From 509249c62ddaff234b49aadb8d6c787e2cf9b4f8 Mon Sep 17 00:00:00 2001 From: fetty Date: Sat, 19 Aug 2023 21:04:32 +0200 Subject: [PATCH] README updated --- README.md | 28 +- debug/exit_flags_fss.csv | 24053 ++++++++++++++++++++++++++++++++++++ debug/mz_fss.csv | 24433 +++++++++++++++++++++++++++++++++++++ debug/solve_time_fss.txt | 24053 ++++++++++++++++++++++++++++++++++++ debug/steering_fss.csv | 24433 +++++++++++++++++++++++++++++++++++++ docs/KIT_lateral_MPC.pdf | Bin 0 -> 3378869 bytes docs/curvature_MPC.pdf | Bin 0 -> 9347255 bytes params/dyn_accel.yaml | 4 +- src/main.cpp | 2 +- 9 files changed, 96993 insertions(+), 13 deletions(-) create mode 100644 debug/exit_flags_fss.csv create mode 100644 debug/mz_fss.csv create mode 100644 debug/solve_time_fss.txt create mode 100644 debug/steering_fss.csv create mode 100644 docs/KIT_lateral_MPC.pdf create mode 100644 docs/curvature_MPC.pdf diff --git a/README.md b/README.md index 571eb8e..e56e25d 100644 --- a/README.md +++ b/README.md @@ -27,11 +27,9 @@
  • Approach - +
    • +
  • + Workflow
    • @@ -65,7 +63,7 @@ Here's a quick explanation of this project branches: * Handles steering commands. * Lower tuning complexity. * Must have a velocity profile as input for the whole horizon length (prediction horizon). - * A longitudinal controller must run in paralel, following as close as possible the given velocity profile. + * A longitudinal controller must run in parallel, following as close as possible the given velocity profile. * `spatial` : space-dependant Non Linear MPC. Here a coupled (lateral+longitudinal dynamics) autonomous controller is specified. The main characteristics of this controller are: * High computational load (30ms) * Curvature based. @@ -88,12 +86,22 @@ As said before, this pkg depends on [Embotech](https://www.embotech.com/products # Approach -Here the theoretical approach of each branch will be summarized. For more information read [TRO](docs/TRO.pdf), [Lateral MPC]() and [Curvature MPC](https://drive.google.com/file/d/1rntZJFIQ_4R1oglZBTvtA23OXXG3SWAU/view?usp=drive_link) papers. +For specific information on how these controllers work read [TRO](docs/TRO.pdf), [Lateral MPC](docs/KIT_lateral_MPC.pdf) and [Curvature MPC](docs/curvature_MPC.pdf) papers. For the sake of simplicity the different controllers are named after their more important characteristic. However, all the specified MPC controllers are curvature-based and follow a simplified non linear bicycle model. -## Lateral MPC +# Workflow + +In order to solve the Non Linear Optimization Problem (NLOP) stated in [solver declaration](solver/FORCES_problem.m) the following procedure must be followed (in this order): + +* __Initial conditions__: the initial conditions for all the state/control variables must be computed. Usually the only unkown initial conditions are the path tracking variables `n` & `mu` so they must be calculated using the actual car state and the planned trajectory. + +* __Real time parameters & variable boundaries__: the value of all the used real time parameters (or constant variables) must be given to the NLOP. A value for each parameter for each horizon stage must be set, meaning an array of __m__ x __n__ (where __m__ == number of parameters and __n__ == horizon length) must be filled. The same logic is applied to the boundaries array. + +* __Memory buffer__: FORCESPRO's solver enable us to control the RAM used by the solver and set memory buffers to be used by the c-generated solver software. _We don't really need this tool so we let FORCESPRO full control on that aspect._ + +* __Solve__: a call to the solver declaration must be made in order to solve the NLOP. It takes as arguments the __m__ x __n__ parameters array & boundaries array, a pointer to the memory handler, an array to fill with the found solution, an array to fill with solving procedure information and a reference to the Algorithmic Differentiation (AD) function (usually c-generated by FORCESPRO). _If a self-developed AD tool is wanted, a reference to the c++ function should be given instead of the default one._ -## Time dependant MPC +* __Get Solution__: after the NLOP is solved, the given empty solution array will be filled with a solution for each optimization variable for each horizon stage. _It becomes handy to copy this solution to some other data structures in order to work with the given solutions easily._ -## Space dependant MPC +* __Progress Prediction__: finally, a progress (s) prediction should be computed using the NLOP solution in order to use this information when setting the curvature values for the next solver call (next iteration). _Basically, knowing the progress for each predicted state we can look for the closest planner points for each state and pick their curvature values for the next iteration. 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