Magneto-mechanical actuation model for fin-based locomotion

In this paper, we report the results from the analysis of a numerical model used for the design of a magnetic linear actuator with applications to fin-based locomotion. Most of the current robotic fish generate bending motion using rotary motors which implies at least one mechanical conversion of the motion. We seek a solution that directly bends the fin and, at the same time, is able to exploit the magneto-mechanical properties of the fin material. This strong fin-actuator coupling blends the actuator and the body of the robot, allowing cross optimization of the system's elements. We study a simplified model of an elastic element, a spring-mass system representing a flexible fin, subjected to nonlinear forcing, emulating magnetic interaction. The dynamics of the system is studied under unforced and periodic forcing conditions. The analysis is focused on the limit cycles present in the system, which allows the periodic bending of the fin and the generation of thrust. The frequency, maximum amplitude and center of the periodic orbits (offset of the bending) depend directly on the stiffness of the fin and the intensity of the forcing; we use this dependency to sketch a simple parameter controller. Although the model is strongly simplified, it provides means to estimate first values of the parameters for this kind of actuator and it is useful to evaluate the feasibility of minimal actuation control of such systems.Comment: Conference paper, 201

force and motion capture system based on distributed micro accelerometers gyros force and tactile sensing

Motion capture is a powerful tool used in a large range of applications towards human movement analysis. Although it is a well-established technique, its main limitation is the lack of dynamic information such as forces and torques during the motion capture. In this paper, we present a novel approach for human wearable dynamic (WearDY) motion capture for the simultaneous estimation of whole-body forces along with the motion. Our conceptual framework encompasses traditional passive markers based methods, inertial and contact force sensor modalities and harnesses a probabilistic computa- tional framework for estimating dynamic quantities originally proposed in the domain of humanoid robot control. We present experimental analysis of our framework on subjects performing a two degrees-of-freedom bowing task and we estimate the motion and dynamic quantities. The results demonstrate the validity of the proposed method. We discuss the implications of our proposal towards the design of a novel wearable force and motion capture suit and its applications

Proximity and Visuotactile Point Cloud Fusion for Contact Patches in Extreme Deformation

Equipping robots with the sense of touch is critical to emulating the capabilities of humans in real world manipulation tasks. Visuotactile sensors are a popular tactile sensing strategy due to data output compatible with computer vision algorithms and accurate, high resolution estimates of local object geometry. However, these sensors struggle to accommodate high deformations of the sensing surface during object interactions, hindering more informative contact with cm-scale objects frequently encountered in the real world. The soft interfaces of visuotactile sensors are often made of hyperelastic elastomers, which are difficult to simulate quickly and accurately when extremely deformed for tactile information. Additionally, many visuotactile sensors that rely on strict internal light conditions or pattern tracking will fail if the surface is highly deformed. In this work, we propose an algorithm that fuses proximity and visuotactile point clouds for contact patch segmentation that is entirely independent from membrane mechanics. This algorithm exploits the synchronous, high-res proximity and visuotactile modalities enabled by an extremely deformable, selectively transmissive soft membrane, which uses visible light for visuotactile sensing and infrared light for proximity depth. We present the hardware design, membrane fabrication, and evaluation of our contact patch algorithm in low (10%), medium (60%), and high (100%+) membrane strain states. We compare our algorithm against three baselines: proximity-only, tactile-only, and a membrane mechanics model. Our proposed algorithm outperforms all baselines with an average RMSE under 2.8mm of the contact patch geometry across all strain ranges. We demonstrate our contact patch algorithm in four applications: varied stiffness membranes, torque and shear-induced wrinkling, closed loop control for whole body manipulation, and pose estimation

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

Impact of body parameters on dynamic movement primitivesfor robot control

The problem of movement coordination in large DoF (Degree of Freedom) robots is complex due to redundancies. In this regard, Dynamic Movement Primitive (DMP) is a useful planning technique, inspired by biology, that can be used to store and reproduce trajectories about every DoF. This work is a preliminary study that aims to understand and quantify the influence of the robot dynamics upon the performance of DMP in a simulated 2DoF robot arm. The investigation demonstrates that the effect of the robot body dynamics needs to be taken into account during the learning process of the DMP

Do muscle synergies reduce the dimensionality of behavior?

The muscle synergy hypothesis is an archetype of the notion of Dimensionality Reduction (DR) occurring in the central nervous system due to modular organisation. Towards validating this hypothesis, it is however important to understand if muscle synergies can reduce the state-space dimensionality while suitably achieving task control. In this paper we present a scheme for investigating this reduction, utilising the temporal muscle synergy formulation. Our approach is based on the observation that constraining the control input to a weighted combination of temporal muscle synergies instead constrains the dynamic behaviour of a system in trajectory-specific manner. We compute this constrained reformulation of system dynamics and then use the method of system balancing for quantifying the DR; we term this approach as Trajectory Specific Dimensionality Analysis (TSDA). We then use this method to investigate the consequence of minimisation of this dimensionality for a given task. These methods are tested in simulation on a linear (tethered mass) and a nonlinear (compliant kinematic chain) system; dimensionality of various reaching trajectories is compared when using idealised temporal synergies. We show that as a consequence of this Minimum Dimensional Control (MDC) model, smooth straight-line Cartesian trajectories with bell-shaped velocity profiles are obtained as the solution to reaching tasks in both of the test systems. We also investigate the effect on dimensionality due to adding via-points to a trajectory. The results indicate that a synergy basis and trajectory-specific DR of motor behaviours results from usage of muscle synergy control. The implications of these results for the synergy hypothesis, optimal motor control, developmental skill acquisition and robotics are then discussed

Fast trajectory optimization for agile quadrotor maneuvers with a cable-suspended payload

Executing agile quadrotor maneuvers with cable-suspended payloads is a challenging problem and complications induced by the dynamics typically require trajectory optimization. State-of-the-art approaches often need significant computation time and complex parameter tuning. We present a novel dynamical model and a fast trajectory optimization algorithm for quadrotors with a cable-suspended payload. Our first contribution is a new formulation of the suspended payload behavior, modeled as a link attached to the quadrotor with a combination of two revolute joints and a prismatic joint, all being passive. Differently from state of the art, we do not require the use of hybrid modes depending on the cable tension. Our second contribution is a fast trajectory optimization technique for the aforementioned system. Our model enables us to pose the trajectory optimization problem as a Mathematical Program with Complementarity Constraints (MPCC). Desired behaviors of the system (e.g., obstacle avoidance) can easily be formulated within this framework. We show that our approach outperforms the state of the art in terms of computation speed and guarantees feasibility of the trajectory with respect to both the system dynamics and control input saturation, while utilizing far fewer tuning parameters. We experimentally validate our approach on a real quadrotor showing that our method generalizes to a variety of tasks, such as flying through desired waypoints while avoiding obstacles, or throwing the payload toward a desired target. To the best of our knowledge, this is the first time that three-dimensional, agile maneuvers exploiting the system dynamics have been achieved on quadrotors with a cable-suspended payload