Exemple #1
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 def rewire(self, q_new, near_v, obs_check):
     """
     :param: q_new: new state form: tuple (mul, buchi)
     :param: near_v: near state form: tuple (mul, buchi)
     :param: obs_check: check obstacle free form: dict { (mulp, mulp): True }
     :return: rewiring the tree
     """
     for near_vertex in near_v:
         if obs_check[(q_new[0], near_vertex[0])] and self.checkTranB(
                 q_new[1], self.tree.nodes[q_new]['label'], near_vertex[1]):
             c = self.tree.nodes[q_new]['cost'] + np.linalg.norm(
                 np.subtract(self.mulp2sglp(
                     q_new[0]), self.mulp2sglp(
                         near_vertex[0])))  # without considering control
             delta_c = self.tree.nodes[near_vertex]['cost'] - c
             # update the cost of node in the subtree rooted at near_vertex
             if delta_c > 0:
                 # self.tree.nodes[near_vertex]['cost'] = c
                 if not list(self.tree.pred[near_vertex].keys()):
                     print('empty')
                 self.tree.remove_edge(
                     list(self.tree.pred[near_vertex].keys())[0],
                     near_vertex)
                 self.tree.add_edge(q_new, near_vertex)
                 edges = dfs_labeled_edges(self.tree, source=near_vertex)
                 for _, v, d in edges:
                     if d == 'forward':
                         self.tree.nodes[v][
                             'cost'] = self.tree.nodes[v]['cost'] - delta_c
Exemple #2
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 def rewire(self, q_new, near_v, obs_check):
     """
     :param: q_new: new state form: tuple (mul, buchi)
     :param: near_v: near state form: tuple (mul, buchi)
     :param: obs_check: check obstacle free form: dict { (mulp, mulp): True }
     :return: rewiring the tree
     """
     for near_vertex in near_v:
         if obs_check[(q_new[0], near_vertex[0])] \
                 and self.checkTranB(q_new[1], self.tree.nodes[q_new]['label'], near_vertex[1]):
             c = self.tree.nodes[q_new]['cost'] \
                 + np.linalg.norm(np.subtract(q_new[0], near_vertex[0]))
             delta_c = self.tree.nodes[near_vertex]['cost'] - c
             # update the cost of node in the subtree rooted at near_vertex
             if delta_c > 0:
                 # self.tree.nodes[near_vertex]['cost'] = c
                 self.tree.remove_edge(
                     list(self.tree.pred[near_vertex].keys())[0],
                     near_vertex)
                 self.tree.add_edge(q_new, near_vertex)
                 edges = dfs_labeled_edges(self.tree, source=near_vertex)
                 acc, changed = self.acpt_check(q_new, near_vertex)
                 self.tree.nodes[near_vertex]['acc'] = set(acc)
                 for u, v, d in edges:
                     if d == 'forward':
                         self.tree.nodes[v][
                             'cost'] = self.tree.nodes[v]['cost'] - delta_c
                         if changed:
                             self.tree.nodes[v]['acc'] = set(
                                 self.acpt_check(u, v)[0])  # copy
Exemple #3
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def update_cost_acc(multi_tree, subtree, succ, changed, index2node_root,
                    root2index, root_pred2index_node):
    """
    update cost and acc of all children node in all subtrees
    :param multi_tree:
    :param subtree:
    :param succ:
    :param changed:
    :param index2node_root:
    :param root2index:
    :param root_pred2index_node:
    :return:
    """
    # we only update the cost and acc if subtree connects to the root of the whole formula
    if subtree.tree.nodes[succ]['cost'] < subtree.base:
        # all other roots in the current tree or its subtrees
        to_update = [root2index[succ]]
        while to_update:
            index = to_update.pop(0)
            to_update += [root2index[r[1]] for r in index2node_root[index]]
            root = multi_tree[index].init
            multi_tree[index].tree.nodes[root]['acc'] = set(multi_tree[
                root_pred2index_node[root][0]].tree.nodes[root]['acc'])
            delta_c = multi_tree[index].tree.nodes[root]['cost'] - \
                      multi_tree[root_pred2index_node[root][0]].tree.nodes[root][
                          'cost']
            for nodes in multi_tree[index].tree.nodes():
                multi_tree[index].tree.nodes[nodes]['cost'] -= delta_c

