The Integration of Tactile and Visual Cues Increases Golf Putting Error in a Mixed-Reality Paradigm.

The integration of visual and tactile cues can enhance perception. However, the nature of this integration, and the subsequent benefits on perception and action execution, are context-dependent. Here, we examined how visual-tactile integration can influence performance on a complex motor task using virtual reality. We asked participants to wear a VR head-mounted display while using a tracked physical putter to make golf putts on a VR golf course in two conditions. In the ‘tactile’ condition, putter contact with the virtual golf ball coincided with physical contact with a physical ball. In a second ‘no tactile’ condition, no physical ball was present, such that only the virtual ball contacted the putter. In contrast to our pre-registered prediction that performance would benefit from the integration of visual and tactile cues, we found golf putting accuracy was higher in the no tactile condition compared to the tactile condition. Participants exhibited higher lateral error variance and over/undershooting when the physical ball was present. These differences in performance between the conditions suggest that tactile cues, when available, were integrated with visual cues. Second, this integration is not necessarily beneficial to performance. We suggest that the decreased performance caused by the addition of a physical ball may have been due to minor incongruencies between the virtual visual cues and the physical tactile cues. We discuss the implications of these results on the use of VR sports training and highlight that the absence of matched tactile cues in VR can result in sub-optimal learning and performance.

Popov-H∞ Robust Path-Tracking Control of Autonomous Ground Vehicles with Consideration of Sector Bounded Kinematic Nonlinearity

This paper proposes a new approach to cope with the kinematic nonlinearity in the H∞ vehicle path-tracking controller synthesis problem. The kinematic nonlinearity presented in the vehicle lateral error state is found to satisfy the sector-bound condition. By isolating the sector bounded nonlinearity via an upper linear fractional transformation (LFT), a Lur'e system is formulated. A nominal robust controller is synthesized to meet both the Popov-H∞ criterion and the regional pole placement requirement. A polytopic gain-scheduling technique is subsequently employed to accommodate the effect of the varying vehicle longitudinal velocity. Finally, an instant-turning maneuver and a sharp lane-changing maneuver are tested in CarSim-Simulink joint simulations whose results demonstrate the superiority of the proposed Popov-H∞ controller over a conventional H∞ controller.

Tracking Control of an Autonomous Head Feeding Combine

This study aims to develop an autonomous combine harvester. A manual steering combine harvester was modified autonomously using navigation systems of an RTK-DGPS, a gyroscope, and crawler speed sensors. These sensors could determine the combine position and heading required to guide the path. The control system is processed for these navigation sensors' data to make the decision of combine movement. Moreover, it commands the actuator to move the steering lever mechanism. The steering control's desired heading angle was determined from lateral error, heading error, and the traveling speed. In this study, the combined harvester's average forward traveling speed was set at 0.17 m/s, adjusted to a navigation sensor's sampling rate of 5 Hz and the steering mechanism delay. The preliminary test showed the combine could turn by pivoting one of its tracks which turned the radius was into 0.4 m. Furthermore, a guidance control system of the combine harvester was developed based on this test result. The developed guidance control system was successfully guiding the combine to follow the harvesting path. The test results showed that the root mean square of the lateral error was less than 0.1 m.

Vision-Based Uncertainty-Aware Lane Keeping Strategy Using Deep Reinforcement Learning

Recent deep learning techniques promise high hopes for self-driving cars while there are still many issues to be addressed such as uncertainties (e.g., extreme weather conditions) in learned models. In this work, for the uncertainty-aware lane keeping, we first propose a convolutional mixture density network (CMDN) model that estimates the lateral position error, the yaw angle error, and their corresponding uncertainties from the camera vision. We then establish a vision-based uncertainty-aware lane keeping strategy in which a high-level reinforcement learning policy hierarchically modulates the reference longitudinal speed as well as the low-level lateral control. Finally, we evaluate the robustness of our strategy against the uncertainties of the learned CMDN model coming from unseen or noisy situations, as compared to the conventional lane keeping strategy without taking into account such uncertainties. Our uncertainty-aware strategy outperformed the conventional lane keeping strategy, without a lane departure in our test scenario during high-uncertainty periods with random occurrences of fog and rain situations on the road. The successfully trained deep reinforcement learning agent slows down the vehicle speed and tries to minimize the lateral error during high uncertainty situations similarly to what human drivers would do in such situations.

