Reaction Null Space of a multibody system with applications in robotics

This paper provides an overview of implementation examples based on the Reaction Null Space formalism, developed initially to tackle the problem of satellite-base disturbance of a free-floating space robot, when the robot arm is activated. The method has been applied throughout the years to other unfixed-base systems, e.g. flexible-base and macro/mini robot systems, as well as to the balance control problem of humanoid robots. The paper also includes most recent results about complete dynamical decoupling of the end-link of a fixed-base robot, wherein the end-link is regarded as the unfixed-base. This interpretation is shown to be useful with regard to motion/force control scenarios. Respective implementation results are provided

Benchmarking Dynamic Balancing Controllers for Humanoid Robots

This paper presents a comparison study of three control design approaches for humanoid balancing based on the Center of Mass (CoM) stabilization and body posture adjustment. The comparison was carried out under controlled circumstances allowing other researchers to replicate and compare our results with their own. The feedback control from state space design is based on simple models and provides sufficient robustness to control complex and high Degrees of Freedom (DoFs) systems, such as humanoids. The implemented strategies allow compliant behavior of the robot in reaction to impulsive or periodical disturbances, resulting in a smooth and human-like response while considering constraints. In this respect, we implemented two balancing strategies to compensate for the CoM deviation. The first one uses the robot’s capture point as a stability principle and the second one uses the Force/Torque sensors at the ankles to define a CoM reference that stabilizes the robot. In addition, was implemented a third strategy based on upper body orientation to absorb external disturbances and counterbalance them. Even though the balancing strategies are implemented independently, they can be merged to further increase balancing performance. The proposed strategies were previously applied on different humanoid bipedal platforms, however, their performance could not be properly benchmarked before. With this concern, this paper focuses on benchmarking in controlled scenarios to help the community in comparing different balance techniques. The key performance indicators (KPIs) used in our comparison are the CoM deviation, the settling time, the maximum measured orientation, passive gait measure, measured ankles torques, and reconstructed Center of Pressure (CoP). The benchmarking experiments were carried out in simulations and using the facility at Istituto Italiano di Tecnologia on the REEM-C humanoid robot provided by PAL robotics inside the EU H2020 project EUROBENCH framework

Tasakaalu rehabilitatsioon: üldprintsiibid ja metoodika

http://tartu.ester.ee/record=b2655087~S1*es

Can a Power Training Program Reduce Fall Risk Factors in Parkinson\u27s Disease?

Introduction: Frequent falls in Parkinson’s disease (PD) are likely partially due to impaired muscle function in PD (i.e. greater coactivation and decreased magnitude of activation in agonists) compared to older adults without PD. Reduced muscle strength and power (ability to generate force rapidly) are also risk factors and are likely occurring due to deficits in muscle parameters. Muscle parameters include: i) the amount of coactivation of antagonist muscles; ii) latency to onset of activation in agonist and antagonist muscles and; iii) the magnitude of activation of agonist and antagonist muscles. Rehabilitation should aim to improve impaired muscle parameters to reduce fall risk in PD. Therefore, two experiments were designed to address this gap in PD literature. Experiment one aimed to identify specific muscle parameters distinguishing fall status in PD, thus providing parameters that can be used to identify if a rehabilitation will be effective in reducing fall risk. Experiment two investigated whether power training (PWR) was more effective than strength training (ST) or a non-exercise control group (CTRL) at improving muscle parameters distinguishing fallers in experiment one. Methods: Experiment one - Forty-six individuals with PD were categorized based on fall status. A fall-like situation (lean and release) was used and electromyography (EMG) data was collected from muscles in both legs (stepping and stance leg): tibialis anterior (TA), lateral gastrocnemius (LG), biceps (BF) and rectus femoris (RF). Results: A Receiver Operating Characteristic (ROC) curve identified fallers vs. non-fallers by EMG measures in the stepping leg; an increased onset latency of LG and a greater TA activation. As well, in the stance limb, an increased coactivation of TA and a larger TA activation identified fallers. Experiment two- Forty-four individuals with PD were randomized to PWR or ST groups, and seventeen individuals with PD volunteered for the CTRL group. Training occurred twice weekly for 12-weeks, where PWR completed the concentric part of the movements rapidly. All groups completed the fall situation (at baseline, one to two weeks prior to the intervention, and one to two weeks after the intervention was complete) while muscle parameters were measured along with muscle strength and muscle power, disease severity and a weekly falls diaries. Results: No differences in muscle parameters were present at post-testing between groups. However, PWR and ST significantly improved muscle strength, and components of muscle power compared to CTRL. Disease severity was improved in PWR at post-testing. Conclusion: Muscle parameters distinguishing PD fallers were identified. As well, PWR and ST improved aspects of risk factors for falls similarly, providing two feasible rehabilitation strategies for PD