            if changed:
                for u, v, d in dfs_labeled_edges(multi_tree[index].tree,
                                                 source=root):
                    if d == 'forward':
                        multi_tree[index].tree.nodes[v]['acc'] = set(
                            multi_tree[index].acpt_check(u, v)[0])
Exemple #4
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 def rewire(self, q_new, succ_list):
     """
     :param: q_new: new state form: tuple (mul, buchi)
     :param: near_v: near state form: tuple (mul, buchi)
     :param: obs_check: check obstacle free form: dict { (mulp, mulp): True }
     :return: rewiring the tree
     """
     for suc in succ_list:
         # root
         if suc == self.init:
             continue
         c = self.tree.nodes[q_new]['cost'] + np.abs(
             q_new[0][0] - suc[0][0]) + np.abs(q_new[0][1] - suc[0][1])
         delta_c = self.tree.nodes[suc]['cost'] - c
         # update the cost of node in the subtree rooted at near_vertex
         if delta_c > 0:
             self.tree.remove_edge(list(self.tree.pred[suc].keys())[0], suc)
             self.tree.add_edge(q_new, suc)
             edges = dfs_labeled_edges(self.tree, source=suc)
             acc, changed = self.acpt_check(q_new, suc)
             self.tree.nodes[suc]['acc'] = set(acc)
             for u, v, d in edges:
                 if d == 'forward':
                     self.tree.nodes[v][
                         'cost'] = self.tree.nodes[v]['cost'] - delta_c
                     if changed:
                         self.tree.nodes[v]['acc'] = set(
                             self.acpt_check(u, v)[0])  # copy
    def rewire(self, q_new, near_nodes, obs_check):
        """
        :param: q_new: new state
        :param: near_nodes: states returned near
        :param: obs_check: check whether obstacle-free
        :return: the tree after rewiring
        """
        STL_cost = []
        i = 0
        for node in near_nodes:
            if obs_check[(q_new[0], node[0])] \
                    and self.check_transition_b(q_new[1], self.biased_tree.nodes[q_new]['label'], node[1]):

