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| 1 | +trajopt_param: |
| 2 | + improve_ratio_threshold: 0.25 # minimum ratio true_improve/approx_improve to accept step. [Research Paper] TrueImprove/ModelImprove > c |
| 3 | + min_trust_box_size: 1e-4 # if trust region gets any smaller, exit and report convergence. [Research Paper] xtol |
| 4 | + min_approx_improve: 1e-4 # if model improves less than this, exit and report convergence |
| 5 | + min_approx_improve_frac: -.Inf # if model improves less than this, exit and report convergence |
| 6 | + max_iter: 100 # The max number of iterations |
| 7 | + trust_shrink_ratio: 0.1 # if improvement is less than improve_ratio_threshold, shrink trust region by this ratio. [Research Paper] tao- |
| 8 | + trust_expand_ratio: 1.5 # if improvement is less than improve_ratio_threshold, shrink trust region by this ratio. [Research Paper] tao+ |
| 9 | + cnt_tolerance: 1e-4 # after convergence of penalty subproblem, if constraint violation is less than this, we're done. [Research Paper] ctol |
| 10 | + max_merit_coeff_increases: 5 # number of times that we jack up penalty coefficient. [Reasearch Paper] Max iteration in PenaltyIteration loop |
| 11 | + merit_coeff_increase_ratio: 10 # ratio that we increate coeff each time. [Researcy Paper] Penalty scaling factory (italic kappa) |
| 12 | + max_time: .inf # not yet implemented |
| 13 | + merit_error_coeff: 10 # initial penalty coefficient. [Research Paper] mu_0 |
| 14 | + trust_box_size: 1e-1 # current size of trust region (component-wise). [Research Paper] s |
| 15 | + |
| 16 | +problem_info: |
| 17 | + basic_info: |
| 18 | + n_steps: 20 # 2 * steps_per_phase |
| 19 | + dt_upper_lim: 2.0 # The upper limit of 1/dt values allowed in the optimization |
| 20 | + dt_lower_lim: 100.0 # The lower limit of 1/dt values allowed in the optimization |
| 21 | + start_fixed: true # if true, start pose is the current pose at timestep = 0 |
| 22 | + # if false, the start pose is the one given by req.start_state |
| 23 | + use_time: false # if false, it means the timestep is unit, meaning x1-x0 is the velocity for example |
| 24 | + # if true, then cost_infos ro cnt_infos should have TT_USE_TIME for their term_type |
| 25 | + convex_solver: 1 # which convex solver to use |
| 26 | + # 1 = AUTO_SOLVER, 2 = BPMPD, 3 = OSQP, 4 = QPOASES, 5 = GUROBI |
| 27 | + |
| 28 | + init_info: |
| 29 | + type: 3 # 1 = STATIONARY, 2 = JOINT_INTERPOLATED, 3 = GIVEN_TRAJ |
| 30 | + # request.start_state should be provided for JOINT_INTERPOLATED and GIVEN_TRAJ |
| 31 | + dt: 0.5 |
| 32 | + |
| 33 | +joint_pos_term_info: |
| 34 | + start_pos: # from req.start_state |
| 35 | + name: start_pos |
| 36 | + first_timestep: 10 # First time step to which the term is applied. |
| 37 | + last_timestep: 10 # Last time step to which the term is applied. |
| 38 | + # if start_fixed is trure then we can not set the first_timestep to 0 for start_pos as it is going |
| 39 | + # to be ignored and only the current pose would be the constraint at timestep = 0. |
| 40 | + term_type: 2 # 1 = TT_COST, 2 = TT_CNT, 3 = TT_USE_TIME |
| 41 | + middle_pos: |
| 42 | + name: middle_pos |
| 43 | + first_timestep: 15 |
| 44 | + last_timestep: 15 |
| 45 | + term_type: 2 |
| 46 | + goal_pos: |
| 47 | + name: goal_pos |
| 48 | + first_timestep: 19 |
| 49 | + last_timestep: 19 |
| 50 | + term_type: 2 |
| 51 | + goal_tmp: # If the constraint from request does not have any name, for example when using MotionPlanning Display in Rviz, |
| 52 | + # goal_tmp is assigned as the name of the constraint. |
| 53 | + # In this case: start_fixed = false and start_pos should be applied at timestep 0 |
| 54 | + # OR: start_fixed = true and start_pos can be applies at any timestep |
| 55 | + name: goal_tmp |
| 56 | + first_timestep: 19 # n_steps - 1 |
| 57 | + last_timestep: 19 # n_steps - 1 |
| 58 | + term_type: 2 |
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