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Fix Typos #3452

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Mar 7, 2023
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Fix Typos #3452

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22 changes: 11 additions & 11 deletions nav2_mppi_controller/README.md
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Expand Up @@ -41,14 +41,14 @@ This process is then repeated a number of times and returns a converged solution
| batch_size | int | Default 1000. Count of randomly sampled candidate trajectories |
| time_steps | int | Default 56. Number of time steps (points) in each sampled trajectory |
| model_dt | double | Default: 0.05. Time interval (s) between two sampled points in trajectories. |
| vx_std | double | Default 0.2. Sampling standart deviation for VX |
| vy_std | double | Default 0.2. Sampling standart deviation for VY |
| wz_std | double | Default 0.4. Sampling standart deviation for Wz |
| vx_std | double | Default 0.2. Sampling standard deviation for VX |
| vy_std | double | Default 0.2. Sampling standard deviation for VY |
| wz_std | double | Default 0.4. Sampling standard deviation for Wz |
| vx_max | double | Default 0.5. Max VX (m/s) |
| vy_max | double | Default 0.5. Max VY in either direction, if holonomic. (m/s) |
| vx_min | double | Default -0.35. Min VX (m/s) |
| wz_max | double | Default 1.9. Max WZ (rad/s) |
| temperature | double | Default: 0.3. Selectiveness of trajectories by their costs (The closer this value to 0, the "more" we take in considiration controls with less cost), 0 mean use control with best cost, huge value will lead to just taking mean of all trajectories without cost consideration |
| temperature | double | Default: 0.3. Selectiveness of trajectories by their costs (The closer this value to 0, the "more" we take in consideration controls with less cost), 0 mean use control with best cost, huge value will lead to just taking mean of all trajectories without cost consideration |
| gamma | double | Default: 0.015. A trade-off between smoothness (high) and low energy (low). This is a complex parameter that likely won't need to be changed from the default of `0.1` which works well for a broad range of cases. See Section 3D-2 in "Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving" for detailed information. |
| visualize | bool | Default: false. Publish visualization of trajectories, which can slow down the controller significantly. Use only for debugging. |
| retry_attempt_limit | int | Default 1. Number of attempts to find feasible trajectory on failure for soft-resets before reporting failure. |
Expand Down Expand Up @@ -95,7 +95,7 @@ This process is then repeated a number of times and returns a converged solution
| Parameter | Type | Definition |
| --------------- | ------ | ----------------------------------------------------------------------------------------------------------- |
| consider_footprint | bool | Default: False. Whether to use point cost (if robot is circular or low compute power) or compute SE2 footprint cost. |
| critical_weight | double | Default 20.0. Weight to apply to critic for near collisions closer than `collision_margin_distance` to prevent near collisions **only** as a method of virtually inflating the footprint. This should not be used to generally influence obstacle avoidance away from criticial collisions. |
| critical_weight | double | Default 20.0. Weight to apply to critic for near collisions closer than `collision_margin_distance` to prevent near collisions **only** as a method of virtually inflating the footprint. This should not be used to generally influence obstacle avoidance away from critical collisions. |
| repulsion_weight | double | Default 1.5. Weight to apply to critic for generally preferring routes in lower cost space. This is separated from the critical term to allow for fine tuning of obstacle behaviors with path alignment for dynamic scenes without impacting actions which may directly lead to near-collisions. This is applied within the `inflation_radius` distance from obstacles. |
| cost_power | int | Default 1. Power order to apply to term. |
| collision_cost | double | Default 10000.0. Cost to apply to a true collision in a trajectory. |
Expand All @@ -110,7 +110,7 @@ This process is then repeated a number of times and returns a converged solution
| threshold_to_consider | double | Default 0.4. Distance between robot and goal above which path align cost stops being considered |
| offset_from_furthest | double | Default 20. Checks that the candidate trajectories are sufficiently far along their way tracking the path to apply the alignment critic. This ensures that path alignment is only considered when actually tracking the path, preventing awkward initialization motions preventing the robot from leaving the path to achieve the appropriate heading. |
| trajectory_point_step | double | Default 4. Step of trajectory points to evaluate for path distance to reduce compute time. Between 1-10 is typically reasonable. |
| max_path_occupancy_ratio | double | Default 0.07 (7%). Maximum proportion of the path that can be occupied before this critic is not considered to allow the obstacle and path follow critics to avoid obstacles while following the path's intent in presense of dynamic objects in the scene. |
| max_path_occupancy_ratio | double | Default 0.07 (7%). Maximum proportion of the path that can be occupied before this critic is not considered to allow the obstacle and path follow critics to avoid obstacles while following the path's intent in presence of dynamic objects in the scene. |


#### Path Angle Critic
Expand Down Expand Up @@ -241,26 +241,26 @@ controller_server:

The `model_dt` parameter generally should be set to the duration of your control frequency. So if your control frequency is 20hz, this should be `0.05`. However, you may also set it lower **but not larger**.

