finish offline jrs generation backport

zonopyrobots/joint_reachable_set/jrs_trig/.gitignore

@@ -0,0 +1,4 @@
+*.mat
+jrs_trig_mat_saved*/
+jrs_trig_tensor_saved*/
+gen_jrs_trig/jrs_trig_dynamics/

zonopyrobots/joint_reachable_set/jrs_trig/gen_jrs_trig/create_jrs_trig_tensor.m

@@ -15,6 +15,7 @@
 t_plan = 0.5;
 t_total = 1;
 dt = 0.01;
+d_kai = pi/24;   % parameter range for acceleration
 
 % generates dynamics parameterized by K
 [dyn_zero_to_t_plan, dyn_t_plan_to_t_total] = ...
@@ -43,6 +44,9 @@
 % save vector of initial velocity subinterval centers
 save(fullfile(save_path,'c_kvi.mat'), 'c_kvi');
 
+% save the parameter range
+save(fullfile(save_path,'d_kai.mat'), 'd_kai');
+
 % set options for reachability analysis:
 options.timeStep = dt;
 options.taylorTerms=5; % number of taylor terms for reachable sets
@@ -63,9 +67,6 @@
 for j = 1:n_JRS
     % break JRS computation into two steps...
     tic;
-    %delta_kai = max(pi/24, (abs(c_kvi(j))+delta_kvi)/3);
-    %delta_kai = pi/24; % EXCLUDE heuristic
-    delta_kai = pi/24;
     % first, use dyn_zero_to_t_plan dynamics
     params.tStart = 0; % start time
     params.tFinal = t_plan; % end time for these dynamics
@@ -74,7 +75,7 @@
     % this is described by eqs. (3) and (8)
     params.x0 = [1; 0; c_kvi(j); c_kai; c_kvi(j); 0]; % start at q_i = 0, so cos(q_i) = 1 and sin(q_i) = 0
     % use two generators, one for K^a_i and one for K^v_i (eq. 8)
-    params.R0 = zonotope([params.x0, [0; 0; 0; delta_kai; 0; 0], [0; 0; delta_kvi; 0; delta_kvi+epsilon; 0]]);
+    params.R0 = zonotope([params.x0, [0; 0; 0; d_kai; 0; 0], [0; 0; delta_kvi; 0; delta_kvi+epsilon; 0]]);
 
     % create system for reachability analysis (1 dummy input)
     % CORA 2018:

zonopyrobots/joint_reachable_set/offline_jrs.py

@@ -11,12 +11,27 @@
     Array = Union[Tensor, ndarray]
 
 class OfflineJRS:
+    """ Wrapper for preloading and processing ARMTD style JRS tensors generated offline
+    
+    These tensors are generated offline using the MATLAB scripts in the jrs_trig/gen_jrs_trig folder.
+    This provides a wrapper for some of the jrs_trig.load_jrs_trig and jrs_trig.process_jrs_trig functions
+    to make it easier to use the JRS tensors. The JRS tensors are preloaded and processed in the __init__ function
+    and then the __call__ function can be used to get the JRS and the corresponding rotatotopes for a given configuration
+    and velocity.
+
+    This specifically loads the tensors from the jrs_trig/jrs_trig_tensor_saved folder
+    """
     def __init__(
         self,
         device: torch.device = 'cpu',
         dtype: torch.dtype = torch.float,
         ):
-        """ Wrapper for preloading and processing JRS tensors """
+        """ Wrapper for preloading and processing JRS tensors
+        
+        Args:
+            device (torch.device, optional): The device to use for the JRS tensors. Defaults to 'cpu'.
+            dtype (torch.dtype, optional): The dtype to use for the JRS tensors. Defaults to torch.float.
+        """
         from .jrs_trig.load_jrs_trig import g_ka
         self.jrs_tensor = _preload_batch_JRS_trig(device=device, dtype=dtype)
         self.g_ka = g_ka