Algorithms for Walking, Running, Swimming, Flying, and Manipulation

© Russ Tedrake, 2022

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The passive dynamic walkers and hopping robots described in the last chapter capture the fundamental dynamics of legged locomotion -- dynamics which are fundamentally nonlinear and punctuated by impulsive events due to making and breaking contact with the environment. But if you start reading the literature on humanoid robots, or many-legged robots like quadrupeds, then you will find a quite different set of ideas taking center stage: ideas like the "zero-moment point" and footstep planning. The goal of this chapter is to penetrate that world.

I'd like to start the discussion with a model that might seem quite far from the world of legged robots, but I think it's a very useful way to think about the problem.

Imagine you have a flying vehicle modeled as a single rigid body in a gravity field with some number of force "thrusters" attached. We'll describe the configuration of the vehicle by its orientation, $\theta$ and the location of its center of mass $(x,z)$. The vector from the center of mass to thruster $i$ is given by $r_i$, yielding the equations of motion: \begin{align*} m\ddot{x} =& \sum_i F_{i,x},\\ m\ddot{z} =& \sum_i F_{i,z} - mg,\\ I\ddot\theta =& \sum_i \left[ r_i \times F_i \right]_y, \end{align*} where I've used $F_{i,x}$ to represent the component of the force in the $x$ direction and have taken just the $y$-axis component of the cross product on the last line.

Our goal is to move the hovercraft to a desired position/orientation and to keep it there. If we take the inputs to be $F_i$, then the dynamics are affine (linear plus a constant term). As such, we can stabilize a stabilizable fixed point using a change of coordinates plus LQR or even plan/track a desired trajectory using time-varying LQR. If I were to add additional linear constraints, for instance constraining $F_{min} \le F_i \le F_{max}$, then I can still use linear model-predictive control (MPC) to plan and stabilize a desired motion. By most accounts, this is a relatively easy control problem. (Note that it would be considerably harder if my control input or input constraints depend on the orientation, $\theta$, but we don't need that here yet).

Now imagine that you have just a small number of thrusters (let's say two) each with severe input constraints. To make things more interesting, let us say that you're allowed to move the thrusters, so $\dot{r}_i$ becomes an additional control input, but with some important limitations: you have to turn the thrusters off when they are moving (e.g. $|F_i||\dot{r}_i| = 0$) and there is a limit to how quickly you can move the thrusters $|\dot{r}|_i \le v_{max}$. This problem is now a lot more difficult, due especially to the fact that constraints of the form $u_1 u_2 = 0$ are non-convex.

I find this a very useful thought exercise; somehow our controller needs to make an effectively discrete decision to turn off a thruster and move it. The framework of optimal control should support this - you are sacrificing a short-term loss of control authority for the long-term benefit of having the thruster positioned where you would like, but we haven't developed tools yet that deal well with this discrete plus continuous decision making. We'll need to address that here.

Unfortunately, although the problem is already difficult, we need to continue to add constraints. Now let's say additionally, that the thrusters can only be turned on in certain locations, as cartooned here:

The union of these regions need not form a convex set. Furthermore, these locations could be specified in the robot frame, but may also be specified in the world frame, which I'll call ${\bf p}_i$: $${\bf p}_i = {\bf r}_i - \begin{bmatrix} x \\ 0 \\ z \end{bmatrix}.$$ This problem still feels like it should be tractable, but it's definitely getting hard.

In my view, the hovercraft problem above is a central component of the walking problem. If we consider a walking robot with massless legs, then the feet are exactly like movable thrusters. As above, they are highly constrained - they can only produce force when they are touching the ground, and (typically) they can only produce forces in certain directions, for instance as described by a "friction cone" (you can push on the ground but not pull on the ground, and with Coulomb friction the forces tangential to the ground must be smaller than the forces normal to the ground, as described by a coefficient of friction, e.g. $|F_{\parallel}| < \mu |F_{\perp}|$).

The constraints on where you can put your feet / thrusters will depend on the kinematics of your leg, and the speed at which you can move them will depend on the full dynamics of the leg -- these are difficult constraints to deal with. But the actual dynamics of the rigid body are actually still affine, and still simple!

