Hi!
Nick, thanks for clearing that up! I hadn't considered the impact of the list as well. It's good to know.
Feeling more confident about the
ExpressionBasedCoordinateForce solution for crank resistance, I want to dive back into the "
goals for average speed.", which was supposed to be the topic here. However, I've encountered some issues related to the forces... I'd appreciate any insights and any other suggestions you might have.
I've conducted simulations for two scenarios:
Crank Only Model
- One actuator (
tau_crank) and one resistance force.
- Varying the resistance force from negative to positive values produced fine results in Moco, with the goals of minimising time (weight 10) and actuators' effort (weight 1). You can view one example of that here:
here. Really easy and straightforward now.
Crank + Human Model
- A 2D human model attached to the crank with 4 actuators on the joints (
hip_l,
hip_r,
knee_l, and
knee_r) and the resistance force. Ankles are locked at 90deg here.
- While varying the resistance, the problem was converging. However... torque values for actuators remained oddly low (close to zero), even with high resistance.
That was not making sense. So, I performed a force analysis (
ForceReport) in the Moco solution, including forces due to constraints.
Conclusion: all forces came from the pedal constraints. Specifically, the
ConstantDistanceConstraint, which I used to attach the feet and pedals. It was a bucket of cold water
![Sad :(](./images/smilies/icon_e_sad.gif)
.
I've retaken a look at the example of kinematic constraints in Moco, but it doesn't address actuator-related issues, as far as I understood.
So my question is:
Should I reconsider the connection between feet and pedal
, or is there a workaround within Moco?
I was chatting with Carlos, and he threw the idea of using
is_free_to_satisfy_constraints, but I'm not sure how that works and whether it's a viable solution for my problem.
Thanks again for your help.
Cheers!