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This example visualizes the path finding algorithm RRT* in a simple environment.

The algorithm finds a path between two points by randomly expanding a tree from the start point. After it has added a random edge to the tree it looks at nearby nodes to check if it's faster to reach them through this new edge instead, and if so it changes the parent of these nodes. This ensures that the algorithm will converge to the optimal path given enough time.

A detailed explanation can be found in the original paper Karaman, S. Frazzoli, S. 2011. "Sampling-based algorithms for optimal motion planning". or in this medium article.

Logging and visualizing with Rerun

All points are logged using the Points2D archetype, while the lines are logged using the LineStrips2D.

The visualizations in this example were created with the following Rerun code:

Map

Starting point

rr.log("map/start", rr.Points2D([start_point], radii=0.02, colors=[[255, 255, 255, 255]]))

Destination point

rr.log("map/destination", rr.Points2D([end_point], radii=0.02, colors=[[255, 255, 0, 255]]))

Obstacles

rr.log("map/obstacles", rr.LineStrips2D(self.obstacles))

RRT tree

Edges

rr.log("map/tree/edges", rr.LineStrips2D(tree.segments(), radii=0.0005, colors=[0, 0, 255, 128]))

New edges

rr.log("map/new/new_edge", rr.LineStrips2D([(closest_node.pos, new_point)], colors=[color], radii=0.001))

Vertices

rr.log("map/tree/vertices", rr.Points2D([node.pos for node in tree], radii=0.002), rr.AnyValues(cost=[float(node.cost) for node in tree]))

Close nodes

rr.log("map/new/close_nodes", rr.Points2D([node.pos for node in close_nodes]))

Closest node

rr.log("map/new/closest_node", rr.Points2D([closest_node.pos], radii=0.008))

Random points

rr.log("map/new/random_point", rr.Points2D([random_point], radii=0.008))

New points

rr.log("map/new/new_point", rr.Points2D([new_point], radii=0.008))

Path

rr.log("map/path", rr.LineStrips2D(segments, radii=0.002, colors=[0, 255, 255, 255]))

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import rerun as rr  # pip install rerun-sdk
rr.init("rerun_example_app")

rr.connect()  # Connect to a remote viewer
# rr.spawn()  # Spawn a child process with a viewer and connect
# rr.save("recording.rrd")  # Stream all logs to disk

# Associate subsequent data with 42 on the “frame” timeline
rr.set_time_sequence("frame", 42)

# Log colored 3D points to the entity at `path/to/points`
rr.log("path/to/points", rr.Points3D(positions, colors=colors