We don't actually need to have massless legs for this discussion to
make sense. If we use the coordinates $x,z$ to describe the location of
the center of mass (CoM) of the entire robot, and $m$ to represent the
entire mass of the robot, then the first two equations remain unchanged.
The center of mass is a configuration dependent point, and does not have
an orientation, but one important generalization of the orientation
dynamics is given by the centroidal momentum matrix, $A(\bq)$, where
$A(\bq)\dot{\bq}$ captures the linear and angular momentum of the robot
around the center of mass

In the previous chapter we devoted relatively a lot of attention to dynamics of impact, characterized for instance by a guard that resets dynamics in a hybrid dynamical system. In those models we used impulsive ground-reaction forces (these forces are instantaneously infinite, but doing finite work) in order to explain the discontinuous change in velocity required to avoid penetration with the ground. This story can be extended naturally to the dynamics of the center of mass.

For an articulated robot, though, there are a number of possible strategies for the legs which can affect the dynamics of the center of mass. For example, if the robot hits the ground with a stiff leg like the rimless wheel, then the angular momentum about the point of collision will be conserved, but any momentum going into the ground will be lost. However, if the robot has a spring leg and a massless toe like the SLIP model, then no energy need be lost.

One strategy that many highly-articulated legged robots use is to keep their center of mass at a constant height, $$z = c \quad \Rightarrow \quad \dot{z} = \ddot{z} = 0,$$ and minimize their angular momentum about the center of mass (here $\ddot\theta=0$). Using this strategy, if the swinging foot lands roughly below the center of mass, then even with a stiff leg there is no energy dissipated in the collision - all of the momentum is conserved. This often (but does not always) justify ignoring the impacts in the center of mass dynamics of the system.

While not the only important case, it is extremely common for our robots to be walking over flat, or nearly flat terrain. In this situation, even if the robot is touching the ground in a number of places (e.g., two heels and two toes), the constraints on the center of mass dynamics can be summarized very efficiently.

First, on flat terrain $F_{i,z}$ represents the force that is normal to the surface at contact point $i$. If we assume that the robot can only push on the ground (and not pull), then this implies $$\forall i, F_{i,z} \ge 0 \Rightarrow \sum_i F_{i,z} \ge 0 \Rightarrow \ddot{z} \ge -g.$$ In other words, if I cannot pull on the ground, then my center of mass cannot accelerate towards the ground faster than gravity.

Furthermore, if we use a Coulomb model of friction on the ground, with friction coefficient $\mu$, then $$\forall i, |F_{i,x}| \le \mu F_{i,z} \Rightarrow \sum_i |F_{i,x}| \le \mu \sum_i F_z \Rightarrow |\ddot{x}| \le \mu (\ddot{z} + g).$$ For instance, if I keep my center of mass at a constant height, then $\ddot{z}=0$ and $|\ddot{x}| \le \mu g$; this is a nice reminder of just how important friction is if you want to be able to move around in the world.

Even better, let us define the "center of pressure" (CoP) as the point on the ground where $$x_{cop} = \frac{\sum_i p_{i,x} F_{i,z}}{\sum_i F_{i,z}},$$ and since all $p_{i,z}$ are equal on flat terrain, $z_{cop}$ is just the height of the terrain. It turns out that the center of pressure is a "zero-moment point" (ZMP) -- a property that we will demonstrate below -- and moment-balance equation gives us a very important relationship between the location of the CoP and the dynamics of the CoM: \[ (m\ddot{z} + mg) (x_{cop} - x) = (z_{cop} - z) m\ddot{x} - I\ddot\theta. \] If we use the strategy proposed above for ignoring collision dynamics, $\ddot{z} = \ddot{\theta} = 0$, then we have $z - z_{cop}$ is a constant height $h$, and the result is the famous "ZMP equations": \[ \ddot{x} = -\frac{g}{h} (x_{cop}-x). \] So the location of the center of pressure completely determines the acceleration of the center of mass, and vice versa! What's more, this relationship is affine -- a property that we can exploit in a number of ways.

As an example, we can easily relate constraints on the CoP to constraints on $\ddot{x}$. It is easy to verify from the definition that the CoP must live inside the convex hull of the ground contact points. Therefore, if we use the $\ddot{z}=\ddot\theta=0$ strategy, then this directly implies strict bounds on the possible accelerations of the center of mass given the locations of the ground contacts.

