Using Buckingham's $π$ Theorem for Multi-System Learning Transfer: a Case-study with 3 Vehicles Sharing a Database

Learning schemes for planning and control are limited by the difficulty of collecting large amounts of experimental data or having to rely on high-fidelity simulations. This paper explores the potential of a proposed learning scheme that leverages dimensionless numbers based on Buckingham's $\pi$ theorem to improve data efficiency and facilitate knowledge sharing between similar systems. A case study using car-like robots compares traditional and dimensionless learning models on simulated and experimental data to validate the benefits of the new dimensionless learning approach. Preliminary results show that this new dimensionless approach could accelerate the learning rate and improve the accuracy of the model and should be investigated further.

Assessing Wind Impact on Semi-Autonomous Drone Landings for In-Contact Power Line Inspection

In recent years, the use of inspection drones has become increasingly popular for high-voltage electric cable inspections due to their efficiency, cost-effectiveness, and ability to access hard-to-reach areas. However, safely landing drones on power lines, especially under windy conditions, remains a significant challenge. This study introduces a semi-autonomous control scheme for landing on an electrical line with the NADILE drone (an experimental drone based on original LineDrone key features for inspection of power lines) and assesses the operating envelope under various wind conditions. A Monte Carlo method is employed to analyze the success probability of landing given initial drone states. The performance of the system is evaluated for two landing strategies, variously controllers parameters and four level of wind intensities. The results show that a two-stage landing strategies offers higher probabilities of landing success and give insight regarding the best controller parameters and the maximum wind level for which the system is robust. Lastly, an experimental demonstration of the system landing autonomously on a power line is presented.

Dimensionless Policies based on the Buckingham $π$ Theorem: Is it a good way to Generalize Numerical Results?

Yes if the context, the list of variables defining the motion control problem, is dimensionally similar. Here we show that by modifying the problem formulation using dimensionless variables, we can re-use the optimal control law generated numerically for a specific system to a sub-space of dimensionally similar systems. This is demonstrated, with numerically generated optimal controllers, for the classic motion control problem of swinging-up a torque-limited inverted pendulum. We also discuss the concept of regime, a region in the space of context variables, that can help relax the condition on dimensional similarity. Futhermore, we discuss how applying dimensionnal scaling of the input and output of a context-specific policy is equivalent to substituing the new systems parameters in an analytical equation for dimentionnaly similar systems. It remains to be seen if this approach can also help generalizing policies for more complex high-dimensional problems.

A Bimodal Hydrostatic Actuator for Robotic Legs with Compliant Fast Motion and High Lifting Force

Robotic legs have bimodal operations: swing phases when the leg needs to move quickly in the air (high-speed, low-force) and stance phases when the leg bears the weight of the system (low-speed, high-force). Sizing a traditional single-ratio actuation system for such extremum operations leads to oversized heavy electric motor and poor energy efficiency, which hinder the capability of legged systems that bear the mass of their actuators and energy source. This paper explores an actuation concept where a hydrostatic transmission is dynamically reconfigured using valves to suit the requirements of each phase of a robotic leg. An analysis of the mass-delay-flow trade-off for the switching valve is presented. Then, a custom actuation system is built and integrated on a robotic leg test bench to evaluate the concept. Experimental results show that 1) small motorized ball valves can make fast transitions between operating modes when designed for this task, 2) the proposed operating principle and control schemes allow for seamless transitions, even during an impact with the ground and 3) the actuator characteristics address the needs of a leg bimodal operation in terms of force, speed and compliance.

Low-Level Force-Control of MR-Hydrostatic Actuators

Precise and high-fidelity force control is critical for new generations of robots that interact with humans and unknown environments. Mobile robots, such as wearable devices and legged robots, must also be lightweight to accomplish their function. Hydrostatic transmissions have been proposed as a promising strategy for meeting these two challenging requirements. In previous publications, it was shown that using magnetorheological (MR) actuators coupled with hydrostatic transmissions provides high power density and great open-loop human-robot interactions. Still, the open-loop force fidelity at low and high frequencies are decreased by the transmission's dynamics and by nonlinear friction. This letter compares control strategies for MR-hydrostatic actuator systems to increase its torque fidelity, defined as the bandwidth (measured vs desired torque reference) and transparency (minimizing the undesired forces reflected to the end effector when backdriving the robot). Four control approaches are developed and compared experimentally: (1) Open-loop control with friction compensation; (2) non-collocated pressure feedback; (3) collocated pressure feedback; (4) LQGI state feedback. A dither strategy is also implemented to smoothen ball screw friction. Results show that approaches (1), (2) and (3) can increase the performances but are facing compromises, while approach (4) can simultaneously improve all metrics. These results show the potential of using control schemes for improving the force control performance of robots using tethered architectures, addressing issues such as transmission dynamics and friction.

