This is the Author's Accepted Manuscript version of the paper: Y. Zheng, M. J. Brudnak, P. Jayakumar, J. L. Stein, and T. Ersal, "An Experimental Evaluation of a Model-Free Predictor Framework in Teleoperated Vehicles," IFAC Workshop on Time Delay Systems, Istanbul, Turkey, 2016.
An Experimental Evaluation of a Model-Free Predictor Framework in Teleoperated Vehicles ? Yingshi Zheng ∗ Mark J. Brudnak ∗∗ Paramsothy Jayakumar ∗∗ Jeffrey L. Stein ∗ Tulga Ersal
∗,??
∗ University of Michigan, Ann Arbor, MI 48109 USA Yingshi Zheng:
[email protected], Tulga Ersal:
[email protected], Jeffrey L. Stein:
[email protected] ∗∗ U.S. Army TARDEC, Warren, MI 48397 USA Mark J. Brudnak:
[email protected] Paramsothy Jayakumar:
[email protected]
Abstract: A teleoperated vehicle is a vehicle operated by a human from a distance by means of a communication network. One important challenge with teleoperated vehicles is that communication delays in the network can negatively affect the mobility performance of the vehicle. This paper adopts and further develops a model-free predictor framework to compensate for communication delays and improve vehicle mobility, where the term “modelfree” indicates that the predictor does not need to know the dynamic equations governing the system. This framework has previously been conceived and applied to the teleoperated vehicle domain; however, prior evaluations have been conducted with simulated drivers and for only the speed control of the vehicle. The contribution of this paper is two-fold. First, the framework is further developed to improve the transient response of the predictors by including a saturation and resetting scheme. Second, to evaluate the effectiveness of the predictor framework with human drivers and combined speed and steering control, a human-in-the-loop simulation platform is developed to emulate a driving task in a virtual environment. Using this platform, human-in-the-loop experiments are performed, where humans are tasked with driving a typical military truck as fast as possible while keeping it as close as possible to the center of the track. Three types of experiments are conducted: (1) without communication delays as a benchmark; (2) with communication delays, but without the predictor framework to quantify the mobility performance degradation due to delays; and (3) with communication delays and the predictor framework to evaluate the change in mobility performance due to the predictor framework. Three metrics are used to quantify performance; namely, track completion time and track keeping error are used to quantify the speed and lateral control performance, respectively, and the steering control effort is monitored to assess drivability. Five drivers repeated each type of experiment seven times, and Analysis of Variance (ANOVA) is used to statistically analyze the results. The conclusion is that the predictor framework improves the mobility performance of the vehicle and increases drivability significantly. Keywords: teleoperated vehicle, model-free predictors, human-in-the-loop simulation 1. INTRODUCTION Teleoperating a vehicle refers to a paradigm where a vehicle is controlled in closed-loop remotely by a human over a communication network. This paradigm is attractive when driver safety or mission efficiency is a major concern, but the vehicle does not have the capability of executing the mission autonomously. ? This work was supported by the Automotive Research Center (ARC) in accordance with Cooperative Agreement W56HZV-142-0001 U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC) Warren, MI. UNCLASSIFIED: Distribution Statement A. Approved for public release. #27479 ??Corresponding author
One difficulty with teleoperation is that delays in the communication network can significantly affect the mobility performance of the vehicle and make the teleoperation very challenging especially at high vehicle speeds. The literature presents many techniques to address this challenge in teleoperated vehicle applications. For example, supervisory control methods add various levels of autonomy to the vehicle, so that the operator is only responsible for designating short term objectives and the vehicle autonomously navigates to the objective (Witus et al., 2011; Sheridan, 1983). Adding autonomy capabilities to the vehicle, however, may not always be an attractive solution due to its cost. Thus, alternative methods have been developed. Predictive displays rely on dynamic models of the system, delays,
