Robustness Experiments for a Planar Hopping Control System

Transcription

1 To appear in International Conference on Clibing and Walking Robots Septeber 22 Robustness Experients for a Planar Hopping Control Syste Kale Harbick and Gaurav S. Sukhate kale gaurav@robotics.usc.edu Robotic Ebedded Systes Laboratory Robotics Research Laboratory Departent of Coputer Science University of Southern California Los Angeles, CA ABSTRACT We explore the robustness of a control syste for a pneuatic onopod siulation by adding Gaussian noise to the sensors and actuators. The control syste is based on Raibert s three-part control syste decoposition; with significant odifications to two of the control loops. Our speed controller uses a neural network to approxiate the neutral point function, and we use a odel-based height controller. No changes were ade to the attitude controller. Siulation experients show that the control syste perfors stably in the noisiest case, with a relative error of approxiately 2%. The control syste is expected to perfor coparably on the real robot since our actual sensors are ore accurate copared to the sensors siulated in these experients. 1 INTRODUCTION The benefits of legged systes over other fors of terrestrial locootion are obvious; they can navigate obstacles which wheeled and treaded vehicles cannot. Dynaically-stable legged systes have advantages over statically-stable legged systes. Running achines can travel faster and can navigate terrain which has points of support that are spaced too far apart for walking achines to reach. Even for the siplest running achine, a one-legged hopper, several research probles have not been copletely solved. Most running achines constructed thus far have served as little ore than existence proofs of different fors of locootion. We seek to construct a running achine that builds on basic locootion with additional behaviors that would be useful in a ore functional robotic syste. To this end we have studied ways to iprove the traditional Raibert three-part control syste [1]. The hopping achine considered in this paper is like a pogo-stick, except it uses a pneuatic rather than echanical spring. To siplify the analysis, we restrict the otion of the robot hopper to the sagittal plane. A diagra of the hopper odel is shown in Figure 1. Table 1 defines the subscripts used in the equations, Table 2 defines the state variables, and Table 3 defines the physical paraeters of the hopper. The leg stroke, hip offset, and ass paraeters are siilar to those of the planar hopping achine reported in [1].

2 Figure 1: 2D hopping achine in stance. The body is not shown. Our controller is based on Raibert s three-part control syste [1]. This ethod decoposes the control into three separate control loops: forward velocity control, body attitude control, and height control. The experients reported in this paper use a odel-based height controller [2], and a speed controller which uses a siple neural network to approxiate the neutral point function [3]. The body attitude controller is unchanged fro Raibert s original design. Siulation experients were perfored first with no noise and then with several cobinations of two sources of sensor noise and one source of actuator noise. All the noise sources were odeled as zero-ean, Gaussian, with no correlation across sources. The noisiest case tested, gave a relative error of approxiately twice that of the noiseless case. The siulated sensors with the least noise are of lower accuracy than the actual sensors we plan to use for the real robot. The paper is organized as follows. Related work is discussed in Section 2. Section 3 suarizes the control syste. Experiental results are presented in Section 4. Conclusions and future work are discussed in Section 5. 2 RELATED WORK A three-part control syste has been used to control onopod hopping robots [1]. This syste used a fixed duration thrust to regulate hopping height, and two PD-controllers to regulate forward running speed. Proportional control has been used to regulate hopping height in siulation [4]. For the Monopod II robot, an adaptive energy feedback controller was used to control the apex energy and thus the apex height [5]. A siilar approach to Raibert s for controlling running speed odifies the neutral point approxiation with an additional factor of the ratio of leg length to rest length [4]. A discrete closed for trajectory odel was used to control a bow leg hopper [6]. Model pa-

3 td touch-down lo lift-off + iediately after iediately before d desired value Table 1: Subscript definitions. L leg length () y height of body c.. () ẏ body vertical velocity (/s) ẋ body horizontal velocity (/s) K s spring constant (N ) θ angle of leg easured fro ground (rad) θ angular velocity of leg (rad/s) I oent of inertia of body w.r.t. foot during stance (kg 2 ) x f distance fro body c.. to foot along horizontal diension () T s stance duration (s) φ body angle w.r.t. horizontal (rad) γ angle of leg w.r.t. body (rad) Table 2: Variable definitions. raeters were experientally deterined with a least squares fit to a set of recorded trajectories. A siulation of a vertical hopping achine was controlled using a near-inverse discrete-tie odel [7]. Tie-varying or unknown paraeters were estiated with a recursive least-squares paraeter estiator. Because the odel was able to update itself in this way, the controller was uch less sensitive to odeling errors and even abrupt changes in physical paraeters. Other statistical learning ethods such as RMRC have been used to learn inverse kineatic appings in real tie [8]. Our speed controller approxiates the neutral point function using a siple neural network, rather than a linear function of forward velocity and stance duration. This allows the controller to track desired speeds beyond the point at which Raibert s approxiation diverges fro linear. For the plant siulated in these experients, this point is approxiately.7 /s. 3 CONTROL SYSTEM 3.1 Height Control The height control loop functions as follows. At lift-off, the controller solves equations of otion for the next five phases: initial ascent, descent, copression, extension, and final ascent. The goal is to control the apex height yax k+1 reached at the end of the final ascent phase. The controller uses state inforation at the beginning of initial ascent (lift-off) and the goal apex height to calculate the proper leg length setting. As the hopper transitions fro initial ascent to descent, the leg setting is realized with a PD controller

