full_chain.py
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00032 
00033 from sensor_msgs.msg import JointState
00034 from numpy import matrix
00035 import numpy
00036 
00037 class FullChainRobotParams:
00038     '''
00039     Wraps a full set of transforms, including a joint_chain
00040 
00041       root
00042           \    
00043            fixed links before -- chain -- fixed links after
00044     '''
00045     def __init__(self, chain_id, tip, root=None):
00046         self.chain_id = chain_id
00047         self.root = root
00048         self.tip = tip
00049         self.calc_block = FullChainCalcBlock()
00050 
00051     def update_config(self, robot_params):
00052         if self.root == None: 
00053             self.root = robot_params.base_link
00054         try:
00055             chain = robot_params.chains[self.chain_id]
00056             before_chain = robot_params.urdf.get_chain(self.root, chain.root, links=False)
00057             full_chain = robot_params.urdf.get_chain(chain.root, chain.tip)
00058             try:
00059                 after_chain = robot_params.urdf.get_chain(chain.tip, self.tip, links=False)
00060                 link_num = -1
00061             except:
00062                 # using only part of the chain, have to calculate link_num
00063                 tip_chain = robot_params.urdf.get_chain(chain.root,self.tip)
00064                 new_root = full_chain[0]
00065                 i = 1
00066                 link_num = -1
00067                 while i < len(full_chain):
00068                     if full_chain[i] in tip_chain:
00069                         if full_chain[i] in chain._active:
00070                             link_num += 1
00071                             new_root = full_chain[i+1]
00072                     i += 1
00073                 after_chain = robot_params.urdf.get_chain(new_root, self.tip, links=False)
00074         except KeyError:
00075             chain = None
00076             before_chain = robot_params.urdf.get_chain(self.root, self.tip, links=False)
00077             after_chain = []
00078             link_num = None
00079         before_chain_Ts = [robot_params.transforms[transform_name] for transform_name in before_chain]
00080         after_chain_Ts  = [robot_params.transforms[transform_name] for transform_name in after_chain]
00081         self.calc_block.update_config(before_chain_Ts, chain, link_num, after_chain_Ts)
00082 
00083     def build_sparsity_dict(self):
00084         """
00085         Build a dictionary that defines which parameters will in fact affect a measurement for a sensor using this chain.
00086         """
00087         sparsity = dict()
00088         sparsity['transforms'] = {}
00089         sparsity['chains'] = {}
00090         if self.chain_id is not None:
00091             for cur_transform in ( self.calc_block._before_chain_Ts + \
00092                                    self.calc_block._chain._transforms.values() + \
00093                                    self.calc_block._after_chain_Ts ):
00094                 sparsity['transforms'][cur_transform._name] = [1, 1, 1, 1, 1, 1]
00095             sparsity['chains'][self.chain_id] = {}
00096             link_num = self.calc_block._link_num
00097             if link_num < 0:
00098                 link_num = self.calc_block._chain._M
00099             sparsity['chains'][self.chain_id]['gearing'] = [1 for i in range(link_num)]
00100         else:
00101             for cur_transform in ( self.calc_block._before_chain_Ts + \
00102                                    self.calc_block._after_chain_Ts ):
00103                 sparsity['transforms'][cur_transform._name] = [1, 1, 1, 1, 1, 1]
00104         return sparsity
00105 
00106 class FullChainCalcBlock:
00107     def update_config(self, before_chain_Ts, chain, link_num, after_chain_Ts):
00108         self._before_chain_Ts = before_chain_Ts
00109         self._chain = chain
00110         self._link_num = link_num
00111         self._after_chain_Ts = after_chain_Ts
00112 
00113     def fk(self, chain_state):
00114         pose = matrix(numpy.eye(4))
00115 
00116         # Apply the 'before chain' transforms
00117         for before_chain_T in self._before_chain_Ts:
00118             pose = pose * before_chain_T.transform
00119 
00120         # Apply the Chain
00121         if self._chain is not None:
00122             chain_T = self._chain.fk(chain_state, self._link_num)
00123             pose = pose * chain_T
00124 
00125         # Apply the 'after chain' transforms
00126         for after_chain_T in self._after_chain_Ts:
00127             pose = pose * after_chain_T.transform
00128 
00129         return pose
00130 


calibration_estimation
Author(s): Vijay Pradeep, Michael Ferguson
autogenerated on Sun Oct 5 2014 22:44:09