def gen_edges(state_pos: np.ndarray,
graph_base: graph_ltpl.data_objects.GraphBase.GraphBase,
stepsize_approx: float,
min_vel_race: float = 0.0,
closed: bool = True) -> None:
"""
Generate edges for a given node skeleton.
:param state_pos: stacked list of x and y coordinates of all nodes to be considered for edge generation
:param graph_base: reference to the class object holding all graph relevant information
:param stepsize_approx: number of samples to be generated for each spline (every n meter one sample)
:param min_vel_race: min. race speed compared to global race line (in percent), all splines not allowing
this velocity will be removed --> set this value to 0.0 in order to allow all splines
:param closed: if true, the track is assumed to be a closed circuit
:Authors:
* Tim Stahl <*****@*****.**>
:Created on:
28.09.2018
"""
# ------------------------------------------------------------------------------------------------------------------
# PREPARE DATA -----------------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------------------------------
if graph_base.lat_offset <= 0:
raise ValueError(
'Requested to small lateral offset! A lateral offset larger than zero must be allowed!'
)
# ------------------------------------------------------------------------------------------------------------------
# DEFINE EDGES AND SAMPLE SPLINE COEFFICIENTS ----------------------------------------------------------------------
# ------------------------------------------------------------------------------------------------------------------
# calculate splines for race-line
raceline_cl = np.vstack((graph_base.raceline, graph_base.raceline[0]))
x_coeff_r, y_coeff_r, _, _ = tph.calc_splines.calc_splines(
path=raceline_cl)
tic = time.time()
# loop over start_layers
for i in range(len(state_pos)):
tph.progressbar.progressbar(i,
len(state_pos) - 1,
prefix="Calculate splines")
start_layer = i
end_layer = i + 1
# if requested end-layer exceeds number of available layers
if end_layer >= len(state_pos):
if closed:
# if closed, connect to first layers
end_layer = end_layer - len(state_pos)
else:
break
# loop over nodes in start_layer
for start_n in range(len(state_pos[start_layer][0])):
# get end node reference (node with same offset to race line)
end_n_ref = graph_base.raceline_index[
end_layer] + start_n - graph_base.raceline_index[start_layer]
# determine allowed lateral offset
# -> get distance between start node and (if possible) central (same index) goal node
d_start = state_pos[start_layer][0][start_n, :]
d_end = state_pos[end_layer][0][
max(0, min(len(state_pos[end_layer][0]) - 1, end_n_ref)), :]
dist = np.sqrt(
np.power(d_end[0] - d_start[0], 2) +
np.power(d_end[1] - d_start[1], 2))
# -> get number of lateral steps based on distance, lateral resolution and allowed lateral offset p. m.
lat_steps = int(
round(dist * graph_base.lat_offset /
graph_base.lat_resolution))
# loop over nodes in end_layer (clipped to the specified lateral offset)
for end_n in range(
max(0, end_n_ref - lat_steps),
min(len(state_pos[end_layer][0]),
end_n_ref + lat_steps + 1)):
if (graph_base.raceline_index[end_layer] == end_n) and \
(graph_base.raceline_index[start_layer] == start_n):
# if race-line element -> use race-line coeffs
x_coeff = x_coeff_r[start_layer, :]
y_coeff = y_coeff_r[start_layer, :]
else:
x_coeff, y_coeff, _, _ = tph.calc_splines.\
calc_splines(path=np.vstack((state_pos[start_layer][0][start_n, :],
state_pos[end_layer][0][end_n, :])),
psi_s=state_pos[start_layer][1][start_n],
psi_e=state_pos[end_layer][1][end_n])
# add calculated edge to graph
graph_base.add_edge(start_layer=start_layer,
start_node=start_n,
end_layer=end_layer,
end_node=end_n,
spline_coeff=[x_coeff, y_coeff])
toc = time.time()
print("Spline generation and edge definition took " + '%.3f' %
(toc - tic) + "s")
# ------------------------------------------------------------------------------------------------------------------
# SAMPLE PATH EDGES (X, Y COORDINATES) -----------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------------------------------
tic = time.time()
rmv_cnt = 0
edge_cnt = 0
for i in range(graph_base.num_layers):
tph.progressbar.progressbar(i,
len(state_pos) - 1,
prefix="Sampling splines ")
start_layer = i
for s in range(graph_base.nodes_in_layer[start_layer]):
pos, psi, raceline, children, _ = graph_base.get_node_info(
layer=start_layer, node_number=s, return_child=True)
# loop over child-nodes
for node in children:
edge_cnt += 1
end_layer = node[0]
e = node[1]
spline = graph_base.get_edge(start_layer=start_layer,
start_node=s,
end_layer=end_layer,
end_node=e)[0]
x_coeff = np.atleast_2d(spline[0])
y_coeff = np.atleast_2d(spline[1])
spline_sample, inds, t_values, _ = tph.interp_splines.interp_splines(
coeffs_x=x_coeff,
coeffs_y=y_coeff,
stepsize_approx=stepsize_approx,