Visualization of the trajectories using `visualize` uses compute resources to back out trajectories for visualization and therefore slows compute time. It is not suggested that this parameter is set to `true` during a deployed use, but is a useful debug instrument while tuning the system, but use sparingly. Visualizating 2000 batches @ 56 points at 30 hz is _alot_.
Visualization of the trajectories using `visualize` uses compute resources to back out trajectories for visualization and therefore slows compute time. It is not suggested that this parameter is set to `true` during a deployed use, but is a useful debug instrument while tuning the system, but use sparingly. Visualizing 2000 batches @ 56 points at 30 hz is _a lot_.

The most common parameters you might want to start off changing are the velocity profiles (`vx_max`, `vx_min`, `wz_max`, and `vy_max` if holonomic) and the `motion_model` to correspond to your vehicle. Its wise to consider the `prune_distance` of the path plan in proportion to your maximum velocity and prediction horizon. The only deeper parameter that will likely need to be adjusted for your particular settings is the Obstacle critics' `repulsion_weight` since the tuning of this is proprtional to your inflation layer's radius. Higher radii should correspond to reduced `repulsion_weight` due to the penalty formation (e.g. `inflation_radius - min_dist_to_obstacle`). If this penalty is too high, the robot will slow significantly when entering cost-space from non-cost space or jitter in narrow corridors. It is noteworthy, but likely not necessary to be changed, that the Obstacle critic may use the full footprint information if `consider_footprint = true`, though comes at an increased compute cost.
The most common parameters you might want to start off changing are the velocity profiles (`vx_max`, `vx_min`, `wz_max`, and `vy_max` if holonomic) and the `motion_model` to correspond to your vehicle. Its wise to consider the `prune_distance` of the path plan in proportion to your maximum velocity and prediction horizon. The only deeper parameter that will likely need to be adjusted for your particular settings is the Obstacle critics' `repulsion_weight` since the tuning of this is proportional to your inflation layer's radius. Higher radii should correspond to reduced `repulsion_weight` due to the penalty formation (e.g. `inflation_radius - min_dist_to_obstacle`). If this penalty is too high, the robot will slow significantly when entering cost-space from non-cost space or jitter in narrow corridors. It is noteworthy, but likely not necessary to be changed, that the Obstacle critic may use the full footprint information if `consider_footprint = true`, though comes at an increased compute cost.

If you don't require path following behavior (e.g. just want to follow a goal pose and let the model predictive elements decide the best way to accomplish that), you may easily remove the PathAlign, PathFollow and PathAngle critics.

### Prediction Horizon, Costmap Sizing, and Offsets

As this is a predictive planner, there is some relationship between maximum speed, prediction times, and costmap size that users should keep in mind while tuning for their application. If a controller server costmap is set to 3.0m in size, that means that with the robot in the center, there is 1.5m of information on either side of the robot. When your prediction horizon (time_steps * model_dt) at maximum speed (vx_max) is larger than this, then your robot will be artifically limited in its maximum speeds and behavior by the costmap limitation. For example, if you predict forward 3 seconds (60 steps @ 0.05s per step) at 0.5m/s maximum speed, the **minimum** required costmap radius is 1.5m - or 3m total width.
As this is a predictive planner, there is some relationship between maximum speed, prediction times, and costmap size that users should keep in mind while tuning for their application. If a controller server costmap is set to 3.0m in size, that means that with the robot in the center, there is 1.5m of information on either side of the robot. When your prediction horizon (time_steps * model_dt) at maximum speed (vx_max) is larger than this, then your robot will be artificially limited in its maximum speeds and behavior by the costmap limitation. For example, if you predict forward 3 seconds (60 steps @ 0.05s per step) at 0.5m/s maximum speed, the **minimum** required costmap radius is 1.5m - or 3m total width.

The same applies to the Path Follow and Align offsets from furthest. In the same example if the furthest point we can consider is already at the edge of the costmap, then further offsets are thresholded because they're unusable. So its important while selecting these parameters to make sure that the theoretical offsets can exist on the costmap settings selected with the maximum prediction horizon and velocities desired.

The Path Follow critic cannot drive velocities greater than the projectable distance of that velocity on the available path on the rolling costmap. The Path Align critic `offset_from_furthest` represents the number of path points a trajectory passes through while tracking the path. If this is set either absurdly low (e.g. 5) it can trigger when a robot is simply trying to start path tracking causing some suboptimal behaviors and local minima while starting a task. If it is set absurdly high (e.g. 50) relative to the path resolution and costmap size, then the critic may never trigger or only do so when at full-speed. A balance here is wise. A selection of this value to be ~30% of the maximum velocity distance projected is good (e.g. if a planner produces points every 2.5cm, 60 can fit on the 1.5m local costmap radius. If the max speed is 0.5m/s with a 3s prediction time, then 20 points represents 33% of the maximum speed projected over the prediction horizon onto the path). When in doubt, `prediction_horizon_s * max_speed / path_resolution / 3.0` is a good baseline.