Dynamic Balance and Gait Metrics for Robotic Bipeds

For legged robots to be useful in the real world, they must be able to balance and walk reliably. Both of these abilities improve when a system is more effective at moving itself around relative to its contacts (i.e., its feet). Achieving this type of movement depends both on the controller used to perform the motion and the physical properties of the system. Although much work has been done on the development of dynamic controllers for balance and gait, only limited research exists on how to quantify a system’s physical balance capabilities or how to modify the system to improve those capabilities. From the control perspective, there are three strategies for maintaining balance in bipeds: flexing, leaning, and stepping. Both stepping and leaning strategies typically depend on balance points (critical points used for maintaining or regaining balance) to determine whether or not a step is needed, and if so, where to step. Although several balance point estimators exist, the majority of these methods make undesirable assumptions (e.g., ignoring the impact dynamics, assuming massless legs, planar motion, etc.). From the physical design perspective, one promising approach for analyzing system performance is a set of dynamic ratios called velocity and momentum gains, which are dependent only on the (scale-invariant) dynamic parameters and instantaneous configuration of a system, enabling entire classes of mechanisms to be analyzed at the same time. This thesis makes four key contributions towards improving biped balancing capabilities. First, a dynamic bipedal controller is proposed which uses a 3D balance point estimator both to respond to disturbances and produce reliable stepping. Second, a novel balance point estimator is proposed that facilitates stepping while combining and expanding the features of existing 2D and 3D estimators to produce a generalized 3D formulation. Third, the momentum gain formulation is extended to general 2D and 3D systems, then both gains are compared to centroidal momentum via a spatial formulation and incorporated into a generalized gain definition. Finally, the gains are used as a metric in an optimization framework to design parameterized balancing mechanisms within a given configuration space. Effectively, this enables an optimization of how well a system could balance without the need to pre-specify or co-generate controllers and/or trajectories. To validate the control contributions, simulated bipeds are subjected to external disturbances while standing still and walking. For the gain contributions, the framework is used to compare gain-optimized mechanisms to those based on the cost of transport metric. Through the combination of gain-based physical design optimization and the use of predictive, real-time balance point estimators within dynamic controllers, bipeds and other legged systems will soon be able to achieve reliable balance and gait in the real world

Bio-inspired robotic control in underactuation: principles for energy efficacy, dynamic compliance interactions and adaptability.

Biological systems achieve energy efficient and adaptive behaviours through extensive autologous and exogenous compliant interactions. Active dynamic compliances are created and enhanced from musculoskeletal system (joint-space) to external environment (task-space) amongst the underactuated motions. Underactuated systems with viscoelastic property are similar to these biological systems, in that their self-organisation and overall tasks must be achieved by coordinating the subsystems and dynamically interacting with the environment. One important question to raise is: How can we design control systems to achieve efficient locomotion, while adapt to dynamic conditions as the living systems do? In this thesis, a trajectory planning algorithm is developed for underactuated microrobotic systems with bio-inspired self-propulsion and viscoelastic property to achieve synchronized motion in an energy efficient, adaptive and analysable manner. The geometry of the state space of the systems is explicitly utilized, such that a synchronization of the generalized coordinates is achieved in terms of geometric relations along the desired motion trajectory. As a result, the internal dynamics complexity is sufficiently reduced, the dynamic couplings are explicitly characterised, and then the underactuated dynamics are projected onto a hyper-manifold. Following such a reduction and characterization, we arrive at mappings of system compliance and integrable second-order dynamics with the passive degrees of freedom. As such, the issue of trajectory planning is converted into convenient nonlinear geometric analysis and optimal trajectory parameterization. Solutions of the reduced dynamics and the geometric relations can be obtained through an optimal motion trajectory generator. Theoretical background of the proposed approach is presented with rigorous analysis and developed in detail for a particular example. Experimental studies are conducted to verify the effectiveness of the proposed method. Towards compliance interactions with the environment, accurate modelling or prediction of nonlinear friction forces is a nontrivial whilst challenging task. Frictional instabilities are typically required to be eliminated or compensated through efficiently designed controllers. In this work, a prediction and analysis framework is designed for the self-propelled vibro-driven system, whose locomotion greatly relies on the dynamic interactions with the nonlinear frictions. This thesis proposes a combined physics-based and analytical-based approach, in a manner that non-reversible characteristic for static friction, presliding as well as pure sliding regimes are revealed, and the frictional limit boundaries are identified. Nonlinear dynamic analysis and simulation results demonstrate good captions of experimentally observed frictional characteristics, quenching of friction-induced vibrations and satisfaction of energy requirements. The thesis also performs elaborative studies on trajectory tracking. Control schemes are designed and extended for a class of underactuated systems with concrete considerations on uncertainties and disturbances. They include a collocated partial feedback control scheme, and an adaptive variable structure control scheme with an elaborately designed auxiliary control variable. Generically, adaptive control schemes using neural networks are designed to ensure trajectory tracking. Theoretical background of these methods is presented with rigorous analysis and developed in detail for particular examples. The schemes promote the utilization of linear filters in the control input to improve the system robustness. Asymptotic stability and convergence of time-varying reference trajectories for the system dynamics are shown by means of Lyapunov synthesis