4 g gravitational acceleration 9.81 /s 2 µ k viscous leg friction 5. N/s d hip distance fro hip to body c...5 L ax ax leg length.285 s sprung ass kg u unsprung ass.225 kg A p area of piston P noinal pressure of upper leg chaber kp a Table 3: Physical paraeters of the hopper. τ = K P (L L d ) K D L where τ is the leg force coand, and K P and K D are proportional and derivative gains. Values for K P and K D used in the siulations are 13. N/ and 5. N/(/s), respectively. This leg position is aintained until touch-down, at which point the actuator releases and the passive dynaics of stance take over. At lift-off the cycle begins again. First we define the spring constant K s There are two equations of otion for copression and extension K s = A p P s L ax. (1) dl dẏ = ( Ks sin θ sl ẏ µ kẏ s + g ) sin θ (2) where θ k+1 lo dθ dẏ = θk+1 ( lo L 2 ax Ks sin θ µ kẏ sl s + g ) L 2 (3) = ẋk+1 lo sin (π θ k+1 lo ) + ẏ k+1 lo cos (π θ k+1 lo ). (4) L ax Equations 1-4 are the key equations for this odel; see [9] and [2] for further details. 3.2 Speed Control Raibert Controller The neutral point of a hop is the point where the foot should be placed which will result in zero net acceleration [1]. This point is a function of hopping height and forward speed. Raibert used an approxiation of the CG print, i.e. the locus of points on the ground over which the

5 Figure 2: The neutral point is approxiated using an estiation of the CG print. body center of ass travels during stance, to estiate the neutral point. This is represented in Figure 2. The length of the CG print was estiated as the stance duration ties the forward running speed, as given below in Equation 5. x f = ẋt s 2 + k ẋ (ẋ ẋ d ). (5) This foot position is converted into a desired leg angle w.r.t. the body γ d = φ arcsin x f L. Finally a second PD loop is used to calculate the hip torque τ = k p (γ γ d ) k d ( γ). We used the following values for gains: kẋ =.3, k p = 47., k d = Neural Network-Based Controller One proble with the Raibert speed controller, as he points out, is that the neutral point is nearly linear with forward speed only up to a certain velocity. This point varies with the physical paraeters of the syste, but for the plant described in this paper, it is approxiately.7 /s. To overcoe this difficulty, we used a siple neural network to ore closely represent the neutral point function. The original controller was odified to replace the first ter in Equation 5 with a neural network output. The second ter is left unchanged. Training data were generated by a huan operator controlling the foot position at varying speeds and apex heights. The siulation recorded neutral point values for two apex heights

6 Avg. Rel. Noise Profile Error (%) No noise 11.6 σ l = 1 c, σ a = 1, σ t = 5 N 14.7 σ l = 1 c, σ a = 1, σ t = 1 N 16.9 σ l = 1 c, σ a = 2, σ t = 5 N 17.4 σ l = 1 c, σ a = 2, σ t = 1 N 16.1 σ l = 2 c, σ a = 1, σ t = 5 N 13.7 σ l = 2 c, σ a = 1, σ t = 1 N 14.9 σ l = 2 c, σ a = 2, σ t = 5 N 2 σ l = 2 c, σ a = 2, σ t = 1 N 18.6 Table 4: Average relative errors as percentages. and five speeds, for a total of ten training points. The neural network was trained off-line using Levenberg-Marquardt optiization [1, 11]. 4 EXPERIMENTS In each run, the desired speed was varied fro.2 /s to 1.8 /s, while the desired apex height reained constant. Three heights were tested, with eight different noise profiles (and one with no noise), for a total of 27 runs. The heights used were.4,.45, and. These heights correspond to the planned operating range of our actual robot, which is being constructed. The length and angle easureents were containated with additive Gaussian noise, as were the torque settings. Two values for standard deviations were used for each noise source: 1 c and 2 c for leg length, 1 and 2 for angle, and 5 N and 1 N for hip torque. Note that the noise sources are uncorrelated with each other. Height and speed tracking perforances for no noise, iniu noise, and axiu noise are shown in Figure 3. Table 4 shows the average relative error coputed over all three heights for each noise profile. Relative errors in forward velocity as functions of desired velocity and desired apex height are shown in Figure 4. 5 CONCLUSIONS AND FUTURE WORK The experients reported in this paper were intended to deterine the sensitivity of the plant to additive Gaussian noise in what we presue to be the key actuator and sensors. We found that the relative error of the test cases with the highest aounts of noise was approxiately twice that of the noiseless case. The leg length and angle sensors we plan to use on the physical robot have accuracies of 2 and.7, respectively. The standard deviations for the noise of these sensors are.67 and.23. Thus the noise for these sensors is less than the lowest noise case reported in these experieents. The noise profiles of our actuators have yet to be deterined. However, the greatest

