This paper presents a multi-contact approach to generalized humanoid fall mitigation planning that unifies inertial shaping, protective stepping, and hand contact strategies. The planner optimizes both the contact sequence and the robot state trajectories. A high-level tree search is conducted to iteratively grow a contact transition tree. At each edge of the tree, trajectory optimization is used to calculate robot stabilization trajectories that produce the desired contact transition while minimizing kinetic energy. Also, at each node of the tree, the optimizer attempts to find a self-motion (inertial shaping movement) to eliminate kinetic energy. This paper also presents an efficient and effective method to generate initial seeds to facilitate trajectory optimization. Experiments demonstrate show that our proposed algorithm can generate complex stabilization strategies for a simulated robot under varying initial pushes and environment shapes.
The problem of balancing a biped or humanoid in response to external disturbance has been an active topic of research for some time @cite_17 @cite_3 . Strategies proposed to address this topic can be classified into two main categories: fixed contact and contact modification . Fixed contact strategies aim to recover the robot purely through joint effort to regulate linear and angular momentum, all while maintaining the current contact state. Two representatives are the ankle strategy and hip strategy @cite_23 @cite_15 . For larger disturbances it is impossible to recover to a stationary state using joint torques alone. Contact modification strategies reduce momentum by making contact at the robot extremities, which transfers kinetic energy from the robot into the environment. Examples of this type of strategy include protective stepping @cite_11 , hand contact @cite_5 @cite_9 , knee contact @cite_25 , tripod posture @cite_2 , and contact with accessories such as a backpack @cite_27 and walking sticks @cite_6 .
@cite_9 @cite_17 @cite_3 @cite_6 @cite_27 @cite_23 @cite_2 @cite_5 @cite_15 @cite_25 @cite_11
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Feedback control of robotic systems interacting with the environment through contacts is a central topic in legged robotics. One of the main challenges posed by this problem is the choice of a model sufficiently complex to capture the discontinuous nature of the dynamics but simple enough to allow online computations. Linear models have proved to be the most effective and reliable choice for smooth systems; we believe that piecewise affine (PWA) models represent their natural extension when contact phenomena occur. Discrete-time PWA systems have been deeply analyzed in the field of hybrid Model Predictive Control (MPC), but the straightforward application of MPC techniques to complex systems, such as a humanoid robot, leads to mixed-integer optimization problems which are not solvable at real-time rates. Explicit MPC methods can construct the entire control policy offline, but the resulting policy becomes too complex to compute for systems at the scale of a humanoid robot. In this paper we propose a novel algorithm which splits the computational burden between an offline sampling phase and a limited number of online convex optimizations, enabling the application of hybrid predictive controllers to higher-dimensional systems. In doing so we are willing to partially sacrifice feedback optimality, but we set stability of the system as an inviolable requirement. Simulation results of a simple planar humanoid that balances by making contact with its environment are presented to validate the proposed controller. This paper studies an optimal planning of falling motions of a human-sized humanoid robot to reduce the damage of the robot. We developed a human-sized robot HRP-2FX which has a simplified humanoid robot shape with seven d.o.f. and can emulate motions in the sagittal plane of a humanoid robot. We simulated falling motions of HRP-2FX which generated by using an optimal control method to minimize a performance index, and verified the optimality. 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. Safety and robustness will become critical issues when humanoid robots start sharing human environments in the future. In physically interactive human environments, a catastrophic fall is the main threat to safety and smooth operation of humanoid robots, and thus it is critical to explore how to manage an unavoidable fall of humanoids. This paper deals with the problem of reducing the impact damage to a robot associated with a fall. A common approach is to employ damage-resistant design and apply impact-absorbing material to robot limbs, such as the backpack and knee, that are particularly prone to fall related impacts. In this paper, we select the backpack to be the most preferred body segment to experience an impact. We proceed to propose a control strategy that attempts to re-orient the robot during the fall such that it impacts the ground with its backpack. We show that the robot can fall on the backpack even when it starts falling sideways. This is achieved by utilizing dynamic coupling, i.e., by rotating the swing leg aiming to generate spin rotation of the trunk (backpack), and by rotating the trunk backward to drive the trunk to touch down with the backpack. The planning and control algorithms for fall are demonstrated in simulation.Copyright © 2011 by ASME We extend simple models previously developed for humanoids to large push recovery. Using these simple models, we develop analytic decision surfaces that are functions of reference points, such as the center of mass and center of pressure, that predict whether or not a fall is inevitable. We explore three strategies for recovery: 1) using ankle torques, 2) moving internal joints, and 3) taking a step. These models can be used in robot controllers or in analysis of human balance and locomotion. This paper addresses a new control strategy to reduce the damage to a humanoid robot during a fall. Instead of following the traditional approach of finding a favorable configuration with which to fall to the ground, this method attempts to stop the robot from falling all the way to the ground. This prevents the full transfer of the robot's potential energy to kinetic energy, and consequently results in a milder impact. The controlled motion of the falling robot involves a sequence of three deliberate contacts to the ground with the swing foot and two hands, in that order. In the final configuration the robot's center of mass (CoM) remains relatively high from the floor and the robot has a relatively stable three-point contact with the ground; hence the name tripod fall. The optimal location of the three contacts are learned through reinforcement learning algorithm. The controller is simulated on a full size humanoid, and experimentally tested on the NAO humanoid robot. In this work we apply our fall controller only to a forward fall. 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. While humans are highly efficient in dealing with balance perturbations, current biped humanoid robots are far from showing similar skills. This is mainly due to the limited capacity of current robot controllers to deal with the inherently complex dynamics of biped robots. Though Model Predictive Control schemes have shown improved robustness to perturbations, they still suffer from a few shortcomings such as not considering the upper body inertial effects or non-optimal step durations. We propose here a Model Predictive Control scheme that specifically addresses these shortcomings and generates human-like responses to perturbations, involving appropriate combinations of ankle, hip and stepping strategies, with automatically adjusted step durations. The emphasis of this paper is on modeling and analyzing the effects of different cost functions and coefficients on the behavior of the controller while leaving real-time implementations and experiments for later work. We present an optimization-based control strategy for generating whole-body trajectories for humanoid robots in order to minimize damage due to falling. In this work, the falling problem is formulated using optimal control where we seek to minimize the impulse on impact with the ground, subject to the full-body dynamics and constraints of the robot in joint space. We extend previous work in this domain by numerically approximating the resulting optimal control, generating open-loop trajectories by solving an equivalent nonlinear programming problem. Compared to previous results in falling optimization, the proposed framework is extendable to more complex dynamic models and generate trajectories that are guaranteed to be physically feasible. These results are implemented in simulation using models of dynamically balancing humanoid robots in several experimental scenarios. In order to interact with human environments, humanoid robots require safe and compliant control which can be achieved through force-controlled joints. In this paper, full body step recovery control for robots with force-controlled joints is achieved by adding model-based feed-forward controls. Push Recovery Model Predictive Control (PR-MPC) is presented as a method for generating full-body step recovery motions after a large disturbance. Results are presented from experiments on the Sarcos Primus humanoid robot that uses hydraulic actuators instrumented with force feedback control.