                STL_cost = calculate_STL_cost(self.mulp2single(q_new[0]),
                                              self.mulp2single(node[0]))
                c = self.biased_tree.nodes[q_new]['cost'] \
                    + np.linalg.norm(np.subtract(self.mulp2single(q_new[0]), self.mulp2single(node[0])))*STL_cost
                delta_c = self.biased_tree.nodes[node]['cost'] - c
                # update the cost of node in the subtree rooted at the rewired node
                if delta_c > 0:
                    print("node", node, "\n")
                    print('STL_cost_rewire save:', STL_cost, '\n')
                    print("final cost c:", c, "\n")
                    print("previous cost:",
                          self.biased_tree.nodes[node]['cost'], "\n")
                    self.biased_tree.remove_edge(
                        list(self.biased_tree.pred[node].keys())[0], node)
                    self.biased_tree.add_edge(q_new, node)
                    edges = dfs_labeled_edges(self.biased_tree, source=node)
                    for _, v, d in edges:
                        if d == 'forward':
                            self.biased_tree.nodes[v][
                                'cost'] = self.biased_tree.nodes[v][
                                    'cost'] - delta_c
                i += 1
 def rewire(self, q_new, near_nodes, obs_check):
     """
     :param: q_new: new state
     :param: near_nodes: states returned near
     :param: obs_check: check whether obstacle-free
     :return: the tree after rewiring
     """
     for node in near_nodes:
         if obs_check[(q_new[0], node[0])] \
                 and self.check_transition_b(q_new[1], self.biased_tree.nodes[q_new]['label'], node[1]):
             c = self.biased_tree.nodes[q_new]['cost'] \
                 + np.linalg.norm(np.subtract(self.mulp2single(q_new[0]), self.mulp2single(node[0])))
             delta_c = self.biased_tree.nodes[node]['cost'] - c
             # update the cost of node in the subtree rooted at the rewired node
             if delta_c > 0:
                 self.biased_tree.remove_edge(list(self.biased_tree.pred[node].keys())[0], node)
                 self.biased_tree.add_edge(q_new, node)
                 edges = dfs_labeled_edges(self.biased_tree, source=node)
                 for _, v, d in edges:
                     if d == 'forward':
                         self.biased_tree.nodes[v]['cost'] = self.biased_tree.nodes[v]['cost'] - delta_c
Exemple #7
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def construction_tree_connect_root(transfer_tree, sample_list):
    """
    construct the tree following the existing subpath
    :param transfer_tree: 
    :param sample_list: 
    :return: 
    """
    # extend towards to other sample points
    for k in range(1, len(sample_list)):
        cand = sample_list[k]
        # not in the set of nodes of the tree
        cost = transfer_tree.tree.nodes[sample_list[k - 1]]['cost'] + \
            np.linalg.norm(np.subtract(sample_list[k - 1][0], cand[0]))
        if cand not in transfer_tree.tree.nodes:
            # if 'accept' in cand[1]:
            #     transfer_tree.goals.append(cand)
            label_cand = transfer_tree.label(cand[0])
            transfer_tree.tree.add_node(cand, cost=cost, label=label_cand)
            transfer_tree.tree.nodes[cand]['acc'] = transfer_tree.acpt_check(sample_list[k-1], cand)
            transfer_tree.tree.add_edge(sample_list[k-1], cand)
            transfer_tree.search_goal(cand, label_cand, transfer_tree.tree.nodes[cand]['acc'])
        else:
                delta_c = transfer_tree.tree.nodes[cand]['cost'] - cost
                # update the cost of node in the transfer_tree rooted at sample_list[k]
                if delta_c > 0:
                    if not list(transfer_tree.tree.pred[cand].keys()):
                        print('empty')
                    transfer_tree.tree.remove_edge(list(transfer_tree.tree.pred[cand].keys())[0], cand)
                    transfer_tree.tree.add_edge(sample_list[k-1], cand)
                    transfer_tree.tree.nodes[cand]['acc'] = transfer_tree.acpt_check(sample_list[k-1], cand)
                    edges = dfs_labeled_edges(transfer_tree.tree, source=cand)
                    for u, v, d in edges:
                        if d == 'forward':
                            # update cost
                            transfer_tree.tree.nodes[v]['cost'] = transfer_tree.tree.nodes[v]['cost'] - delta_c
                            # update accept state
                            transfer_tree.tree.nodes[v]['acc'] = transfer_tree.acpt_check(u, v)
Exemple #8
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def construction_tree_connect_sample(subtree, sample_list, multi_tree, init,
                                     root_pred2index_node, root2index, centers,
                                     todo_succ, connect, starting2waypoint,
                                     subtask2path, newsubtask2subtask_p,
                                     index2node_root):
    """
    construct current subtree following the existing subpath
    :param subtree:
    :param sample_list:
    :param multi_tree:
    :param init:
    :param root_pred2index_node:
    :param root2index:
    :return:
    """

    # extend towards to other sample points
    for k in range(1, len(sample_list)):
        cand = sample_list[k]
        # connect the second to last to other roots
        if k == len(sample_list) - 1:
            construction_tree_connect_root(
                subtree, sample_list[k - 1],
                subtree.tree.nodes[sample_list[k - 1]]['label'], centers,
                todo_succ, connect, starting2waypoint, subtask2path,
                newsubtask2subtask_p, root2index, root_pred2index_node,
                index2node_root, multi_tree, init)
            break

        # not in the set of nodes of the tree
        cost = subtree.tree.nodes[sample_list[k - 1]]['cost'] + \
               np.linalg.norm(np.subtract(sample_list[k - 1][0], cand[0]))
        if cand not in subtree.tree.nodes():
            # extend-like
            # if 'accept' in cand[1]:
            #     subtree.goals.append(cand)
            label_cand = subtree.label(cand[0]) + '_' + str(1)
            subtree.tree.add_node(cand, cost=cost, label=label_cand)
            subtree.tree.nodes[cand]['acc'] = set(
                subtree.acpt_check(sample_list[k - 1], cand)[0])
            subtree.tree.add_edge(sample_list[k - 1], cand)
            search_goal(multi_tree, init, subtree, cand, label_cand,
                        subtree.tree.nodes[cand]['acc'], root_pred2index_node,
                        root2index)