and the environment, and project a predicted vision of the world that is likely to result from the current actions of the operator (Witus et al., 2011; Davis et al., 2010; Kelly et al., 2011). Various delay compensation methods have been used in other teleoperated systems, as well (Naghshtabrizi and Hespanha, 2005; Daly and Wang, 2009; Smith and Hashtrudi-Zaad, 2006). These mathematical controllers focus on predicting and recovering the undelayed signals accurately, but also require knowledge of system dynamics. Solutions that do not require knowledge of system dynamics have also been considered in pursuit of the robustness properties such “model-free” approaches typically possess. Passivity-based approaches transmit scattering signals or wave variables through the communication network to guarantee system stability despite the presence of delays if all the components of the system are passive (Anderson and Spong, 1989; Ware and Pan, 2011). A proportionalderivative-like scheme can be used to inject additional damping to the systems to dissipate energy and achieve stability (Hua and Liu, 2010). These methods guarantee stability without knowing the system dynamics on either end of the network, but this stability robustness may come at the cost of transparency (Lawrence, 1993). Therefore, researchers have also focused on developing metrics for and improving transparency (Hashtrudi-Zaad and Salcudean, 2002). Transparency is typically defined in terms of local and remote impedances; i.e., if the local ratio of force to velocity matches the remote ratio, maximum transparency is achieved. Even though this definition is useful in teleoperated manipulator applications, where an accurate replication of remote impedance on the local site is critical for a good haptic feedback, it is unknown what the relationship between this definition of transparency and vehicle mobility is, and thus it is unknown to what extent the teleoperated vehicles mobility performance can be improved with these methods. Recognizing the robustness benefits of a model-free approach, this paper considers a model-free predictor framework first developed in (Tandon et al., 2013). This framework is model-free in the sense that it does not require that the system dynamics are known. Besides robustness, a model-free approach would also have the benefit of decoupling the predictor design from the vehicle design, thus providing more flexibility for using the same predictor for different vehicles. Furthermore, being model-free, the predictor can be used for the vehicle and human driver alike, allowing for a bilateral prediction framework. This model-free predictor framework has been applied to a teleoperated vehicle system in (Ge et al., 2015a), but only for longitudinal speed control on a point mass vehicle model using a proportional-integral controller to represent the driver. This paper extends the prior knowledge with two original contributions. First, a saturation and resetting scheme is introduced into the predictor framework to improve its transient performance. Second, a human-in-the-loop evaluation of the performance of the framework is provided for the first time using a 14 degrees-of-freedom (DoF) vehicle model and a driving scenario that requires both longitudinal and lateral control of the vehicle.
In the experiments, five drivers drove the simulated vehicle seven times for each of the following three scenarios: (1) without communication delays as a benchmark; (2) with communication delays but without the predictor framework to quantify the mobility performance degradation due to delays; and (3) with communication delays and the predictor framework to evaluate the change in mobility performance due to the predictor framework. Three metrics are used to quantify performance; namely, track completion time, track keeping error, and the steering control effort. These metrics capture the vehicle’s longitudinal and lateral performance and drivability, respectively. Analysis of Variance (ANOVA) is used to statistically analyze the results, and the conclusion is that the predictor framework helps significantly improve vehicle mobility and drivability. The rest of the paper is organized as follows. Section 2 presents the predictor framework applied to the teleoperated vehicle system. The simulation platform with predictor framework is described in Section 3. Human-inthe-loop experiments are explained in Section 4, including the addition of a saturation and resetting scheme into the framework. Section 5 presents and discusses the experimental results. Conclusions are given in Section 6. 2. PREDICTOR FRAMEWORK Consider a general teleoperated vehicle system as shown in Fig. 1. The Driver and the Vehicle are two remote subsystems that are coupled through a communication network with bilateral communication delays. For the purposes of modeling and analysis, the network is considered as a pure delay and referred to as delay in short. If the delay from the Driver to the Vehicle and from the Vehicle to the Driver are td1 and td2 , respectively, then there exists a round trip delay of td1 + td2 between the Driver sending out control commands to the Vehicle and receiving a response from the Vehicle. This delay desynchronizes the two remote systems, degrading the performance of the teleoperated vehicle system. It may even cause instability.
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Fig. 1. General teleoperated vehicle setup. The Driver and Vehicle communicate in closed-loop with bilateral delays.
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Fig. 2. Predictor framework applied to the general teleoperated vehicle setup. The Driver and Vehicle communicate with each other indirectly through the predictors.