7 standard deviation for torque which was tested in these siulations (1 N ) is approxiately 25% of the axiu torque of the actuator. We believe that it is unlikely the true inaccuracy in torque will be greater than the values tested. We expect the controller will perfor coparably on the real robot as it has in the siulations. Future work in siulation includes 3D siulations and experients with additional locootory behaviors such as leaning against a wall and traversing inclined surfaces. We will also test the control syste on a real hopping robot, which is being constructed. ACKNOWLEDGEMENTS The authors would like to thank Michael Poole for his work on designing and constructing the physical robot frae, presently under developent. The Vortex physics siulation toolkit developed by CM Labs was used for the experients reported in this paper. REFERENCES [1] Marc Raibert, Legged Robots that Balance, MIT Press, Cabridge, MA, [2] Kale Harbick and Gaurav S. Sukhate, Controlling hopping height of a pneuatic onopod, in IEEE Intl. Conf. on Robotics and Autoation, 22, vol. 4, pp [3] Kale Harbick and Gaurav S. Sukhate, Speed control of a pneuatic onopod using a neural network, Tech. Rep. IRIS-2-413, Institute for Robotics and Intelligent Systes, University of Southern California, 22. [4] M. Ahadi and M. Buehler, Stable control of a siulated one-legged running robot with hip and leg copliance, IEEE Transactions on Robotics and Autoation, vol. 13, no. 1, pp , February [5] M. Ahadi and M. Buehler, The ARL Monopod II Running Robot: Control and Energetics, in IEEE Intl. Conf. on Robotics and Autoation, 1999, pp [6] G.Z. Zeglin and H.B. Brown, Control of a bow leg hopping robot, in IEEE Intl. Conf. on Robotics and Autoation, [7] Joseph Prosser and Moshe Ka, Control of hopping height for a one-legged hopping achine, Mobile Robots VII, vol. 1831, pp , Noveber [8] A. D Souza and S. Vijayakuar and S. Schaal, Learning inverse kineatics, in IEEE Intl. Conf. on Intelligence in Robotics and Autonoous Systes, 21. [9] Kale Harbick and Gaurav S. Sukhate, Height control for a one-legged hopping robot using a two-diensional odel, Tech. Rep. IRIS-1-46, Institute for Robotics and Intelligent Systes, University of Southern California, 21. [1] K. Levenberg, A ethod for the solution of certain non-linear probles in least squares, Quarterly Journal of Applied Matheatics, vol. 2, no. 2, pp , [11] D. W. Marquardt, An algorith for least-squares estiation of non-linear paraeters, Journal of the Society of Industrial and Applied Matheatics, vol. 11, no. 2, pp , 1963.

8 Forward Velocity (/s) Apex Height () Forward Velocity (/s) Apex Height () Forward Velocity (/s) Apex Height () Tie (s) Tie (s) Tie (s) (a) No noise. (b) σ l = 1 c, σ a = 1, σ t = 5 N (c) σ l = 2 c, σ a = 2, σ t = 1 N Figure 3: Height and speed tracking (a) No noise. (b) σ l = 1 c, σ a = 1, σ t = 5 N (c) σ l = 1 c, σ a = 1, σ t = 1 N (d) σ l = 1 c, σ a = 2, σ t = 5 N (e) σ l = 1 c, σ a = 2, σ t = 1 N (f) σ l = 2 c, σ a = 1, σ t = 5 N (g) σ l = 2 c, σ a = 1, σ t = 1 N (h) σ l = 2 c, σ a = 2, σ t = 5 N (i) σ l = 2 c, σ a = 2, σ t = 1 N Figure 4: Relative errors (in percentages) as a function of speed and height.