Accuracy of Electrode Placement in IRE Treatment with Navigated Guidance

Purpose Evaluate the accuracy of multiple electrode placements in IRE treatment of liver tumours using a stereotactic CT-based navigation system. Method Analysing data from all IRE treatments of liver tumours at one institution until 31 December 2018. Comparing planned with validated electrode placement. Analysing lateral and angular errors and parallelism between electrode pairs Results Eighty-four tumours were treated in 60 patients. Forty-six per cent were hepatocellular carcinoma, and 36% were colorectal liver metastases. The tumours were located in all segments of the liver. Data were complete from 51 treatments. Two hundred and six electrodes and 336 electrode pairs were analysed. The median lateral and angular error, comparing planned and validated electrode placement, was 3.6 mm (range 0.2–13.6 mm) and 3.1° (range 0°–16.1°). All electrodes with a lateral error >10 mm were either re-positioned or excluded before treatment. The median angle between the electrode pairs was 3.8° (range 0.3°–17.2°). There were no electrode placement-related complications. Conclusion The use of a stereotactic CT-based system for navigation of electrode placement in IRE treatment of liver tumours is safe, accurate and user friendly.

A path-tracking algorithm using predictive Stanley lateral controller

Path tracking is one of the most important aspects of autonomous vehicles. The current research focuses on designing path-tracking controllers taking into account the stability of the yaw and the nonholonomic constraints of the vehicle. In most cases, the lateral controller design relies on identifying a path reference point, the one with the shortest distance to the vehicle giving the current state of the vehicle. That restricts the controller’s ability to handle sudden changes of the trajectory heading angle. The present article proposes a new approach that imitates human behavior while driving. It is based on a discrete prediction model that anticipates the future states of the vehicle, allowing the use of the control algorithm in future predicted states augmented with the current controller output. The performance of the proposed approach is verified through several simulations on V-REP simulator with different types of maneuvers (double lane change, hook road, S road, and curved road) and a wide range of velocities. Predictive Stanley controller was used compared to the original Stanley controller. The obtained results of the proposed control approach show the advantage and the performance of the technique in terms of minimizing the lateral error and ensuring yaw stability by an average of 53% and 22%, respectively.

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge–Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

An autonomous driving controller with heading adaptive calibration for agricultural robots

Purpose This paper aims to propose a new autonomous driving controller to calibrate the absolute heading adaptively. Besides, the second purpose of this paper is to propose a new angle-track loop with a mass regulator to improve the adaptability of the autonomous driving system under different loads and road conditions. Design/methodology/approach In this paper, the error model of heading is built and a new autonomous driving controller with heading adaptive calibration is designed. The new controller calculates the average lateral error by the self-adjusting interval window and calibrates the absolute heading through the incremental proportional–integral–derivative (PID) controller. A window-size adjustment strategy, based on the current lateral error and the derivative of lateral error, is proposed to improve both the transient and the steady-state responses. An angle-tracking loop with mass regulator is proposed to improve the adaptability of autonomous steering system under different loads and road conditions. Findings The experiment results demonstrate that this method can compensate the heading installation error and restrain the off-track error from 13.8 to 1.30 cm. The standard error of new controller is smaller than fuzzy-PID calibration controller and the accuracy of autonomous driving system is improved. Originality/value The accuracy of heading calibrated by the new controller is not affected by external factors and the efficiency of calibration is improved. As the model parameters of steering system can be obtained manually, the new autonomous steering controller has more simple structure and is easy to implement. Mass regulator is adjusted according to the road conditions and the mass of harvester, which can improve the system adaptability.

Active Stereo Vision for Precise Autonomous Vehicle Control

This paper describes the development of a low-cost, lowpower, accurate sensor designed for precise, feedback control of an autonomous vehicle to a hitch. The solution that has been developed uses an active stereo vision system, combining classical stereo vision with a low cost, low power laser speckle projection system, which solves the correspondence problem experienced by classic stereo vision sensors. A third camera is added to the sensor for texture mapping. A model test of the hitching problem was developed using an RC car and a target to represent a hitch. A control system is implemented to precisely control the vehicle to the hitch. The system can successfully control the vehicle from within 35° of perpendicular to the hitch, to a final position with an overall standard deviation of 3.0 m m of lateral error and 1.5° of angular error.

Trajectory Control and Sensitivity Analysis of Curiosity Rover on Uneven Terrains

In this paper, the six-wheel lunar rover is simulated in Adams/View software environment and then via co-simulation between adams and matlab/simulink with which a path-following controller is designed and implemented on the rocker-bogie mechanism. The proposed algorithm consists of three parts. First, the inverse kinematic equations are used to transform the trajectory into appropriate desired values. Second, a sliding mode controller (SMC) is designed which used the desired values to control the motion of the robot. Moreover, disturbances are taken into consideration to minimize the lateral error. In order to investigate the proposed integrated algorithm, the analysis of rover traversability on the uneven surface of the moon is performed in two different states, namely by considering the motion restrictions of the rocker-bogie mechanisms and by increasing the rover speed, body yaw angle, and also obstacle height in crossing the rough terrain. Investigation of the rover in different states has given insight on the performance of the proposed controller at limits of mobility of the robot. Finally, to reduce the battery energy consumption, input torques proportional to the load on the wheels are produced. The values of the deviations from the desired path and velocity in all the mentioned analyses indicate the effectiveness of the SMC.