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The main task is to implement two approaches solving the curvature-constrained Traveling Salesman Problem with neighborhoods.

Deadline | November 28, 2021, 23:59 PST |

Points | 7 |

Label in BRUTE | t2b-dtspn |

Files to submit | archive with `DTSPNSolver.py` |

Resources | B4M36UIR_t2_resource_pack |

In the `plan_tour_decoupled`

function implement the decoupled solution for the Dubins TSP with Neighborhoods (DTSPN) with disk-shaped regions.

The decoupled approach comprises the following basic steps

- Estimate sequence of visits by Euclidean TSP connecting centers of the regions.
- For each region, sample boundary points and heading angles.
- Find the shortest feasible tour comprising Dubins maneuvers connecting the regions, where the sequence of visits is estimated from the ETSP.

The `plan_tour_decoupled`

function has the following prescription:

def plan_tour_decoupled(goals, sensing_radius, turning_radius): """ Compute a DTSPN tour using the decoupled approach. Parameters ---------- goals: list Vector3 list of the TSP goal coordinates (x, y, 0) sensing_radius: float neighborhood of TSP goals turning_radius: float turning radius for the Dubins vehicle model Returns ------- Path, path_length (float) tour as a list of robot configurations in SE3 densely sampled """

In the `plan_tour_noon_bean`

function implement the Noon-Bean trasnform solution for the Dubins TSP with Neighborhoods (DTSPN) with disk-shaped regions.

The Noon-Bean approach comprises the following basic steps

- For each region, sample boundary points and heading angles.
- Construct a distance matrix using the Noon-Bean transform (from lectures) where the individual regions correspond to the NoonBean's Generalized TSP sets.
- Find the shortest feasible tour created from Dubins maneuvers by solving the Asymmetric TSP problem characterized by the distance matrix.

The `plan_tour_noon_bean`

function has the following prescription

def plan_tour_noon_bean(goals, sensing_radius, turning_radius): """ Compute a DTSPN tour using the NoonBean approach. Parameters ---------- goals: list Vector3 list of the TSP goal coordinates (x, y, 0) sensing_radius: float neighborhood of TSP goals turning_radius: float turning radius for the Dubins vehicle model Returns ------- Path, path_length (float) tour as a list of robot configurations in SE3 densely sampled """

The LKH solver seems to be very sensitive to the choice of bigM constant for Noon-Bean transformation. It influences the computational time significantly. The lower the bigM constant is, the faster it computes; but if it is too small, it can violate the original assumption to visit each set only once. The reference sets bigM to be the length of the longest Dubins path and use it also for representing infinity. The distance matrix is normalized before solving by LKH, and thus it cannot contains infinity values. (Alternatively, you can use a slightly smaller number of samples.)

1) Download prepared codes and configuration files.

2) Install 'dubins' package to python3.

pip3 install dubins # or our favorite way to install the package

3) Compile the LKH solver (implementation of the Lin–Kernighan heuristic algorithm) and the GDIP Dubins library as follows

`./install_lkh.sh`

Since both the dubins package and the LKH wrapper used the second part of the tasj use SE(2) coordinates represented as tuples, `pose_to_se2`

are `se2_to_pose`

functions are provided for you convenience:

def pose_to_se2(pose): return pose.position.x,pose.position.y,pose.orientation.to_Euler()[0] def se2_to_pose(se2): pose = Pose() pose.position.x = se2[0] pose.position.y = se2[1] pose.orientation.from_Euler(se2[2],0,0) return pose

courses/uir/hw/t2b-dtspn.txt · Last modified: 2021/09/14 19:44 by pragrmi1