Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization

A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable joint-space trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint space-to-task space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous control-based approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint space-to-task space motion mappings in various scenarios. By representing the manipulator trajectory as a continuous-time Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations.Comment: In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS'19), Macau, China, Nov. 4-8, 201

Motion Planning for the On-orbit Grasping of a Non-cooperative Target Satellite with Collision Avoidance

A method for grasping a tumbling noncooperative target is presented, which is based on nonlinear optimization and collision avoidance. Motion constraints on the robot joints as well as on the end-effector forces are considered. Cost functions of interest address the robustness of the planned solutions during the tracking phase as well as actuation energy. The method is applied in simulation to different operational scenarios

Planning and Control Strategies for Motion and Interaction of the Humanoid Robot COMAN+

Despite the majority of robotic platforms are still confined in controlled environments such as factories, thanks to the ever-increasing level of autonomy and the progress on human-robot interaction, robots are starting to be employed for different operations, expanding their focus from uniquely industrial to more diversified scenarios. Humanoid research seeks to obtain the versatility and dexterity of robots capable of mimicking human motion in any environment. With the aim of operating side-to-side with humans, they should be able to carry out complex tasks without posing a threat during operations. In this regard, locomotion, physical interaction with the environment and safety are three essential skills to develop for a biped. Concerning the higher behavioural level of a humanoid, this thesis addresses both ad-hoc movements generated for specific physical interaction tasks and cyclic movements for locomotion. While belonging to the same category and sharing some of the theoretical obstacles, these actions require different approaches: a general high-level task is composed of specific movements that depend on the environment and the nature of the task itself, while regular locomotion involves the generation of periodic trajectories of the limbs. Separate planning and control architectures targeting these aspects of biped motion are designed and developed both from a theoretical and a practical standpoint, demonstrating their efficacy on the new humanoid robot COMAN+, built at Istituto Italiano di Tecnologia. The problem of interaction has been tackled by mimicking the intrinsic elasticity of human muscles, integrating active compliant controllers. However, while state-of-the-art robots may be endowed with compliant architectures, not many can withstand potential system failures that could compromise the safety of a human interacting with the robot. This thesis proposes an implementation of such low-level controller that guarantees a fail-safe behaviour, removing the threat that a humanoid robot could pose if a system failure occurred

Modeling & control of a space robot for active debris removal

Space access and satellites lifespan are increasingly threatened by the great amount of debris in Low Earth Orbits (LEO). Among the many solutions proposed in the literature so far, the emphasis is put here on a robotic arm mounted on a satellite to capture massive debris, such as dead satellites or launch vehicle upper stages. The modeling and control of such systems are investigated throughout the paper. Dynamic models rely on an adapted Newton-Euler algorithm, and control algorithms are based on the recent structured H infinity method. The main goal is to efficiently track a target point on the debris while using simple PD-like controllers to reduce computational burden. The structured H infinity framework proves to be a suitable tool to design a reduced-order robust controller that catches up with external disturbances and is simultaneously compatible with current space processors capacities

Tasks prioritization for whole-body realtime imitation of human motion by humanoid robots

International audienceThis paper deals with on-line motion imitation of a human being by a humanoid robot using inverse kinematics (IK). First, the human observed trajectories are scaled in order to match the robot geometric and kinematic description. Second, a task prioritization process is defined using both equality and minimized constraints in the robot IK model, with four tasks: balance management, end-effectors tracking, joint limits avoidance and staying close to the human joint trajectories. The method was validated using the humanoid robot NAO

Learning Singularity Avoidance

With the increase in complexity of robotic systems and the rise in non-expert users, it can be assumed that task constraints are not explicitly known. In tasks where avoiding singularity is critical to its success, this paper provides an approach, especially for non-expert users, for the system to learn the constraints contained in a set of demonstrations, such that they can be used to optimise an autonomous controller to avoid singularity, without having to explicitly know the task constraints. The proposed approach avoids singularity, and thereby unpredictable behaviour when carrying out a task, by maximising the learnt manipulability throughout the motion of the constrained system, and is not limited to kinematic systems. Its benefits are demonstrated through comparisons with other control policies which show that the constrained manipulability of a system learnt through demonstration can be used to avoid singularities in cases where these other policies would fail. In the absence of the systems manipulability subject to a tasks constraints, the proposed approach can be used instead to infer these with results showing errors less than 10^-5 in 3DOF simulated systems as well as 10^-2 using a 7DOF real world robotic system

