Posture and balance control for biped robots based on contact force optimization

Ott, Christian; Roa, Máximo A.; Hirzinger, Gerd

doi:10.1109/humanoids.2011.6100882

Cited by 213 publications

(185 citation statements)

References 19 publications

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“…: R 3×3 → R 3 is a mapping from a rotation matrix to the associated rotation vector, ω b ∈ R 3 is the angular velocity of the base. As shown in [21], if the CoM velocity is used as a generalized velocity instead of the base velocity, the robot's dynamic equations get simplified. In this case, we can write the centroidal robot dynamics as in [17]:…”

Section: Whole-body Controllermentioning

confidence: 99%

Trajectory and foothold optimization using low-dimensional models for rough terrain locomotion

Mastalli

Focchi

Havoutis

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We present a trajectory optimization framework for legged locomotion on rough terrain. We jointly optimize the center of mass motion and the foothold locations, while considering terrain conditions. We use a terrain costmap to quantify the desirability of a foothold location. We increase the gait's adaptability to the terrain by optimizing the step phase duration and modulating the trunk attitude, resulting in motions with guaranteed stability. We show that the combination of parametric models, stochastic-based exploration and receding horizon planning allows us to handle the many local minima associated with different terrain conditions and walking patterns. This combination delivers robust motion plans without the need for warm-starting. Moreover, we use soft-constraints to allow for increased flexibility when searching in the cost landscape of our problem. We showcase the performance of our trajectory optimization framework on multiple terrain conditions and validate our method in realistic simulation scenarios and experimental trials on a hydraulic, torque controlled quadruped robot.

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Section: Whole-body Controllermentioning

confidence: 99%

Trajectory and foothold optimization using low-dimensional models for rough terrain locomotion

Mastalli

Focchi

Havoutis

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…Both methods are efficient for small scale humanoids, but they may not be applicable to humanoids with larger sizes. Usually, postural balance of a humanoid can be recovered through contact force optimization [5], [6]. With this in mind, we designed an automated method which can analyze the force sensory data, acquired from sensors in the feet of a robot or a human.…”

Section: Related Workmentioning

confidence: 99%

The anatomy of a fall: Automated real-time analysis of raw force sensor data from bipedal walking robots and humans

Kormushev

Uğurlu

Colasanto

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-An automated approach is proposed which can analyze ground reaction force data from bipedal walking robots and humans. The input of the automated analysis is the raw data from force sensors mounted in the feet of a robot. The output is detailed information, such as detected single support, double support, and swing phases, their durations, timings of events like heel strikes, properties of the phase transitions and of the robot itself. The proposed approach is generic, parameter-free, model-free, robust, computationally efficient, and applicable for real-time use during walking. It can detect early indications of instability that could lead to a fall of the robot. Three real-world experiments are presented: with a compliant bipedal robot, with a stiff humanoid robot, and with a human subject.

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“…Additionally, a joint impedance controller is applied to improve tracking q * andq * with the current system stateq andq respectively. To stabilize the walking motion, τ balance , similarly to [15] albeit without the constrained optimization problem, is introduced:…”

Section: Simulationmentioning

confidence: 99%

Generalization of optimal motion trajectories for bipedal walking

Werner

Trautmann

Lee

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Control of robot locomotion profits from the use of pre-planned trajectories. This paper presents a way to generalize globally optimal and dynamically consistent trajectories for cyclic bipedal walking. A small task-space consisting of stride-length and step time is mapped to spline parameters which fully define the optimal joint space motion. The paper presents the impact of different machine learning algorithms for velocity and torque optimal trajectories with respect to optimality and feasibility. To demonstrate the usefulness of the trajectories, a control approach is presented that allows general walking including transitions between points in the task-space. I. IntroductionAs many aspects of robotics technology improve, robots cover more tasks such as manipulating objects in complex environments. Often designed with humans in mind, these environments feature challenges like stairs or uneven terrain. It is therefore desirable that robots have matching abilities when it comes to locomotion in these environments. This motivates the use of legged locomotion for our robotic platforms.However, legged locomotion comes with rather complex kinematics and dynamics which still provide plenty of challenges for planning and control. All approaches create a mapping between the task of moving the robot somewhere and a suitable trajectory which deals with the full complexity of the robot. Models of varying complexity [1] are used to create this mapping. Creating a trajectory using a model that contains all degrees of freedom and takes into account the systems limitations is however not feasible when the solution should be created on-line, taking into account the task and the environment.The approach developed in this work, first presented in [2], provides this mapping from the task-space to the trajectory. For this it uses a combined approach of trajectory planning using non-linear optimization and generalization of these results by current machine learning methods. The optimization allows exploitation of the dynamics of the system by using strong models. This paper contains an analysis of the performance of a set of machine learning algorithms in providing this mapping for a successful execution of the task.

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Posture and balance control for biped robots based on contact force optimization

Cited by 213 publications

References 19 publications

Trajectory and foothold optimization using low-dimensional models for rough terrain locomotion

Trajectory and foothold optimization using low-dimensional models for rough terrain locomotion

The anatomy of a fall: Automated real-time analysis of raw force sensor data from bipedal walking robots and humans

Generalization of optimal motion trajectories for bipedal walking

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