This paper proposes a novel design strategy and task‐priority‐based control methodology for a robot to successfully complete a rescue operation in an extremely unstructured environment. The ...mechanical structure is designed to obtain both versatile manipulability and all‐terrain mobility. The regularized hierarchical quadratic program is used for whole‐body motion and force control. The optimization strategy is reasoning about regularization and thus it ensures convergence of the solution in the face of singularities while taking into account equality and inequality constraints. We demonstrate the effectiveness of the online optimization‐based control algorithms through extensive real‐world numerical and experimental results. Finally, we highlight that the rescue robot can successfully execute missions to extract a casualty and dispose of a dangerous object both indoor and outdoor environments.
Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning ...method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitude planner ensures the robot stability by imposing limits to the angular acceleration. Our whole-body controller tracks compliantly trunk motions while avoiding slippage, as well as kinematic and torque limits. Despite the use of a simplified model, which is restricted to flat terrain, our approach shows remarkable capability to deal with a wide range of noncoplanar terrains. The results are validated by experimental trials and comparative evaluations in a series of terrains of progressively increasing complexity.
Quadruped robots working in jungles, mountains or factories should be able to move through challenging scenarios. In this paper, we present a control framework for quadruped robots walking over rough ...terrain. The planner plans the trajectory of the robot's center of gravity by using the normalized energy stability criterion, which ensures that the robot is in the most stable state. A contact detection algorithm based on the probabilistic contact model is presented, which implements event‐based state switching of the quadruped robot legs. And an on‐line detection of contact force based on generalized momentum is also showed, which improves the accuracy of proprioceptive force estimation. A controller combining whole body control and virtual model control is proposed to achieve precise trajectory tracking and active compliance with environment interaction. Without any knowledge of the environment, the experiments of the quadruped robot SDUQuad‐144 climbs over significant obstacles such as 38 cm high steps and 22.5 cm high stairs are designed to verify the feasibility of the proposed method.
•A momentum-based observer for quadruped robots is proposed.•The observer estimates forces acting on both stance and swing legs.•Whole-body controller with a quadratic problem is used.•Disturbances ...on swing legs are compensated using operational space.•The observer is compared with two from the state-of-the-art.
This paper presents an estimator of external disturbances for legged robots, based on the system’s momentum. The estimator, along with a suitable motion planner for the trajectory of the robot’s center of mass and an optimization problem based on the modulation of ground reaction forces, devises a whole-body controller for the robot. The designed solution is tested on a quadruped robot within a dynamic simulation environment. The quadruped is stressed by external disturbances acting on stance and swing legs indifferently. The proposed approach is also evaluated through a comparison with two state-of-the-art solutions.
This work presents a new control approach to multi-contact balancing for
torque-controlled humanoid robots. The controller includes a non-strict task hierarchy,
which allows the robot to use a subset ...of its end effectors for balancing while the
remaining ones can be used for interacting with the environment. The controller creates a
passive and compliant behavior for regulating the center of mass (CoM) location, hip
orientation and the poses of each end effector assigned to the interaction task. This is
achieved by applying a suitable wrench (force and torque) at each one of the end effectors
used for interaction. The contact wrenches at the balancing end effectors are chosen such
that the sum of the balancing and interaction wrenches produce the desired wrench at the
CoM. The algorithm requires the solution of an optimization problem, which distributes the
CoM wrench to the end effectors taking into account constraints for unilaterality,
friction and position of the center of pressure. Furthermore, the feedback controller is
combined with a feedforward control in order to improve performance while tracking a
predefined trajectory, leading to a control structure similar to a PD+ control. The
controller is evaluated in several experiments with the humanoid robot TORO.