Singularity Maps of Space Robots and their Application to Gradient-based Trajectory Planning

We present a numerical method to compute singularity sets in the configuration space of free-floating robots, comparing two different criteria based on formal methods. By exploiting specific properties of free-floating systems and an alternative formulation of the generalized Jacobian, the search space and computational complexity of the algorithm is reduced. It is shown that the resulting singularity maps can be applied in the context of trajectory planning to guarantee feasibility with respect to singularity avoidance. The proposed approach is validated on a space robot composed of a six degrees-of-freedom (DOF) arm mounted on a body with six DOF

Methods to improve the coping capacities of whole-body controllers for humanoid robots

Current applications for humanoid robotics require autonomy in an environment specifically adapted to humans, and safe coexistence with people. Whole-body control is promising in this sense, having shown to successfully achieve locomotion and manipulation tasks. However, robustness remains an issue: whole-body controllers can still hardly cope with unexpected disturbances, with changes in working conditions, or with performing a variety of tasks, without human intervention. In this thesis, we explore how whole-body control approaches can be designed to address these issues. Based on whole-body control, contributions have been developed along three main axes: joint limit avoidance, automatic parameter tuning, and generalizing whole-body motions achieved by a controller. We first establish a whole-body torque-controller for the iCub, based on the stack-of-tasks approach and proposed feedback control laws in SE(3). From there, we develop a novel, theoretically guaranteed joint limit avoidance technique for torque-control, through a parametrization of the feasible joint space. This technique allows the robot to remain compliant, while resisting external perturbations that push joints closer to their limits, as demonstrated with experiments in simulation and with the real robot. Then, we focus on the issue of automatically tuning parameters of the controller, in order to improve its behavior across different situations. We show that our approach for learning task priorities, combining domain randomization and carefully selected fitness functions, allows the successful transfer of results between platforms subjected to different working conditions. Following these results, we then propose a controller which allows for generic, complex whole-body motions through real-time teleoperation. This approach is notably verified on the robot to follow generic movements of the teleoperator while in double support, as well as to follow the teleoperator\u2019s upper-body movements while walking with footsteps adapted from the teleoperator\u2019s footsteps. The approaches proposed in this thesis therefore improve the capability of whole-body controllers to cope with external disturbances, different working conditions and generic whole-body motions

Inverse kinematics of a 5-axis hybrid robot with non-singular tool path generation

This paper deals with non-singular tool path generation of a 5-axis hybrid robot named TriMule, which is designed for large part machining in situ. It is observed that at a singularity pose sudden changes occur in rotation of the C-axis and lengths of three telescopic legs. It is found that when the tool axis rotates about the axis normal to the plane expanded by the tool axis and the singular axis, the singular axis itself is forced to rotate simultaneously about the same axis in the opposite direction. This exploration enables the minimum rotation angle of the tool axis to be determined accurately for avoiding singularity and reducing machined surface errors caused by tool axis modification, leading to the development of an algorithm for non-singular tool path generation by modifying a partial set of the control points of B-splines. Both simulation and experiment on a prototype machine are carried out to verify the effectiveness of this approach

Robotic Manipulation and Capture in Space: A Survey

Space exploration and exploitation depend on the development of on-orbit robotic capabilities for tasks such as servicing of satellites, removing of orbital debris, or construction and maintenance of orbital assets. Manipulation and capture of objects on-orbit are key enablers for these capabilities. This survey addresses fundamental aspects of manipulation and capture, such as the dynamics of space manipulator systems (SMS), i.e., satellites equipped with manipulators, the contact dynamics between manipulator grippers/payloads and targets, and the methods for identifying properties of SMSs and their targets. Also, it presents recent work of sensing pose and system states, of motion planning for capturing a target, and of feedback control methods for SMS during motion or interaction tasks. Finally, the paper reviews major ground testing testbeds for capture operations, and several notable missions and technologies developed for capture of targets on-orbit