Design a Fall Recovery Strategy for a Wheel-Legged Quadruped Robot Using Stability Feature Space

In this paper, we introduced a conceptual analysis to select stability features when performing predefined and precise motions on robots. By analyzing the different stable poses named features and the possible transitions towards different ones, the introduced concept allows to design more predictable and suitable motions when performing particular tasks. As an example of how the concept can be applied we use it on the fall recovery of the quadruped robot CENTAURO. This robot, which is equipped with a custom hybrid wheel-legged mobility system, have good intrinsic stability as other quadrupeds. However, the characteristics of the rough terrains where it might be deployed require complex maneuvers to cope with possible strong disturbances. To prevent and more importantly recover from falls, realignment of postural responses will not be adequate, and effective recovery procedures should be developed. This paper introduces the details of how the presented conceptual analysis provides and an effective fall recovery routine for CENTAURO based on a state machine. The performance of the proposed approach is evaluated with extensive simulation trials using the dynamic model of the CENTAURO robot showing good effectiveness in recovering the robot after fall on flat and inclined surfaces

On the role of robot configuration in Cartesian stiffness control

The stiffness ellipsoid, i.e. the locus of task-space forces obtained corresponding to a deformation of unit norm in different directions, has been extensively used as a powerful representation of robot interaction capabilities. The size and shape of the stiffness ellipsoid at a given end-effector posture are influenced by both joint control parameters and - for redundant manipulators - by the chosen redundancy resolution configuration. As is well known, impedance control techniques ideally provide control parameters which realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector in an arbitrary non-singular configuration, so that arm geometry selection could appear secondary. This definitely contrasts with observations on how humans control their arm stiffness, who in fact appear to predominantly use arm configurations to shape the stiffness ellipsoid. To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of redundant arms, which explains why arm geometry also plays a fundamental role in interaction capabilities of a torque controlled robot. We show that stiffness control of realistic robot models with bounds on joint torques can't indeed achieve arbitrary stiffness ellipsoids at any given arm configuration. We first introduce the notion of maximum allowable Cartesian force/displacement (“stiffness feasibility”) regions for a compliant robot. We show that different robot configurations modify such regions, and explore the role of different configurations in defining the performance limits of Cartesian stiffness controllers. On these bases, we design a stiffness control method that suitably exploits both joint control parameters and redundancy resolution to achieve desired task-space interaction behavior

A Method for Autonomous Robotic Manipulation through Exploratory Interactions with Uncertain Environments

Expanding robot autonomy can deliver functional flexibility and enable fast deployment of robots in challenging and unstructured environments. In this direction, significant advances have been recently made in visual-perception driven autonomy, which is mainly due to the availability of rich sensory data-sets. However, current robots’ physical interaction autonomy levels still remain at a basic level. Towards providing a systematic approach to this problem, this paper presents a new context-aware and adaptive method that allows a robotic platform to interact with unknown environments. In particular, a multi-axes self-tuning impedance controller is introduced to regulate quasi-static parameters of the robot based on previous experience in interacting with similar environments and the real-time sensory data. The proposed method is also capable of differentiating internal and external disruptions, and responding to them accordingly and appropriately. An agricultural experiment with different deformable material is presented to validate robot interaction autonomy improvements, and the capability of the proposed methodology in detecting and responding to unexpected events (e.g., faults)

Detecting Object Affordances with Convolutional Neural Networks

We present a novel and real-time method to detect object affordances from RGB-D images. Our method trains a deep Convolutional Neural Network (CNN) to learn deep features from the input data in an end-to-end manner. The CNN has an encoder-decoder architecture in order to obtain smooth label predictions. The input data are represented as multiple modalities to let the network learn the features more effectively. Our method sets a new benchmark on detecting object affordances, improving the accuracy by 20% in comparison with the state-of-the-art methods that use hand-designed geometric features. Furthermore, we apply our detection method on a full-size humanoid robot (WALK-MAN) to demonstrate that the robot is able to perform grasps after efficiently detecting the object affordances

Sampled Data Control of a Compliant Actuated Joint Using On/Off Solenoid Valves

This paper proposes a new control system design method for a compliant actuated joint using on/off solenoid valves. Themathematical modelling and the system’s hardware are described in detail. The control design method is presented in ageneral manner so it could be applied for any other similar system. For the present system, the designed controller is implementedvia a digital computer and it is characterised by very good performance and simplicity. The success of the proposedmethod is validated via simulations and experiment

Bipedal Walking Energy Minimization by Reinforcement Learning with Evolving Policy Parameterization

We present a learning-based approach for minimizing the electric energy consumption during walking of a passively-compliant bipedal robot. The energy consumption is reduced by learning a varying-height center-of-mass trajectory which uses efficiently the robots passive compliance. To do this, we propose a reinforcement learning method which evolves the policy parameterization dynamically during the learning process and thus manages to find better policies faster than by using fixed parameterization. The method is first tested on a function approximation task, and then applied to the humanoid robot COMAN where it achieves significant energy reduction. © 2011 IEEE

Versatile Reactive Bipedal Locomotion Planning Through Hierarchical Optimization

© 2019 IEEE. When experiencing disturbances during locomotion, human beings use several strategies to maintain balance, e.g. changing posture, modulating step frequency and location. However, when it comes to the gait generation for humanoid robots, modifying step time or body posture in real time introduces nonlinearities in the walking dynamics, thus increases the complexity of the planning. In this paper, we propose a two-layer hierarchical optimization framework to address this issue and provide the humanoids with the abilities of step time and step location adjustment, Center of Mass (CoM) height variation and angular momentum adaptation. In the first layer, times and locations of consecutive two steps are modulated online based on the current CoM state using the Linear Inverted Pendulum Model. By introducing new optimization variables to substitute the hyperbolic functions of step time, the derivatives of the objective function and feasibility constraints are analytically derived, thus reduces the computational cost. Then, taking the generated horizontal CoM trajectory, step times and step locations as inputs, CoM height and angular momentum changes are optimized by the second layer nonlinear model predictive control. This whole procedure will be repeated until the termination condition is met. The improved recovery capability under external disturbances is validated in simulation studies

Terrain Segmentation and Roughness Estimation using RGB Data: Path Planning Application on the CENTAURO Robot

Robots operating in real world environments require a high-level perceptual understanding of the chief physical properties of the terrain they are traversing. In unknown environments, roughness is one such important terrain property that could play a key role in devising robot control/planning strategies. In this paper, we present a fast method for predicting pixel-wise labels of terrain (stone, sand, road/sidewalk, wood, grass, metal) and roughness estimation, using a single RGB-based deep neural network. Real world RGB images are used to experimentally validate the presented approach. Furthermore, we demonstrate an application of our proposed method on the centaur-like wheeled-legged robot CENTAURO, by integrating it with a navigation planner that is capable of re-configuring the leg joints to modify the robot footprint polygon for stability purposes or for safe traversal among obstacles