Multimodal Hydrostatic Actuators for Wearable Robots: A Preliminary Assessment of Mass-Saving and Energy-Efficiency Opportunities

Wearable robots are limited by their actuators performances because they must bear the weight of their own power system and energy source. This paper explores the idea of leveraging hybrid modes to meet multiple operating points with a lightweight and efficient system by using hydraulic valves to dynamically reconfigure the connections of a hydrostatic actuator. The analyzed opportunities consist in 1) switching between a highly geared power source or a fast power source, 2) dynamically connecting an energy accumulator and 3) using a locking mechanism for holding. Based on a knee exoskeleton case study analysis, results show that switching between gearing ratio can lead to a lighter and more efficient actuator. Also, results show that using an accumulator to provide a preload continuous force has great mass-saving potential, but does not reduce mass significantly if used as a power booster for short transients. Finally, using a locking valve can slightly reduce battery mass if the work cycle includes frequent stops. The operating principles of the proposed multimodal schemes are demonstrated with a one-DOF prototype.

A Lightweight Force-Controllable Wearable Arm Based on Magnetorheological-Hydrostatic Actuators

Supernumerary Robotic Limbs (SRLs) are wearable robots augmenting human capabilities by acting as a co-worker, reaching objects, support human arms, etc. However, existing SRLs lack the mechanical backdrivability and bandwidth required for tasks where the interaction forces must be controllable such as painting, manipulating fragile objects, etc. Being highly backdrivable with a high bandwidth while minimizing weight presents a major technological challenge imposed by the limited performances of conventional electromagnetic actuators. This paper studies the feasibility of using magnetorheological (MR) clutches coupled to a low-friction hydrostatic transmission to provide a highly capable, but yet lightweight, force-controllable SRL. A 2.7 kg 2-DOFs wearable robotic arm is designed and built. Shoulder and elbow joints are designed to deliver 39 and 25 Nm, with 115 and 180{\deg} of range of motion. Experimental studies conducted on a one-DOF test bench and validated analytically demonstrate a high force bandwidth (>25 Hz) and a good ability to control interaction forces even when interacting with an external impedance. Furthermore, three force-control approaches are studied and demonstrated experimentally: open-loop, closed-loop on force, and closed-loop on pressure. All three methods are shown to be effective. Overall, the proposed MR-Hydrostatic actuation system is well-suited for a lightweight SRL interacting with both human and environment that add unpredictable disturbances.

Performance Study of Low Inertia Magnetorheological Actuators for Kinesthetic Haptic Devices

A challenge to high quality virtual reality (VR) simulations is the development of high-fidelity haptic devices that can render a wide range of impedances at both low and high frequencies. To this end, a thorough analytical and experimental assessment of the performance of magnetorheological (MR) actuators is performed and compared to electric motor (EM) actuation. A 2 degrees-of-freedom dynamic model of a kinesthetic haptic device is used to conduct the analytical study comparing the rendering area, rendering bandwidth, gearing and scaling of both technologies. Simulation predictions are corroborated by experimental validation over a wide range of operating conditions. Results show that, for a same output force, MR actuators can render a bandwidth over 52.9% higher than electric motors due to their low inertia. Unlike electric motors, the performance of MR actuators for use in haptic devices are not limited by their output inertia but by their viscous damping, which must be carefully addressed at the design stage.

Controller design and experimental evaluation of a motorised assistance for a patient transfer floor lift

Patient transfer is a critical task in patient care and medical sectors. However, it is challenging because it exposes caregivers to injury risks. Available transfer devices, like floor lifts, lead to improvements but are far from perfect. They do not eliminate the caregivers' risk for musculoskeletal disorders, and they can be burdensome to use due to their poor manoeuvrability. This paper presents a new motorised floor lift with a single central motorised wheel connected to an instrumented handle. It proposes admittance controllers (the reference velocity of the motorised wheel, is computed as a function of the force applied on the handle), 1) to achieve the best manoeuvrability, 2) to reduce the effort required in a task, decreasing the risk of injuries among caregivers and 3) while guaranteeing the security and comfort of patients. Two controller designs, one with a linear admittance law and a non-linear admittance law with variable damping, were developed and implemented on a prototype. Tests were performed on seven participants to evaluate the performance of the assistance system and the controllers. The experimental results show that 1) the motorised assistance with the variable damping controller improves manoeuvrability by 28%, 2) reduces the amount of effort required to push the lift by 66% and 3) provides the same level of patient comfort compared to a standard unassisted floor lift.

Development of a hybrid model-based data-driven collision avoidance algorithm for vehicles in low adhesion conditions

Winter conditions, characterized by the presence of ice and snow on the ground, are more likely to lead to road accidents. This paper presents an experimental proof of concept of a collision avoidance algorithm for vehicles evolving in low adhesion conditions, implemented on a 1/5th scale car platform. In the proposed approach, a model-based estimator first processes the high-dimensional sensors data of the IMU, LIDAR and encoders to estimate physically relevant vehicle and ground conditions parameters such as the inertial velocity of the vehicle $v$, the friction coefficient $\mu$, the cohesion $c$ and the internal shear angle $\phi$. Then, a data-driven predictor is trained to predict the optimal maneuver to perform in the situation characterized by the estimated parameters. Experiments show that it is possible to 1) produce a real-time estimate of the relevant ground parameters, and 2) determine an optimal collision avoidance maneuver based on the estimated parameters.