to desired deformations of compliant surfaces. To achieve desired forces, deformations are controlled by controlling the contact point positions. Parameters of contact models are assumed to be known or estimated using the approach described in this paper. The proposed methods for estimating the contact parameters and controlling the contact normal force are implemented on a LWR KUKA IV arm. To verify both methods, experiments are performed with the KUKA arm while its end-effector is in contact with two different soft objects.