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ɺ t ) = xɺ (t − t ) xˆ( d
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Fig. 3. The predictor is a first-order time delay system with no information about the dynamic equation of the remote system. To alleviate the negative impact of delay, a predictor framework is adopted as in Fig. 2 (Ge et al., 2015a). Compared to the general teleoperated vehicle setup in Fig. 1, the Driver and Vehicle do not communicate directly to each other. Instead, two predictors are inserted to receive the delayed information from the remote systems and counteract the effect of the delays. On the Driver side, a Vehicle predictor is locally connected to the Driver, receiving the delayed response x2 (t − td2 ) from the Vehicle and predicting the undelayed response to the Driver. Additionally, the Vehicle predictor requires the derivative information of the delayed response, x˙ 2 (t − td2 ), which can either be obtained from the Vehicle system or estimated. Similarly, a Driver predictor on the Vehicle side predicts the undelayed commands from the Driver. The predicted command and response vectors are x ˆ1 (t) and x ˆ2 (t), respectively. The predictor structure considered in this work has been conceived in (Tandon et al., 2013) and is illustrated in Fig. 3. A single input x(t) and its derivative x(t) ˙ from the remote system are delayed by td . The predictor takes the delayed information as inputs and outputs the predicted signal x ˆ(t). The control problem of interest is to make x ˆ(t) track x(t) satisfactorily, thereby improving the closed-loop system performance compared to using x(t−td ). Predictor dynamics are given as (1) x ˆ˙ (t) = x(t ˙ − td ) + λ(x(t − td ) − x ˆ(t − td )) The predictor is a first-order time delay system with only one design parameter λ. Knowledge of the delay td is required to delay the predictor’s state by the same amount. td can be measured by synchronizing the clocks on the two sides of the network and time-stamping the packets. The dynamic equation of the remote system is not required. Referring back to Fig. 2, the Driver side does not need the knowledge of the Vehicle dynamics and vice versa. Thus, the predictor is considered to be model-free. Since the human driver is hard to model and the vehicle dynamics are usually complicated and nonlinear, a modelfree predictor provides an attractive solution. Different drivers and vehicle platforms can be accommodated with the model-free predictor framework with minimal tuning on design parameters. For multiple inputs, the predictor predicts each signal independently with the same predictor structure, but potentially different design parameters λ. The stability analysis of this predictor framework was also addressed in (Tandon et al., 2013) for constant delays. Define the state tracking error as e(t) = x(t) − x ˆ(t) and the coupling error as u(t) = x(t) − x(t − td ). The state
Frequency domain performance of the predictor is studied in (Ge et al., 2015b) and it indicates that the predictor is capable of improving system performance for relatively low frequency signals. Our results in Section 5 support this analysis, as well. This basic predictor structure is further developed in this work to improve the transient response by including a saturation and resetting scheme, the details of which is explained in Section 4.3. 3. SIMULATION PLATFORM To evaluate the predictor framework experimentally, a real-time human-in-the-loop simulation platform has been developed to emulate a teleoperated vehicle system as shown in Fig. 4. The driver station contains a monitor showing the virtual environment the vehicle is moving in, along with a set of Logitech G27 steering wheel and pedals for lateral and longitudinal control of the vehicle. The virtual environment is implemented using the Matlab Simulink Virtual Reality Toolbox. It provides the human drivers with a firstperson camera view and the current vehicle speed. Based on this visual feedback, the drivers use the steering wheel and pedals to control vehicle’s heading and speed. A typical military truck, namely, a notional High Mobility Multipurpose Wheeled Vehicle (HMMWV), is chosen as Driver Station Monitor
Delay td
tracking error system has then the input-output transfer function E(s) s (2) = U (s) s + λe−td s The asymptotic stability of the state tracking error system is guaranteed for constant delays if and only if the design parameter λ is chosen within the range π 0 0 (6) x ˆ(t) ≥ x ˆss (t) if x(t ˙ − td ) ≤ 0 In Fig. 8, the saturation occurs from 5.32 s to 5.92 s, marked by the square frame. Applying the saturation results in faster transient response.
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Fig. 10. Track keeping error in delayed, predictor and ideal cases for all drivers. Delays significantly increase the error for all drivers and the predictors can significantly improve the track keeping performance compared to the delayed case for drivers 2-5.
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Fig. 11. Track completion time in delayed, predictor and ideal cases for all drivers. Delays significantly increase the completion time for all drivers and the predictors can significantly improve it compared to the delayed case for driver 1 and 4. Delayed case
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Fig. 12. Steering control effort in delayed, predictor and ideal cases for all drivers. Delays significantly increase the effort and the predictors can significantly improve it compared to the delayed case for all drivers. statistically significantly different. The delayed case has the worst track keeping performance for all drivers, and the predictors significantly improve the performance compared to the delayed case for 4 out of 5 drivers (p < 0.05). The other two performance metrics, track completion time and steering control effort, are shown in Fig. 11 and Fig. 12, respectively, for each driver. Significant improvements are also found in these two performance metrics due to the predictors (p < 0.05). In the experiments it was observed that drivers put different emphasis on reducing the metrics of track keeping error and completion time. As a result, driver 4 shows significant improvement in both metrics with the