Abstract:We present a motion planning and control framework for ALMA, a torque-controlled quadrupedal robot equipped with a six degrees of freedom robotic arm capable of performing dynamic locomotion while executing manipulation tasks. The online motion planning framework, together with a whole-body controller based on a hierarchical optimization algorithm, enable the system to walk, trot and pace while executing tasks such as fixed-position end-effector control, reactive human-robot collaboration and torso posture opt… Show more
“…Typically, whole-body control is associated with torquecontrolled robots and there is rich literature covering the topic [19], [20], [2], [8], [9]. However, many existing machines do not feature actuators for high accuracy torque control (e.g.…”
Section: A Related Workmentioning
confidence: 99%
“…Hence, the base tracking task is split into terrain adaptive posture tracking (roll, pitch and height that are influenced by FE joints) and 2D pose tracking (x, y and yaw influenced by Abduction/Adduction (AA) and steer joints). Exploiting the hierarchical task setup to achieve posture adaptation has been reported in [19]. We exploit hierarchical task setup to realize the terrain adaptive behavior.…”
This article presents a planning and control pipeline for legged-wheeled (hybrid) machines. It consists of a Trajectory Optimization based planner that computes references for end-effectors and joints. The references are tracked using a whole-body controller based on a hierarchical optimization approach. Our controller is capable of performing terrain adaptive whole-body control. Furthermore, it computes both torque and position/velocity references, depending on the actuator capabilities. We perform experiments on a Menzi Muck M545, a full size 31 Degrees of Freedom (DoF) walking excavator with five limbs: four wheeled legs and an arm. We show motions that require full-body coordination executed in realistic conditions. To the best of our knowledge, this is the first work that shows the execution of whole-body motions on a full size walking excavator, using all DoFs for locomotion.
“…Typically, whole-body control is associated with torquecontrolled robots and there is rich literature covering the topic [19], [20], [2], [8], [9]. However, many existing machines do not feature actuators for high accuracy torque control (e.g.…”
Section: A Related Workmentioning
confidence: 99%
“…Hence, the base tracking task is split into terrain adaptive posture tracking (roll, pitch and height that are influenced by FE joints) and 2D pose tracking (x, y and yaw influenced by Abduction/Adduction (AA) and steer joints). Exploiting the hierarchical task setup to achieve posture adaptation has been reported in [19]. We exploit hierarchical task setup to realize the terrain adaptive behavior.…”
This article presents a planning and control pipeline for legged-wheeled (hybrid) machines. It consists of a Trajectory Optimization based planner that computes references for end-effectors and joints. The references are tracked using a whole-body controller based on a hierarchical optimization approach. Our controller is capable of performing terrain adaptive whole-body control. Furthermore, it computes both torque and position/velocity references, depending on the actuator capabilities. We perform experiments on a Menzi Muck M545, a full size 31 Degrees of Freedom (DoF) walking excavator with five limbs: four wheeled legs and an arm. We show motions that require full-body coordination executed in realistic conditions. To the best of our knowledge, this is the first work that shows the execution of whole-body motions on a full size walking excavator, using all DoFs for locomotion.
“…In order to save energy, a third QP has to be added to minimize torque commands. Usually, those are the three fundamental QPs required by HQP controllers (Bellicoso et al, 2019 ). For walking gaits, if we separate swing legs and torso into different prioritized tasks, one more QP has to be solved.…”
Quadruped robots require compliance to handle unexpected external forces, such as impulsive contact forces from rough terrain, or from physical human-robot interaction. This paper presents a locomotion controller using Cartesian impedance control to coordinate tracking performance and desired compliance, along with Quadratic Programming (QP) to satisfy friction cone constraints, unilateral constraints, and torque limits. First, we resort to projected inverse-dynamics to derive an analytical control law of Cartesian impedance control for constrained and underactuated systems (typically a quadruped robot). Second, we formulate a QP to compute the optimal torques that are as close as possible to the desired values resulting from Cartesian impedance control while satisfying all of the physical constraints. When the desired motion torques lead to violation of physical constraints, the QP will result in a trade-off solution that sacrifices motion performance to ensure physical constraints. The proposed algorithm gives us more insight into the system that benefits from an analytical derivation and more efficient computation compared to hierarchical QP (HQP) controllers that typically require a solution of three QPs or more. Experiments applied on the ANYmal robot with various challenging terrains show the efficiency and performance of our controller.
“…So far, the most remarkable robot capable of performing dynamic coordination of locomotion, manipulation and balancing on a statically unstable platform is Handle by Boston Dynamics [17], whose controller is not described in any publications. In [4], manipulation is performed with an arm on a quadrupedal robot. However, contact forces are considered as disturbances in the balancing planner.…”
Section: A Related Workmentioning
confidence: 99%
“…In the robotics literature, there are works that have treated the manipulation problem for systems balancing on unstable platforms (e.g., [4], [5], [6]). However, current approaches do not take into account the full non-linear system dynamics in the planner.…”
Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a wholebody optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening.
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