Model-based control for robots has increasingly depended on optimization-based methods, such as differential dynamic programming (DDP) and iterative LQR (iLQR). These methods can form the basis of ...model-predictive control, which is commonly used for controlling legged robots. Computing the partial derivatives of the robot dynamics is often the most expensive part of these algorithms, regardless of whether analytical methods, finite difference, automatic differentiation (AD), or chain-rule accumulation is used. Since the second-order derivatives of the robot dynamics result in tensor computations, they are often ignored, leading to the use of iLQR, instead of the full second-order DDP method. In this article, we present analytical methods to compute the second-order derivatives of inverse and forward dynamics for open-chain rigid-body systems with multi-DoF joints and fixed/floating bases. An extensive comparison of accuracy and run-time performance with AD and other methods is provided, including the consideration of code-generation techniques in C/C++ to speed up the computations. For the 36 DoF ATLAS humanoid, the second-order inverse and forward dynamics derivatives take <inline-formula><tex-math notation="LaTeX">\approx 200 \,\mu \text{s}</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">\approx \text{2.1}\,\text{ms}</tex-math></inline-formula>, respectively, on a 12th Gen Intel i5-12400 processor with 2.5 GHz clock-speed, resulting in a <inline-formula><tex-math notation="LaTeX">\approx 3.2 \times</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">\approx 3.8 \times</tex-math></inline-formula> speedup, respectively, over the AD approach.
Research into legged robotics is primarily motivated by the prospects of building machines that are able to navigate in challenging and complex environments that are predominantly non-flat. In this ...context, control of contact forces is fundamental to ensure stable contacts and equilibrium of the robot. In this paper we propose a planning/control framework for quasi-static walking of quadrupedal robots, implemented for a demanding application in which regulation of ground reaction forces is crucial. Experimental results demonstrate that our 75-kg quadruped robot is able to walk inside two high-slope (
50
∘
) V-shaped walls; an achievement that to the authors’ best knowledge has never been presented before. The robot distributes its weight among the stance legs so as to optimize user-defined criteria. We compute joint torques that result in no foot slippage, fulfillment of the unilateral constraints of the contact forces and minimization of the actuators effort. The presented study is an experimental validation of the effectiveness and robustness of QP-based force distributions methods for quasi-static locomotion on challenging terrain.
The flexible joint robot has superior attractive features because of its high mobility, high load ratiohigh torque fidelity, robustness for external disturbance, task adaptability, and safety. In ...this paper, an autonomous mobile manipulator driven by the designed series elastic actuators (SEAs) is developed. A complete dynamic model of nonholonomic mobile manipulator including a mobile platform and manipulator with joint flexibility operating simultaneously is proposed. An integrated whole body trajectory control framework is proposed for such robot to perform mobile manipulation tasks. Considering the nonholonomic and holonomic constraints in the mobile manipulation, the whole-body dynamics is formulated and reduced. To address the highly nonlinear of the dynamics and model uncertainty, a novel integral Lyapunov function (ILF)-based adaptive neural network control for task tracking under uncertainties of the flexible joint robot model is proposed. Compared with existing methods, the proposed method provides an alternative for controlling flexible joint robots. The feasibility of the proposed method is verified by the extensive trajectory tracking experiment results in our developed flexible joint manipulator.
Hierarchical inverse dynamics based on cascades of quadratic programs have been proposed for the control of legged robots. They have important benefits but to the best of our knowledge have never ...been implemented on a torque controlled humanoid where model inaccuracies, sensor noise and real-time computation requirements can be problematic. Using a reformulation of existing algorithms, we propose a simplification of the problem that allows to achieve real-time control. Momentum-based control is integrated in the task hierarchy and a LQR design approach is used to compute the desired associated closed-loop behavior and improve performance. Extensive experiments on various balancing and tracking tasks show very robust performance in the face of unknown disturbances, even when the humanoid is standing on one foot. Our results demonstrate that hierarchical inverse dynamics together with momentum control can be efficiently used for feedback control under real robot conditions.
The control of a robot in its task space is a standard approach nowadays. If the system is kinematically redundant with respect to this goal, one can even execute additional subtasks simultaneously. ...By utilizing null space projections, for example, the whole stack of tasks can be implemented within a strict task hierarchy following the order of priority. One of the most common methods to track multiple task-space trajectories at the same time is to feedback-linearize the system and dynamically decouple all involved subtasks, which finally yields the exponential stability of the desired equilibrium. In this article, we provide a hierarchical multi-objective controller for trajectory tracking that ensures both asymptotic stability of the equilibrium and a desired contact impedance at the same time. In contrast to the state of the art in prioritized multi-objective control, feedback of the external forces can be avoided and the natural inertia of the robot is preserved. The controller is evaluated in simulations and on a standard lightweight robot with torque interface. The approach is predestined for precise trajectory tracking where dedicated and robust physical-interaction compliance is crucial at the same time.
PDF