incl_last_point=True)
psi, kappa = tph.calc_head_curv_an.calc_head_curv_an(
coeffs_x=x_coeff,
coeffs_y=y_coeff,
ind_spls=inds,
t_spls=t_values)
# Extract race speed from global race line
vel_rl = graph_base.vel_raceline[i] * min_vel_race
# calculate min. allowed corner radius, when assuming 10m/s² lateral acceleration
min_turn = np.power(vel_rl, 2) / 10.0
# check if spline violates vehicle turn radius (race line is guaranteed to be among graph set)
if (all(abs(kappa) <= 1 / graph_base.veh_turn) and all(abs(kappa) <= 1 / min_turn)) or \
(raceline and graph_base.get_node_info(layer=end_layer, node_number=e)[2]):
graph_base.update_edge(
start_layer=start_layer,
start_node=s,
end_layer=end_layer,
end_node=e,
spline_x_y_psi_kappa=np.column_stack(
(spline_sample, psi, kappa)))
else:
graph_base.remove_edge(start_layer=start_layer,
start_node=s,
end_layer=end_layer,
end_node=e)
rmv_cnt += 1
toc = time.time()
print("Spline sampling took " + '%.3f' % (toc - tic) + "s")
print("Added %d splines to the graph!" % edge_cnt)
if rmv_cnt > 0:
print(
"Removed %d splines due to violation of the specified vehicle's turn radius or velocity aims!"
% rmv_cnt)
def plot_graph_base(self,
graph_base: graph_ltpl.data_objects.GraphBase.GraphBase,
cost_dep_color: bool = True,
plot_edges: bool = True) -> None:
"""
Plot the major components stored in the graph_base object
:param graph_base: reference to the GraphBase object instance holding all graph relevant information
:param cost_dep_color: boolean flag, specifying, whether to plot edges with a variable color (depending on
cost) or not (Note: cost dependent plotting is drastically slower)
:param plot_edges: boolean flag, specifying, whether the edges should be included in the plot
"""
# refline
plt_refline, = plt.plot(graph_base.refline[:, 0],
graph_base.refline[:, 1],
"k--", linewidth=1.4, label="Refline")
# track bounds
# bound1 = graph_base.refline + graph_base.normvec_normalized * graph_base.track_width[:, np.newaxis] / 2
# bound2 = graph_base.refline - graph_base.normvec_normalized * graph_base.track_width[:, np.newaxis] / 2
bound1 = graph_base.refline + graph_base.normvec_normalized * np.expand_dims(graph_base.track_width_right, 1)
bound2 = graph_base.refline - graph_base.normvec_normalized * np.expand_dims(graph_base.track_width_left, 1)
x = list(bound1[:, 0])
y = list(bound1[:, 1])
x.append(None)
y.append(None)
x.extend(list(bound2[:, 0]))
y.extend(list(bound2[:, 1]))
plt_bounds, = self.__main_ax.plot(x, y, "k-", linewidth=1.4, label="Bounds")
# norm vecs
x = []
y = []
for i in range(bound1.shape[0]):
temp = np.vstack((bound1[i], bound2[i]))
x.extend(temp[:, 0])
y.extend(temp[:, 1])
x.append(None)
y.append(None)
plt_normals, = plt.plot(x, y, color=TUM_colors['TUM_blue_dark'], linestyle="-", linewidth=0.7, label="Normals")
# raceline points
rlpt = graph_base.refline + graph_base.normvec_normalized * graph_base.alpha[:, np.newaxis]
plt_raceline, = self.__main_ax.plot(rlpt[:, 0], rlpt[:, 1], color=TUM_colors['TUM_blue'], linestyle="-",
linewidth=1.4, label="Raceline")
# plot state poses
nodes = graph_base.get_nodes()
i = 0
x = []
y = []
for node in nodes:
tph.progressbar.progressbar(i, len(nodes) - 1, prefix="Plotting nodes ")
# Try to get node info (if filtered, i.e. online graph, this will fail)
try:
node_pos = graph_base.get_node_info(node[0], node[1])[0]
x.append(node_pos[0])
y.append(node_pos[1])
except ValueError:
pass
i += 1
plt_nodes, = self.__main_ax.plot(x, y, "x", color=TUM_colors['TUM_blue'], markersize=3, label="Nodes")
if plot_edges:
# plot edges
edges = graph_base.get_edges()
i = 0
if not cost_dep_color:
x = []
y = []
color_spline = TUM_colors['TUM_blue_light'] # (0, 1, 0)
min_cost = None
max_cost = None
else:
# get maximum and minimum cost in all provided edges
min_cost = 9999.9
max_cost = -9999.9
for edge in edges:
try:
edge_cost = graph_base.get_edge(edge[0], edge[1], edge[2], edge[3])[2]
min_cost = min(min_cost, edge_cost)
max_cost = max(max_cost, edge_cost)
except ValueError:
pass
color_spline = None
plt_edges = None
for edge in edges:
tph.progressbar.progressbar(i, len(edges) - 1, prefix="Plotting edges ")
# Try to get edge (if filtered, i.e. online graph, this will fail)
try:
spline = graph_base.get_edge(edge[0], edge[1], edge[2], edge[3])
spline_coords = spline[1][:, 0:2]
spline_cost = spline[2]
# cost dependent color
if cost_dep_color:
color_spline = (round(min(1, (spline_cost - min_cost) / (max_cost - min_cost)), 2),
round(max(0, 1 - (spline_cost - min_cost) / (max_cost - min_cost)), 2), 0)
self.__main_ax.plot(spline_coords[:, 0], spline_coords[:, 1], "-",
color=color_spline, linewidth=0.7)
else:
# Faster plot method (but for now, no individual color shading)
x.extend(spline_coords[:, 0])
x.append(None)
y.extend(spline_coords[:, 1])
y.append(None)
except ValueError:
