A wearable light-touch contact device for human balance support

There is an urgent need for the development of ways to address the major issue of falls among today’s globally aging population. The authors here outline a new approach referred to as virtual light-touch contact to mitigate postural sway during ambulatory and stationary periods, and propose a wearable light-touch (WLT) system featuring a virtual partition around the user that allows determination of virtual forces resulting from related contact. The data produced are used to create vibrotactile fingertip feedback, which supports comprehensive perception of the partition. Non-impaired subjects were recruited to support evaluation of the prototype system (incorporating tactile stimulation and motion-capture technology), with outcomes showing successful mitigation of postural sway in a heel-to-toe tandem stance. Research performed with 150 able-bodied volunteers to validate the performance of the new set-up (incorporating an acceleration sensor and a voice coil motor to render the light-touch effect) suggested that the proposed WLT approach supports human balance on a level comparable to that of the light-touch effect.

Intrinsically Distributed Probabilistic Algorithm for Human–Robot Distance Computation in Collision Avoidance Strategies

Humans and robots are becoming co-workers, both in industrial and medical applications. This new paradigm raises problems related to human safety. To accomplish and solve this issue, many researchers have developed collision avoidance strategies—mainly relying on potential field approaches—in which attractive and repulsive virtual forces are generated between manipulators and objects within a collaborative workspace. The magnitude of such virtual forces strongly depends on the relative distance between the manipulators and the approaching agents, as well on their relative velocity. In this paper, authors developed an intrinsically distributed probabilistic algorithm to compute distances between the manipulator surfaces and humans, allowing tuning the computational time versus estimation accuracy, based on the application requirements. At each iteration, the algorithm computes the human–robot distances, considering all the Cartesian points within a specific geometrical domain, built around humans’ kinematic chain, and selecting a random subset of points outside of it. Experimental validation was performed in a dynamic and unstructured condition to assess the performance of the algorithm, simulating up to six humans into the shared workspace. Tests showed that the algorithm, with the selected hardware, is able to estimate the distance between the human and the manipulator with a RMSE of 5.93 mm (maximum error of 34.86 mm).

Issues of Motion Control of Mobile Robots Based on the Potential Guidance Method

One of the topical areas of research in modern robotics is the problem of local navigation of mobile robots (MR), which ensures the movement of the robot to the target with the bypass of obstacles in the process of movement. The navigation process includes the following steps: mapping the environment, localization of the robot and planning the route leading to the goal. Among the popular methods of local navigation of robots is the method of artificial potential fields (PF). The essence of the PF method is to implement the movement of the MR in the field of "information forces" using the forces of "attraction" to the target position and the forces of "repulsion" from obstacles.This article addresses the issues of local navigation and motion control of the MR based on the method of PF.When using traditional attracting potential forces, the structure of virtual forces near the obstacle depends on the distance of the MR from the target, and the robot movement will slow down at the end of the route, which will inevitably lead to an unjustified tightening of the total time of moving the robot to the target position. To eliminate this undesirable effect, the authors propose to use attracting potential fields of special type.The authors propose new methods of PF allowing to solve the key problems for the control of MR — "traps" (potential pits) and bypass obstacles: the method of two maps of potential fields and the method of "fairway" on the map of potential fields. The methods of "beetle" for solving the problem of bypass obstacles in the condition of the absence of a priori information about the working space of MR are discussed. A modified method of "beetle" having a number of advantages in comparison with classical methods is proposed.

A Hierarchical Bayesian Method for Time Domain Structure Damage Detection

A time domain structural damage detection method based on hierarchical Bayesian framework is proposed. Due to local stiffness reductions, the responses of damaged structures vary from those in undamaged status under the same external excitation. In this paper, the responses of damaged structures are assumed as the result of a summation of known external forces and unknown virtual forces exerted on corresponding undamaged structures. The damages can thus be detected, located, and quantified by the identification of associated virtual forces. A hierarchical Bayesian formulation considering all undetermined damage-related variables is adopted for the identification of virtual forces. The reasonable values of the variables and their uncertainties are depicted by their posterior distributions, sampled by Markov chain Monte Carlo method. Compared with traditional Bayesian formulations, manual choice of prior parameters is avoided and less prior information is required. The proposed virtual force indicator provides a more intuitive perspective for damage detection tasks and is potentially more operable in engineering practice. These advantages are illustrated by simulation of a cantilever beam under various damage conditions.

Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)

As an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reaction force (GRF) were not sufficiently accurate because of the fundamental limitations, fixed coordinates and narrow sensing areas, which cannot cover the whole insole. To address these issues, in this paper, we describe an algorithm of virtual forces and corresponding coordinates with an artificial neural network (ANN) for low-cost flexible insole pressure measurement sensors. The proposed algorithm estimates the magnitude of the GRF and the location of the foot plantar CoP. To compose the algorithm, we divided the insole area into six areas and created six virtual forces and the corresponding coordinates. We used the ANN algorithm with the input of magnitudes of FSR sensors, 1st and 2nd derivatives of them to estimate the virtual forces and coordinates. Eight healthy males were selected for data acquisition. They performed an experiment composed of the following motions: standing with weight shifting, walking with 1 km/h and 2 km/h, squatting and getting up from a sitting position to a standing position. The ANN for estimating virtual forces and corresponding coordinates was fitted according to those data, converted to c script, and downloaded to a microcontroller for validation experiments in real time. The results showed an average RMSE the whole experiment of 31.154 N for GRF estimation and 8.07 mm for CoP calibration. The correlation coefficients of the algorithm were 0.94 for GRF, 0.92 and 0.76 for the X and Y coordinate respectively.