Meta-package for Boeing's mobile robot navigation stack. Contains:
autonomy- Main package for navigationastar_planner- Hybrid A* path plannersim_band_planner- Sim band (trajectory optimiser)pure_pursuit_controller- Pure Pursuit (controller)gridmap- Layered probability gridhd_map- interface packagemap_manager- map database interface
Autonomy is a multi-layered system for 3D robot navigation (X,Y and rotation). Conceptually, navigation can be broken into three layers:
- Path planning - produce a rough path from A to B
- Trajectory optimisation - Smooth out the path and swerve around immediate obstacles
- Control - Follow the path and perform last-second collision checking
Each layer can in theory be swapped out with different algorithms without
affecting the next. Therefore, the interface for each layer is separately
defined in navigation_interface.
For now, there is only one of each layer:
Hybrid A* divides the map into a grid and "explores" it by computing the cost required to get to each cell.
The cost is computed as the cost to get from Start to the cell, plus a heuristic. The heuristic is an estimate of the cost to get from that cell to the goal. Starting with the Start cell, the algorithm always explores the next cheapest cell. Because of the heuristic, the search may be "guided" towards the goal without having to explore all the cells (as in Dijkstra), significantly reducing the time required to find a valid path.
A well tuned heuristic is extremely important. If we underestimate the heuristic, it means each time we take a step towards the goal, the traversal is more expensive than what the heuristic predicted, so we will stop exploring down that path and end up doing a wide search (slow). However, an overestimating heuristic will quickly guide you down a narrow path that could end up being invalid (since it doesn't check collisions properly). Currently, the heuristic is calculated using a 2D Dijkstra (ignoring rotation) and estimating the robot as a single circle. Interestingly, the Dijkstra is cached and done in reverse (goal to robot) to be able to re-use the cached costs as the robot drives towards the goal.
The cost is also calculated differently for different types of motion. For example, driving backwards is more expensive than forwards, allowing us to tune the behaviour of the robot.
The robot's footprint is approximated using a few small circles. This makes collision checking fast. For the heursitic, the robot uses a single conservative circle.
http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf
Trajectory optimisation does two things: smooth out the path generated by the planner and alter the path slightly to swerve around obstacles. Sim band does this by treating the path like an elastic band with beads (the nodes). Obstacles exert forces on the nodes and we iteratively shift the nodes to achieve "equilibrium".
The footprint is also approximated using small circles like in the planner. This allows us to calculate the force on each circle individually and from that, calculate the overall torque on the robot, allowing us to optimise rotation as well as translation. Note that the footprint can be different from the planner's but in general, you should have a footprint that is equal to or smaller than the planner so that the planner will not generate a path that causes Sim Band to be in collision.
The controller's job is to track the trajectory while performing last-second, thorough collision checking. Pure pursuit is one of the simplest control algorithms. The vehicle finds a point on the path that is a certain distance ahead (the lookahead distance) and chases it. This method is popular for Ackerman steered vehicles, but our robot can also pivot and strafe.
The main change to the traditional approach is to treat rotation as a third axis and distance is measured as an L2 norm with an adjustable weight for the rotation. Currently the weight is 1.0, meaning 1 rad is equivalent to 1m. We use this distance function to find the point on the path that is closest to the robot as well as for calculating the lookahead.
First, we find the closest node to the robot, then based on the robot's current speed, we calculate the required lookahead distance by multiplying the speed with a lookahead time. We then calculate the target by finding the first point on the path that is just ahead of the required lookahead distance. The robot the chases this point with a PD controller.
When the robot approaches the final node, the integral term is enabled, allowing the robot to completely close the gap.
astar_planner and sim_band_planner need to be built with compiler
optimisation to work in real time:
catkin build modular_navigation --cmake-args -DCMAKE_CXX_FLAGS="-O2 -Wall -Werror"Run the simulation and use goal.py. This script sends goal commands to
autonomy in a loop.
Build astar_planner with the test arg:
catkin build astar_planner --make-args testsThe unit_test will be in devel/.private/astar_planner/lib/astar_planner
The Boeing Company
Phillip Haeusler (Author)
William Ko (Maintainer - [email protected])
Leng Vongchanh
Alexandre Desbiez
Richard Bain
Jason Cochrane
Dominic Wierzbicki
Zhengzhi Zhang
Copyright 2020 The Boeing Company
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