### Obstacle, Inflation Layer, and Path Following

There also exists a relationship between the costmap configurations and the Obstacle critic configurations. If the Obstacle critic is not well tuned with the costmap parameters (inflation radius, scale) it can cause the robot to wobble significantly as it attempts to take finitely lower-cost trajectories with a slightly lower cost in exchange for jerky motion. The default behavior was tuned for small AMRs (e.g. turtlebots or similar), so if using a larger robot, you may want to reduce the `repulsion_weight` in kind. It may also perform awkward maneuvors when in free-space to try to maximize time in a small pocket of 0-cost over a more natural motion which involves moving into some low-costed region. Finally, it may generally refuse to go into costed space at all when starting in a free 0-cost space if the gain is set disproportionately higher than the Path Follow scoring to encourage the robot to move along the path. This is due to the critic cost of staying in free space becoming more attractive than entering even lightly costed space in exchange for progression along the task.
There also exists a relationship between the costmap configurations and the Obstacle critic configurations. If the Obstacle critic is not well tuned with the costmap parameters (inflation radius, scale) it can cause the robot to wobble significantly as it attempts to take finitely lower-cost trajectories with a slightly lower cost in exchange for jerky motion. The default behavior was tuned for small AMRs (e.g. turtlebots or similar), so if using a larger robot, you may want to reduce the `repulsion_weight` in kind. It may also perform awkward maneuvers when in free-space to try to maximize time in a small pocket of 0-cost over a more natural motion which involves moving into some low-costed region. Finally, it may generally refuse to go into costed space at all when starting in a free 0-cost space if the gain is set disproportionately higher than the Path Follow scoring to encourage the robot to move along the path. This is due to the critic cost of staying in free space becoming more attractive than entering even lightly costed space in exchange for progression along the task.

Thus, care should be taken to select weights of the obstacle critic in conjunction with the costmap inflation radius and scale so that a robot does not have such issues. How I (Steve, your friendly neighborhood navigator) tuned this was to first create the appropriate obstacle critic behavior desirable in conjunction with the inflation layer parameters. Its worth noting that the Obstacle critic converts the cost into a distance from obstacles, so the nature of the distribution of costs in the inflation isn't overly significant. However, the inflation radius and the scale will define the cost at the end of the distribution where free-space meets the lowest cost value within the radius. So testing for quality behavior when going over that threshold should be considered.

As you increase or decrease your weights on the Obstacle, you may notice the aforementioned behaviors (e.g. won't overcome free to non-free threshold). To overcome them, increase the FollowPath critic cost to increase the desire for the trajectory planner to continue moving towards the goal. Make sure to not overshoot this though, keep them balanced. A desirable outcome is smooth motion roughly in the center of spaces without significant close interactions with obstacles. It shouldn't be perfectly following a path yet nor should the output velocity be wobbling jaggedly.

Once you have your obstacle avoidance behavior tuned and matched with an appropriate path following penalty, tune the Path Align critic to align with the path. If you design exact-path-alignment behavior, its possible to skip the obstacle critic step as highly tuning the system to follow the path will give it less ability to deviate to avoid obstacles (though it'll slow and stop). Tuning the critic weight for the Obstacle critic high will do the job to avoid near-collisions but the repulsion weight is largely unnecessary to you. For others wanting more dynamic behavior, it _can_ be beneficial to slowly lower the weight on the obstacle critic to give the path alignment critic some more room to work. If your path was generated with a cost-aware planner (like all provided by Nav2) and providing paths sufficiently far from obstacles for your satesfaction, the impact of a slightly reduced Obstacle critic with a Path Alignment critic will do you well. Not over-weighting the path align critic will allow the robot to deviate from the path to get around dynamic obstacles in the scene or other obstacles not previous considered duing path planning. It is subjective as to the best behavior for your application, but it has been shown that MPPI can be an exact path tracker and/or avoid dynamic obstacles very fluidly and everywhere in between. The defaults provided are in the generally right regime for a balanced initial trade-off.
Once you have your obstacle avoidance behavior tuned and matched with an appropriate path following penalty, tune the Path Align critic to align with the path. If you design exact-path-alignment behavior, its possible to skip the obstacle critic step as highly tuning the system to follow the path will give it less ability to deviate to avoid obstacles (though it'll slow and stop). Tuning the critic weight for the Obstacle critic high will do the job to avoid near-collisions but the repulsion weight is largely unnecessary to you. For others wanting more dynamic behavior, it _can_ be beneficial to slowly lower the weight on the obstacle critic to give the path alignment critic some more room to work. If your path was generated with a cost-aware planner (like all provided by Nav2) and providing paths sufficiently far from obstacles for your satisfaction, the impact of a slightly reduced Obstacle critic with a Path Alignment critic will do you well. Not over-weighting the path align critic will allow the robot to deviate from the path to get around dynamic obstacles in the scene or other obstacles not previous considered during path planning. It is subjective as to the best behavior for your application, but it has been shown that MPPI can be an exact path tracker and/or avoid dynamic obstacles very fluidly and everywhere in between. The defaults provided are in the generally right regime for a balanced initial trade-off.
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