Reducing the complexity of higher order problems can enable solving them in analytical ways. In this paper, we propose an analytic whole body motion generator for humanoid robots. Our approach targets inexpensive platforms that possess position controlled joints and have limited feedback capabilities. By analysing the mass distribution in a humanoid-like body, we find relations between limb movement and their respective CoM positions. A full pose of a humanoid robot is then described with five point-masses, with one attached to the trunk and the remaining four assigned to each limb. The weighted sum of these masses in combination with a contact point form an inverted pendulum. We then generate statically stable poses by specifying a desired upright pendulum orientation, and any desired trunk orientation. Limb and trunk placement strategies are utilised to meet the reference CoM position. A set of these poses is interpolated to achieve stable whole body motions. The approach is evaluated by performing several motions with an igus Humanoid Open Platform robot. We demonstrate the extendability of the approach by applying basic feedback mechanisms for disturbance rejection and tracking error minimisation.
Reducing the complexity of a humanoid robot representation, while retaining most of the relevant information for control purposes led to the development of various models. @cite_6 introduced the 3D Linear Inverted Pendulum, which allowed for generation of a biped walk depending on desired velocity and direction. In this model, the mass stays at a constant height and a change in the robot's configuration cannot influence it by definition. The Reaction Mass Pendulum described by @cite_17 is a more comprehensive model, that uses three pairs of equal point masses at different radial distances to shape the inertia of the robot. These masses are abstract however, and do not map directly to a specific limb or body part. Describing the robot by distributed masses was also done by @cite_7 for the purpose of fast gait generation. Their simplified model allows for fast calculations, but does not consider upper body movement and kinematic constraints.
@cite_7 @cite_6 @cite_17
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A number of conceptually simple but behavior-rich "inverted pendulum" humanoid models have greatly enhanced the understanding and analytical insight of humanoid dynamics. However, these models do not incorporate the robot's angular momentum properties, a critical component of its dynamics. We introduce the reaction mass pendulum (RMP) model, a 3D generalization of the better-known reaction wheel pendulum. The RMP model augments the existing models by compactly capturing the robot's centroidal momenta through its composite rigid body (CRB) inertia. This model provides additional analytical insights into legged robot dynamics, especially for motions involving dominant rotation, and leads to a simpler class of control laws. In this paper we show how a humanoid robot of general geometry and dynamics can be mapped into its equivalent RMP model. A movement is subsequently mapped to the time evolution of the RMP. We also show how an "inertia shaping" control law can be designed based on the RMP. This paper presents three feedback controllers that achieve an asymptotically stable, periodic, and fast walking gait for a 3-D bipedal robot consisting of a torso, revolute knees, and passive (unactuated) point feet. The walking surface is assumed to be rigid and flat; the contact between the robot and the walking surface is assumed to inhibit yaw rotation. The studied robot has 8 DOF in the single support phase and six actuators. In addition to the reduced number of actuators, the interest of studying robots with point feet is that the feedback control solution must explicitly account for the robot's natural dynamics in order to achieve balance while walking. We use an extension of the method of virtual constraints and hybrid zero dynamics (HZD), a very successful method for planar bipeds, in order to simultaneously compute a periodic orbit and an autonomous feedback controller that realizes the orbit, for a 3-D (spatial) bipedal walking robot. This method allows the computations for the controller design and the periodic orbit to be carried out on a 2-DOF subsystem of the 8-DOF robot model. The stability of the walking gait under closed-loop control is evaluated with the linearization of the restricted Poincare map of the HZD. Most periodic walking gaits for this robot are unstable when the controlled outputs are selected to be the actuated coordinates. Three strategies are explored to produce stable walking. The first strategy consists of imposing a stability condition during the search of a periodic gait by optimization. The second strategy uses an event-based controller to modify the eigenvalues of the (linearized) Poincare map. In the third approach, the effect of output selection on the zero dynamics is discussed and a pertinent choice of outputs is proposed, leading to stabilization without the use of a supplemental event-based controller. It is known that for a large magnitude push a human or a humanoid robot must take a step to avoid a fall. Despite some scattered results, a principled approach towards "when and where to take a step" has not yet emerged. Towards this goal, we present methods for computing capture points and the capture region, the region on the ground where a humanoid must step to in order to come to a complete stop. The intersection between the capture region and the base of support determines which strategy the robot should adopt to successfully stop in a given situation. Computing the capture region for a humanoid, in general, is very difficult. However, with simple models of walking, computation of the capture region is simplified. We extend the well-known linear inverted pendulum model to include a flywheel body and show how to compute exact solutions of the capture region for this model. Adding rotational inertia enables the humanoid to control its centroidal angular momentum, much like the way human beings do, significantly enlarging the capture region. We present simulations of a simple planar biped that can recover balance after a push by stepping to the capture region and using internal angular momentum. Ongoing work involves applying the solution from the simple model as an approximate solution to more complex simulations of bipedal walking, including a 3D biped with distributed mass. Humanoid robots are prone to fall caused by disturbances. If the disturbance is weak, a humanoid robot can avoid falling by using feedback control. If the disturbance is strong, humanoid robots can perform an Ukemi motion: an active shock-reducing motion. This research proposes a technique for selecting an optimal strategy to handle disturbances while walking, and describes a method of generating Ukemi motion. This technique detects disturbances using sensor data while walking steadily. Moreover, an experiment with a real robot was performed where fall detection was computed using discriminant analysis of walking data labelled as fall and non-fall. Furthermore, this study uses the three-dimensional linear inverted pendulum mode (3D-LIPM), which has been utilized in gait generation of humanoid robots, to generate a solution for the center of gravity for the contracting expanding fall correction movement. The effectiveness of the proposed technique was confirmed by the verification experiments.