State Estimation for a Humanoid Robot

This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in [1] on a quadruped platform by incorporating the rotational constraints imposed by the humanoid's flat feet. As in previous work, the proposed Extended Kalman Filter (EKF) accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. The filter employs a sensor-based prediction model which uses inertial data from an IMU and corrects for integrated error using a kinematics-based measurement model which relies on joint encoders and a kinematic model to determine the relative position and orientation of the feet. A nonlinear observability analysis is performed on both the original and updated filters and it is concluded that the new filter significantly simplifies singular cases and improves the observability characteristics of the system. Results on simulated walking and squatting datasets demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.Comment: IROS 2014 Submission, IEEE/RSJ International Conference on Intelligent Robots and Systems (2014) 952-95

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Development of a Locomotion and Balancing Strategy for Humanoid Robots

The locomotion ability and high mobility are the most distinguished features of humanoid robots. Due to the non-linear dynamics of walking, developing and controlling the locomotion of humanoid robots is a challenging task. In this thesis, we study and develop a walking engine for the humanoid robot, NAO, which is the official robotic platform used in the RoboCup Spl. Aldebaran Robotics, the manufacturing company of NAO provides a walking module that has disadvantages, such as being a black box that does not provide control of the gait as well as the robot walk with a bent knee. The latter disadvantage, makes the gait unnatural, energy inefficient and exert large amounts of torque to the knee joint. Thus creating a walking engine that produces a quality and natural gait is essential for humanoid robots in general and is a factor for succeeding in RoboCup competition. Humanoids robots are required to walk fast to be practical for various life tasks. However, its complex structure makes it prone to falling during fast locomotion. On the same hand, the robots are expected to work in constantly changing environments alongside humans and robots, which increase the chance of collisions. Several human-inspired recovery strategies have been studied and adopted to humanoid robots in order to face unexpected and avoidable perturbations. These strategies include hip, ankle, and stepping, however, the use of the arms as a recovery strategy did not enjoy as much attention. The arms can be employed in different motions for fall prevention. The arm rotation strategy can be employed to control the angular momentum of the body and help to regain balance. In this master\u27s thesis, I developed a detailed study of different ways in which the arms can be used to enhance the balance recovery of the NAO humanoid robot while stationary and during locomotion. I model the robot as a linear inverted pendulum plus a flywheel to account for the angular momentum change at the CoM. I considered the role of the arms in changing the body\u27s moment of inertia which help to prevent the robot from falling or to decrease the falling impact. I propose a control algorithm that integrates the arm rotation strategy with the on-board sensors of the NAO. Additionally, I present a simple method to control the amount of recovery from rotating the arms. I also discuss the limitation of the strategy and how it can have a negative impact if it was misused. I present simulations to evaluate the approach in keeping the robot stable against various disturbance sources. The results show the success of the approach in keeping the NAO stable against various perturbations. Finally,I adopt the arm rotation to stabilize the ball kick, which is a common reason for falling in the soccer humanoid RoboCup competitions

Feedback Error Learning for Rhythmic Motor Primitives

Abstract — Rhythmic motor primitives can be used to learn a variety of oscillatory behaviors from demonstrations or reward signals, e.g., hopping, walking, running and ball-bouncing. However, frequently, such rhythmic motor primitives lead to failures unless a stabilizing controller ensures their functionality, e.g., a balance controller for a walking gait. As an ideal oscillatory behavior requires the stabilizing controller only for exceptions, e.g., to prevent failures, we devise an online learning approach that reduces the dependence on the stabilizing controller. Inspired by related approaches in model learning, we employ the stabilizing controller’s output as a feedback error learning signal for adapting the gait. We demonstrate the resulting approach in two scenarios: a rhythmic arm’s movements and gait adaptation of an underactuated biped. I

A sensory-based adaptive walking control algorithm for variable speed biped robot gaits

A balance scheme for handling variable speed gaits was implemented on an experimental biped. The control scheme used pre-planned but adaptive motion sequences in combination with closed loop reactive control. CMAC neural networks were responsible for the adaptive control of side-to-side and front-to-back balance. The biped performance improved with neural network training. The biped was able to walk with variable speed gaits, and to change gait speeds on the fly. The slower gait speeds required statically balanced walking, while the faster speeds required dynamically balanced walking. It was not necessary to distinguish between the two balance modes within the controller. Following training, the biped was able to walk with continuous motion on flat, non-slippery surfaces at forward progression velocities in the range of 21 cm/min to 72 cm/min, with average stride lengths of 6.5 cm

A novel approach to user controlled ambulation of lower extremity exoskeletons using admittance control paradigm

The robotic lower extremity exoskeletons address the ambulatory problems confronting individuals with paraplegia. Paraplegia due to spinal cord injury (SCI) can cause motor deficit to the lower extremities leading to inability to walk. Though wheelchairs provide mobility to the user, they do not provide support to all activities of everyday living to individuals with paraplegia. Current research is addressing the issue of ambulation through the use of wearable exoskeletons that are pre-programmed. There are currently four exoskeletons in the U.S. market: Ekso, Rewalk, REX and Indego. All of the currently available exoskeletons have 2 active Degrees of Freedom (DOF) except for REX which has 5 active DOF. All of them have pre-programmed gait giving the user the ability to initiate a gait but not the ability to control the stride amplitude (height), stride frequency or stride length, and hence restricting users’ ability to navigate across different surfaces and obstacles that are commonly encountered in the community. Most current exoskeletons do not have motors for abduction or adduction to provide users with the option for movement in coronal plane, hence restricting user’s ability to effectively use the exoskeletons. These limitations of currently available pre-programmed exoskeleton models are sought to be overcome by an intuitive, real time user-controlled control mechanism employing admittance control by using hand-trajectory as a surrogate for foot trajectory. Preliminary study included subjects controlling the trajectory of the foot in a virtual environment using their contralateral hand. The study proved that hands could produce trajectories similar to human foot trajectories when provided with haptic and visual feedback. A 10 DOF 1/2 scale biped robot was built to test the control paradigm. The robot has 5 DOF on each leg with 2 DOF at the hip to provide flexion/extension and abduction/adduction, 1 DOF at the knee to provide flexion and 2 DOF at the ankle to provide flexion/extension and inversion/eversion. The control mechanism translates the trajectory of each hand into the trajectory of the ipsilateral foot in real time, thus providing the user with the ability to control each leg in both sagittal and coronal planes using the admittance control paradigm. The efficiency of the control mechanism was evaluated in a study using healthy subjects controlling the robot on a treadmill. A trekking pole was attached to each foot of the biped. The subjects controlled the trajectory of the foot of the biped by applying small forces in the direction of the required movement to the trekking pole through a force sensor. The algorithm converted the forces to Cartesian position of the foot in real time using admittance control; the Cartesian position was converted to joint angles of the hip and knee using inverse kinematics. The kinematics, synchrony and smoothness of the trajectory produced by the biped robot was evaluated at different speeds, with and without obstacles, and compared with typical walking by human subjects on the treadmill. Further, the cognitive load required to control the biped on the treadmill was evaluated and the effect of speed and obstacles with cognitive load on the kinematics, synchrony and smoothness was analyzed