predictors, whereas driver 1 significantly improves the time and the remaining drivers significantly improve the error. Considering the two metrics together to quantify vehicle mobility, predictors help significantly improve the vehicle mobility compared to the delayed cases for all drivers. Normalizing the metrics for the delayed and predictor case with respect to the ones of the ideal case and combining all drivers’ data together, the performance metrics as compared in Fig. 13 are obtained using two-way ANOVA. Error bars represent the standard error of mean. Asterisks denote the pairs that are statistically significantly different, which in this case is all pairs (p < 0.05). On average, the predictors reduce the track completion time by 35%, the track keeping error by 50%, and the steering control effort by 53% compared to the difference in the means between the delayed and ideal cases. Smaller metrics indicate better vehicle mobility and drivability. Thus, predictors are effective in terms of alleviating the negative effect of
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Fig. 13. The predictors improve track completion time by 35%, track keeping error by 50%, and steering control effort by 53% compared to the difference in the means between the delayed and ideal cases. delays on vehicle mobility and drivability in a teleoperation setting. Further work remains to be done in testing the effectiveness of the predictors. This study used five human subjects for the experiments. More subjects will be tested in the future. Also, the design parameters of both predictors are set the same for all drivers. Online adaptation of the parameters due to varying signal frequency and different driving styles will be implemented to test if such an adaptive scheme can lead to a better predictor performance and thus better vehicle mobility performance. Finally, only constant delays are considered in this work. Experiments with stochastic delays will also be considered as part of the future work. 6. CONCLUSION This paper considers a model-free predictor framework to improve vehicle mobility and drivability in teleoperated vehicles and tests its performance experimentally for the first time using a human-in-the-loop simulation setup and a driving task that requires both longitudinal and lateral control of the vehicle. In addition, the original framework is developed further to improve the transient response of the predictors by including saturation and resetting. The conclusion from the statistical analysis of the results obtained from five drivers is that the predictors are effective in achieving a higher vehicle speed, more accurate lateral control, and better drivability as indicated by the track completion time, track keeping error, and steering control effort metrics. REFERENCES Anderson, R.J. and Spong, M.W. (1989). Bilateral control of teleoperators with time delay. IEEE Transactions on Automatic Control, 34(5), 494–501. Daly, J.M. and Wang, D.W.L. (2009). Bilateral teleoperation using unknown input observers for force estimation. In Proceedings of the American Control Conference, 89– 95. Davis, J., Smyth, C., and McDowell, K. (2010). The effects of time lag on driving performance and a possible mitigation. IEEE Transactions on Robotics, 26(3), 590– 593. Day, T.D. and Metz, L.D. (2000). The simulation of driver inputs using a vehicle driver model. In SAE Technical Paper, 2000-01-1313.
Ersal, T., Brudnak, M., Salvi, A., Stein, J., Filipi, Z., and Fathy, H. (2011). Development and model-based transparency analysis of an internet-distributed hardware-inthe-loop simulation platform. Mechatronics, 21(1), 22– 29. Ge, X., Brudnak, M., Jayakumar, P., Stein, J., and Ersal, T. (2015a). A model-free predictor framework for teleoperated vehicles. In Proceedings of the American Control Conference, 4573–4578. Ge, X., Zheng, Y., Brudnak, M., Jayakumar, P., Stein, J., and Ersal, T. (2015b). Performance analysis of a modelfree predictor for delay compensation in networked systems. In IFAC Workshop on Time Delay Systems, volume 48, 434–439. Hashtrudi-Zaad, K. and Salcudean, S.E. (2002). Transparency in time-delayed systems and the effect of local force feedback for transparent teleoperation. IEEE Transactions on Robotics and Automation, 18(1), 108– 114. Hua, C.C. and Liu, X. (2010). Delay-dependent stability criteria of teleoperation systems with asymmetric timevarying delays. IEEE Transactions on Robotics, 26(5), 925–932. Kelly, A., Chan, N., Herman, H., Huber, D., Meyers, R., Rander, P., Warner, R., Ziglar, J., and Capstick, E. (2011). Real-time photorealistic virtualized reality interface for remote mobile robot control. International Journal of Robotics Research, 30(3), 384–404. Lawrence, D.A. (1993). Stability and transparency in bilateral teleoperation. IEEE Transactions on Robotics and Automation, 9(5), 624–637. Liu, J., Jayakumar, P., Overholt, J.L., Stein, J., and Ersal, T. (2013). The role of model fidelity in model predictive control based hazard avoidance in unmanned ground vehicles using lidar sensors. In 2013 Dynamic Systems and Control Conference, volume 3. Naghshtabrizi, P. and Hespanha, J.P. (2005). Designing an observer-based controller for a network control system. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, 848–853. Sheridan, T.B. (1983). Supervisory control of remote manipulators, vehicles and dynamic processes: Experiments in command and display aiding. Technical report, Man-machine Systems Lab, Massachusetts Institute of Technology. Smith, A. and Hashtrudi-Zaad, K. (2006). Smith predictor type control architectures for time delayed teleoperation. International Journal of Robotics Research, 25(8), 797–818. Tandon, A., Brudnak, M.J., Stein, J., and Ersal, T. (2013). An observer based framework to improve fidelity in internet-distributed hardware-in-the-loop simulations. In 2013 Dynamic Systems and Control Conference, volume 3. Ware, J. and Pan, Y.J. (2011). Realisation of a bilaterally teleoperated robotic vehicle platform with passivity control. IET Control Theory and Applications, 5(8), 952– 962. Witus, G., Hunt, S., and Janicki, P. (2011). Methods for ugv teleoperation with high latency communications. In Proceedings of the SPIE, Unmanned Systems Technology XIII, volume 8045N.