        else:
            # rewire-like
            delta_c = subtree.tree.nodes[cand]['cost'] - cost
            # update the cost of node in the subtree rooted at sample_list[k]
            if delta_c > 0:
                subtree.tree.remove_edge(
                    list(subtree.tree.pred[cand].keys())[0], cand)
                subtree.tree.add_edge(sample_list[k - 1], cand)
                acc, changed = subtree.acpt_check(sample_list[k - 1], cand)
                subtree.tree.nodes[cand]['acc'] = set(acc)
                edges = dfs_labeled_edges(subtree.tree, source=cand)
                for u, v, d in edges:
                    if d == 'forward':
                        # update cost
                        subtree.tree.nodes[v][
                            'cost'] = subtree.tree.nodes[v]['cost'] - delta_c
                        # update accept state
                        if changed:
                            subtree.tree.nodes[v]['acc'] = set(
                                subtree.acpt_check(u, v)[0])
Exemple #9
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def construction_tree_connect_sample(subtree, sample_list, multi_tree, init,
                                     root_pred2index_node, root2index, centers,
                                     todo_succ, connect, starting2waypoint,
                                     subtask2path, newsubtask2subtask_p,
                                     index2node_root, obs_check):
    """
    construct current subtree following the existing subpath
    :param subtree:
    :param sample_list:
    :param multi_tree:
    :param init:
    :param root_pred2index_node:
    :param root2index:
    :return:
    """

    # print('construction_tree_connect_sample')
    # extend towards to other sample points
    for k in range(1, len(sample_list) - 1):
        cand = sample_list[k]

        # not in the set of nodes of the tree
        cost = subtree.tree.nodes[sample_list[k - 1]]['cost'] + \
                        np.abs(sample_list[k - 1][0][0] - cand[0][0]) + np.abs(sample_list[k - 1][0][1] - cand[0][1])
        if cand not in subtree.tree.nodes():
            # extend-like
            # if 'accept' in cand[1]:
            #     subtree.goals.append(cand)
            label_cand = subtree.label(cand[0]) + '_' + str(1)
            subtree.tree.add_node(cand, cost=cost, label=label_cand)
            subtree.tree.nodes[cand]['acc'] = set(
                subtree.acpt_check(sample_list[k - 1], cand)[0])
            subtree.tree.add_edge(sample_list[k - 1], cand)
            search_goal(multi_tree, init, subtree, cand, label_cand,
                        subtree.tree.nodes[cand]['acc'], root_pred2index_node,
                        root2index, obs_check)

        else:
            # rewire-like
            delta_c = subtree.tree.nodes[cand]['cost'] - cost
            # update the cost of node in the subtree rooted at sample_list[k]
            if delta_c > 0:
                subtree.tree.remove_edge(
                    list(subtree.tree.pred[cand].keys())[0], cand)
                subtree.tree.add_edge(sample_list[k - 1], cand)
                acc, changed = subtree.acpt_check(sample_list[k - 1], cand)
                subtree.tree.nodes[cand]['acc'] = set(acc)
                edges = dfs_labeled_edges(subtree.tree, source=cand)
                for u, v, d in edges:
                    if d == 'forward':
                        # update cost
                        subtree.tree.nodes[v][
                            'cost'] = subtree.tree.nodes[v]['cost'] - delta_c
                        # update accept state
                        if changed:
                            subtree.tree.nodes[v]['acc'] = set(
                                subtree.acpt_check(u, v)[0])

                            # those sampled point are more likely to be specific to one task, and the endpoint of the subpath may not
                            # directly connect to other roots since two regions can not be connected via a straight line
                            # connect to other roots
                            # connect the second to last to other roots
        if k == len(sample_list) - 2:
            construction_tree_connect_root(
                subtree, sample_list[k],
                subtree.tree.nodes[sample_list[k]]['label'], centers,
                todo_succ, connect, starting2waypoint, subtask2path,
                newsubtask2subtask_p, root2index, root_pred2index_node,
                index2node_root, multi_tree, init, obs_check)