pass
i += 1
# plt.pause(0.000001) # Live plotting -> caution: slows down drastically!
plt_edges = None
if not cost_dep_color:
plt_edges, = self.__main_ax.plot(x, y, "-", color=color_spline, linewidth=0.7, label="Edges")
# properties
leg = self.__main_ax.legend(loc='upper left')
if plot_edges and not cost_dep_color:
elements = [plt_refline, plt_bounds, plt_normals, plt_raceline, plt_nodes, plt_edges]
else:
elements = [plt_refline, plt_bounds, plt_normals, plt_raceline, plt_nodes]
elementd = dict()
# couple legend entry to real line
for leg_element, orig_element in zip(leg.get_lines(), elements):
leg_element.set_pickradius(10) # 5 pts tolerance
elementd[leg_element] = orig_element
# line picking
self.__fig.canvas.mpl_connect('pick_event', lambda event: self.__eh.onpick(event=event,
elementd=elementd))
# detail information
node_plot_marker, = self.__main_ax.plot([], [], 'o', color=TUM_colors['TUM_orange'])
edge_plot_marker, = self.__main_ax.plot([], [], '-', color=TUM_colors['TUM_orange'])
annotation = self.__main_ax.annotate('', xy=[0, 0], xytext=(0, 0), arrowprops={'arrowstyle': "->"})
self.__eh.set_graph_markers(node_plot_marker=node_plot_marker,
edge_plot_marker=edge_plot_marker,
annotation=annotation)
self.__fig.canvas.mpl_connect('motion_notify_event',
lambda event: self.__eh.onhover(event=event,
graph_base=graph_base))
self.__text_display = self.__main_ax.text(0.02, 0.95, "", transform=plt.gcf().transFigure)
self.__text_display2 = self.__main_ax.text(0.8, 0.9, "", transform=plt.gcf().transFigure)
if type(self.__time_ax) is not str:
self.__time_annotation = self.__time_ax.annotate("", xy=(0, 0), xytext=(0.05, 0.90),
textcoords='figure fraction',
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"))
def prune_graph(graph_base: graph_ltpl.data_objects.GraphBase.GraphBase,
closed: bool = True) -> None:
"""
Prune graph - remove nodes and edges that are not reachable within the cyclic graph.
:param graph_base: reference to the GraphBase object instance holding all graph relevant information
:param closed: if false, an un-closed track is assumed, i.e. last layer nodes will not be pruned
:Authors:
* Tim Stahl <*****@*****.**>
:Created on:
28.09.2018
"""
j = 0
rmv_cnt_tot = 0
nodes = graph_base.get_nodes()
while True:
rmv_cnt = 0
for i, node in enumerate(nodes):
tph.progressbar.progressbar(min(j * len(nodes) + i, len(nodes) * 10 - 2),
len(nodes) * 10 - 1, prefix="Pruning graph ")
# if not closed, keep all nodes in start and end-layer
if not closed and (node[0] == graph_base.num_layers - 1 or node[0] == 0):
continue
# get children and parents of node
_, _, _, children, parents = graph_base.get_node_info(layer=node[0],
node_number=node[1],
return_child=True,
return_parent=True)
# remove edges (removing nodes may destroy indexing conventions)
if not children or not parents:
# if no children or no parents, remove all connecting edges
if not children:
for parent in parents:
rmv_cnt += 1
graph_base.remove_edge(start_layer=parent[0],
start_node=parent[1],
end_layer=node[0],
end_node=node[1])
else:
for child in children:
rmv_cnt += 1
graph_base.remove_edge(start_layer=node[0],
start_node=node[1],
end_layer=child[0],
end_node=child[1])
if rmv_cnt == 0:
break
else:
rmv_cnt_tot += rmv_cnt
j += 1
tph.progressbar.progressbar(100, 100, prefix="Pruning graph ")
if rmv_cnt_tot > 0:
print("Removed %d edges, identified as dead ends!" % rmv_cnt_tot)