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Path planning is an important part of navigation for mobile robots. Several approaches have been proposed in the literature based on a discretisation of the map, including A*, Theta*, and RRT*. While these approaches have been widely adopted also in real applications, they tend to generate non-smooth paths, which can be difficult to follow, based on the kinematic and dynamic constraints of the robot. Time-Elastic-Bands (TEB) have also been used in the literature, to deform an original path in real-time to produce a smoother path, and to handle potential local changes in the environment, such as the detection of an unknown obstacle. This work analyses the effects on the overall path for different choices of initial paths fed to TEB. In particular, the produced paths are compared in terms of total distance, curvature, and variation in the desired heading. The optimised version of the solution produced by Theta* shows the highest performance among the considered methods and metrics, and we show that it can be successfully followed by an autonomous bicycle. 

In this paper, we develop and compare the performance of different controllers for balancing an autonomous bicycle. The evaluation is carried out both in simulation, using two different models, and experimentally, on a bicycle instrumented with only lightweight components, and leaving the bicycle structure practically unchanged. Two PID controllers, a Linear Quadratic Regulator (LQR), and a fuzzy controller are developed and evaluated in simulations where both noise and disturbances are induced in the models. The simulation shows that the LQR controller has the best performance in the simulation scenarios. Experimental results, on the other hand, show that the PID controllers provide better performance when balancing the instrumented bicycle.

Trajectory tracking for an autonomous bicycle is considered in this paper. The trajectory tracking controller is designed using a Model Predictive Controller with constraints on the lean, steer, and heading angle as well as the position coordinates of the bicycle. The output from the trajectory tracking controller is the desired lean angle and forward velocity. Furthermore, a PID controller is designed to follow the desired lean angle, while maintaining balance, by actuation of the handlebar. The proposed control strategy is evaluated in numerous simulations where a realistic nonlinear model of the bicycle is traversing a go-kart track and a short track with narrow curves. The Hausdorff distance and Mean Squared Error are considered as measurements of the performance. The results show that the bicycle successfully can track desired trajectories at varying velocities.

Several control methods have been proposed to stabilise riderless bicycles but they do not have sufficient simplicity for practical applications. This paper proposes a practical approach to model an instrumented bicycle as a combination of connected systems. Using this model, a PID controller is designed by a loop shaping method to stabilise the instrumented riderless bicycle. The initial results show that the bicycle can be stabilised when running on a roller. The work presented in this paper shows that it is possible to self stabilise a riderless bicycle using cascade PI/PID controllers.

Neurofeedback in real-time has proven effective when subjects learn to control a BCI. To facilitate learning, a closed-loop feedback system should provide neurofeedback with maximal accuracy and minimal delay. In this article, we propose a modular system for real-time neurofeedback experiments and evaluate its performance as a function of increased stress level applied to the system. The system shows stable behavior and decent performance when streaming with many EEG channels (36-72) and 500-5000 Hz, which is common in BCI setups. With very low data loads (1 channel, 500-1000 Hz) the performance dropped significantly and the system became highly unpredictable. We show that the system delays did not correlate linearly with the stress-level applied to the system, emphasizing the importance of system delay tests before conducting real-time BCI-experiments. 

Objectives

Walking speed is an important component of movement and is a predictor of health in the elderly. Pedobarography, the study of forces acting between the plantar surface of the foot and a supporting surface, is an approach to estimating walking speed even when no global positioning system signal is available. The developed portable system, Identifying Velocity and Load (IngVaL), is a cost effective alternative to commercially available pedobarography systems because it only uses three force sensing resistors. In this study, the IngVaL system was evaluated. The three variables investigated in this study were the sensor durability, the proportion of analyzable steps, and the linearity between the system output and the walking speed.

Methods

Data was collected from 40 participants, each of whom performed five walks at five different self-paced walking speeds. The linearity between the walking speed and step frequency measured with R2 values was compared for the walking speed obtained ‘A’ only using amplitude data from the force sensors, ‘B’ that obtained only using the step frequency, and ‘C’ that obtained by combining amplitude data for each of the 40 test participants.

Results

Improvement of the wireless data transmission increased the percentage of analyzable steps from 83.1% measured with a prototype to 96.6% for IngVaL. The linearity comparison showed that the methods A, B, and C were accurate for 2, 15, and 23 participants, respectively.

Conclusions

Increased sensor durability and a higher percentage of analyzed steps indicates that IngVaL is an improvement over the prototype system. The combined strategy of amplitude and step frequency was confirmed as the most accurate method.

Respiratory failure can be difficult to predict. It can develop into a life-threatening condition in just a few minutes, or it can build up more slowly. Thus continuous monitoring of respiratory activity should be mandatory in clinical, high-risk situations, and appropriate monitoring equipment could be life-saving. The review considers non-invasive methods and devices claimed to provide information about respiratory rate or depth, or gas exchange. Methods are categorised into those responding to movement, volume and tissue composition detection; air flow, and blood gas concentration. The merits and limitations of the methods and devices are analysed, considering information. content and their ability to minimise the rate of false alarms and false non-alarms. It is concluded that the field of non-invasive respiratory monitoring is still in an exploratory phase, with numerous reports on specific device solutions but less work on evaluation and adaptation to clinical requirements. Convincing evidence of the clinical usefulness of respiratory monitors is still lacking. Devices responding only to respiratory rate, and lacking information about actual gas exchange, will have limited clinical value. Furthermore, enhancement in specificity and sensitivity to avoid false alarms and non-alarms will be necessary to meet clinical requirements. Miniature CO2 sensors are identified as one route towards substantial improvement.