Motion Control of the Hybrid Wheeled-Legged Quadruped Robot Centauro

Emerging applications will demand robots to deal with a complex environment, which lacks the structure and predictability of the industrial workspace. Complex scenarios will require robot complexity to increase as well, as compared to classical topologies such as fixed-base manipulators, wheeled mobile platforms, tracked vehicles, and their combinations. Legged robots, such as humanoids and quadrupeds, promise to provide platforms which are flexible enough to handle real world scenarios; however, the improved flexibility comes at the cost of way higher control complexity. As a trade-off, hybrid wheeled-legged robots have been proposed, resulting in the mitigation of control complexity whenever the ground surface is suitable for driving. Following this idea, a new hybrid robot called Centauro has been developed inside the Humanoid and Human Centered Mechatronics lab at Istituto Italiano di Tecnologia (IIT). Centauro is a wheeled-legged quadruped with a humanoid bi-manual upper-body. Differently from other platform of similar concept, Centauro employs customized actuation units, which provide high torque outputs, moderately fast motions, and the possibility to control the exerted torque. Moreover, with more than forty motors moving its limbs, Centauro is a very redundant platform, with the potential to execute many different tasks at the same time. This thesis deals with the design and development of a software architecture, and a control system, tailored to such a robot; both wheeled and legged locomotion strategies have been studied, as well as prioritized, whole-body and interaction controllers exploiting the robot torque control capabilities, and capable to handle the system redundancy. A novel software architecture, made of (i) a real-time robotic middleware, and (ii) a framework for online, prioritized Cartesian controller, forms the basis of the entire work

Ein hierarchisches Framework für physikalische Mensch-Roboter-Interaktion

Robots are becoming more than machines performing repetitive tasks behind safety fences, and are expected to perform multiple complex tasks and work together with a human. For that purpose, modern robots are commonly built with two main characteristics: a large number of joints to increase their versatility and the capability to feel the environment through torque/force sensors. Controlling such complex robots requires the development of sophisticated frameworks capable of handling multiple tasks. Various frameworks have been proposed in the last years to deal with the redundancy caused by a large number of joints. Those hierarchical frameworks prioritize the achievement of the main task with the whole robot capability, while secondary tasks are performed as well as the remaining mobility allows it. This methodology presents considerable drawbacks in applications requiring that the robot respects constraints imposed by, e.g., safety restrictions or physical limitations. One particular case is unilateral constraints imposed by, e.g., joint or workspace limits. To implement them, task hierarchical frameworks rely on the activation of repulsive potential fields when approaching the limit. The performance of the potential field depends on the configuration and speed of the robot. Additionally, speed limitation is commonly required in collaborative scenarios, but it has been insufficiently treated for torque-controlled robots. With the aim of controlling redundant robots in collaborative scenarios, this thesis proposes a framework that handles multiple tasks under multiple constraints. The robot’s reaction to physical interaction must be intuitive and safe for humans: The robot must not impose high forces on the human or react unexpectedly to external forces. The proposed framework uses novel methods to avoid exceeding position, velocity and acceleration limits in joint and Cartesian spaces. A comparative study is carried out between different redundancy resolution solvers to contrast the diverse approaches used to solve the redundancy problem. Widely used projector-based and quadratic programming-based hierarchical solvers were experimentally analyzed when reacting to external forces. Experiments were performed using an industrial redundant collaborative robot. Results demonstrate that the proposed method to handle unilateral constraints produces a safe and expected reaction in the presence of external forces exerted by humans. The robot does not exceed the given limits, while the tasks performed are prioritized hierarchically

An adaptive compliance Hierarchical Quadratic Programming controller for ergonomic human–robot collaboration

This paper proposes a novel Augmented Hierarchical Quadratic Programming (AHQP) framework for multi-tasking control in Human-Robot Collaboration (HRC) which integrates human-related parameters to optimize ergonomics. The aim is to combine parameters that are typical of both industrial applications (e.g. cycle times, productivity) and human comfort (e.g. ergonomics, preference), to identify an optimal trade-off. The augmentation aspect avoids the dependency from a fixed end-effector reference trajectory, which becomes part of the optimization variables and can be used to define a feasible workspace region in which physical interaction can occur. We then demonstrate that the integration of the proposed AHQP in HRC permits the addition of human ergonomics and preference. To achieve this, we develop a human ergonomics function based on the mapping of an ergonomics score, compatible with AHQP formulation. This allows to identify at control level the optimal Cartesian pose that satisfies the active objectives and constraints, that are now linked to human ergonomics. In addition, we build an adaptive compliance framework that integrates both aspects of human preferences and intentions, which are finally tested in several collaborative experiments using the redundant MOCA robot. Overall, we achieve improved human ergonomics and health conditions, aiming at the potential reduction of work-related musculoskeletal disorders

Bounded haptic teleoperation of a quadruped robot’s foot posture for sensing and manipulation

This paper presents a control framework to teleoperate a quadruped robot's foot for operator-guided haptic exploration of the environment. Since one leg of a quadruped robot typically only has 3 actuated degrees of freedom (DoFs), the torso is employed to assist foot posture control via a hierarchical whole-body controller. The foot and torso postures are controlled by two analytical Cartesian impedance controllers cascaded by a null space projector. The contact forces acting on supporting feet are optimized by quadratic programming (QP). The foot's Cartesian impedance controller may also estimate contact forces from trajectory tracking errors, and relay the force-feedback to the operator. A 7D haptic joystick, Sigma.7, transmits motion commands to the quadruped robot ANYmal, and renders the force feedback. Furthermore, the joystick's motion is bounded by mapping the foot's feasible force polytope constrained by the friction cones and torque limits in order to prevent the operator from driving the robot to slipping or falling over. Experimental results demonstrate the efficiency of the proposed framework.Comment: Under review. Video Available at https://www.youtube.com/watch?v=htI8202vfe

Stable Torque Optimization for Redundant Robots Using a Short Preview

We consider the known phenomenon of torque oscillations and motion instabilities that occur in redundant robots during the execution of sufficiently long Cartesian trajectories when the joint torque is instantaneously minimized. In the framework of online local redundancy resolution methods, we propose basic variations of the minimum torque scheme to address this issue. Either the joint torque norm is minimized over two successive discrete-time samples using a short preview window, or we minimize the norm of the difference with respect to a desired momentum-damping joint torque, or the two schemes are combined together. The resulting local control methods are all formulated as well-posed linear quadratic problems, and their closed-form solutions also generate low joint velocities while addressing the primary torque optimization objectives. Stable and consistent behaviors are obtained along short or long Cartesian position trajectories, as illustrated with simulations on a 3R planar arm and with experiments on a 7R KUKA LWR robot