The zero-moment point (ZMP) is discussed very frequently in the current
literature on legged robots. It also has an unfortunate tendency to be
surrounded by some confusion; a number of people have defined the ZMP is
slightly different ways (see e.g.

First let us recall that for rigid body systems I can always summarize
the contributions from many external forces as a single *wrench*
(force and torque) on the object -- this is simply because the $F_i$ terms
enter our equations of motion linearly. Specifically, given any point in
space, $r$, in coordinates relative to $(x,z)$:

I can re-write the equations of motion as \begin{align*} m\ddot{x} =& \sum_i F_{i,x} = F_{net,x},\\ m\ddot{z} =& \sum_i F_{i,z} - mg = F_{net,z} - mg,\\ I\ddot\theta =& \sum_i \left[ r_i \times F_i \right]_y = ({\bf r} \times {\bf F}_{net})_y + \tau_{net}, \end{align*} where ${\bf F}_{net} = \sum_i {\bf F}_i$ and the value of $\tau_{net}$ depends on the location ${\bf r}$. For some choices of ${\bf r}$, we can make $\tau_{net}=0$. Examining \[ ({\bf r} \times {\bf F}_{net})_y = r_z F_{net,x} - r_x F_{net,z} = \left[ r_i \times F_i \right]_y, \] we can see that when $F_{net,z} > 0$ there is an entire line of solutions, $r_x = a r_z + b$, including one that will intercept the terrain. For walking robots, it is this point on the ground from which the external wrench can be described by a single force vector (and no moment) that is the famous "zero-moment point" (ZMP). Substituting the back in to replace $F_{net}$, we can see that \[ r_x = \frac{r_z m \ddot{x} - I \ddot\theta}{m\ddot{z} + mg}. \] If we assume that $\ddot{z}=\ddot{\theta}=0$ and replace the relative coordinates with the global coordinates ${\bf r} = {\bf p} - [x,0,z]^T$, then we arrive at precisely the equation presented above.

Furthermore, since \[\left[ r_i \times F_i \right]_y = \sum_i \left(
r_{i,z} F_{i,x} - r_{i,x} F_{i,z} \right), \] and for *flat terrain*
we have \[ r_z F_{net,x} = \sum_i r_{i,z} F_{i,x}, \] then we can see that
this ZMP is exactly the CoP: \[ r_x = \frac{\sum_i r_{i,x} F_{i,z}}{
F_{net,z} }. \]

In three dimensions, we solve for the point on the ground where $\tau_{net,y} = \tau_{net,x} = 0$, but allow $\tau_{net,z} \ne 0$ to extract the analogous equations in the $y$-axis: \[ r_y = \frac{r_z m \ddot{y} - I \ddot\theta}{m\ddot{z} + mg}. \]

Coming soon. For a description of our approach with Atlas, see

Coming soon. For a description of our approach with Atlas, see

Coming soon. For a description of our approach with Atlas, see

In this exercise we implement a simplified version of the footstep
planning method proposed in

- The constraint that limits the maximum step length.
- The constraint for which a foot cannot be in two stepping stones at the same time.
- The constraint that assigns each foot to a stepping stone, for each step of the robot.
- The objective function that minimizes the sum of the squares of the step lengths.

- "Centroidal dynamics of a humanoid robot", Autonomous Robots, no. September 2012, pp. 1--16, jun, 2013. ,
- "Postural stability of biped robots and the foot rotation indicator ({FRI}) point", International Journal of Robotics Research, vol. 18, no. 6, 1999. ,
- "An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion", Proceedings of the International Conference on Robotics and Automation , May, 2014. [ link ] ,
- "Optimization-based Locomotion Planning, Estimation, and Control Design for the {A}tlas Humanoid Robot", Autonomous Robots, vol. 40, no. 3, pp. 429--455, 2016. [ link ] ,
- "Footstep Planning on Uneven Terrain with Mixed-Integer Convex Optimization", Proceedings of the 2014 IEEE/RAS International Conference on Humanoid Robots (Humanoids 2014) , 2014. [ link ] ,
- "Capturability-based analysis and control of legged locomotion, Part 1: Theory and application to three simple gait models", The International Journal of Robotics Research, vol. 31, no. 9, pp. 1094-1113, 2012. ,
- "Whole-body Motion Planning with Centroidal Dynamics and Full Kinematics", IEEE-RAS International Conference on Humanoid Robots, 2014. [ link ] ,

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