This example demonstrates how to compute an obstacle-free path between two locations on a given map using the Probabilistic Roadmap (PRM) path planner. PRM path planner constructs a roadmap in the free space of a given map using randomly sampled nodes in the free space and connecting them with each other. Once the roadmap has been constructed, you can query for a path from a given start location to a given end location on the map.
In this example, the map is represented as an occupancy grid map using imported data. When sampling nodes in the free space of a map, PRM uses this binary occupancy grid representation to deduce free space. Furthermore, PRM does not take into account the robot dimension while computing an obstacle-free path on a map. Hence, you should inflate the map by the dimension of the robot, in order to allow computation of an obstacle-free path that accounts for the robot's size and ensures collision avoidance for the actual robot. Define start and end locations on the map for the PRM path planner to find an obstacle-free path.
The imported maps are :
Name Size Bytes Class Attributes complexMap 41x52 2132 logical emptyMap 26x27 702 logical simpleMap 26x27 702 logical ternaryMap 501x501 2008008 double
Use the imported
simpleMap data and construct an occupancy grid representation using the
binaryOccupancyMap object. Set the resolution to 2 cells per meter for this map.
map = binaryOccupancyMap(simpleMap,2);
Display the map using the
show function on the
To ensure that the robot does not collide with any obstacles, you should inflate the map by the dimension of the robot before supplying it to the PRM path planner.
Here the dimension of the robot can be assumed to be a circle with radius of 0.2 meters. You can then inflate the map by this dimension using the
robotRadius = 0.2;
As mentioned before, PRM does not account for the dimension of the robot, and hence providing an inflated map to the PRM takes into account the robot dimension. Create a copy of the map before using the
inflate function to preserve the original map.
mapInflated = copy(map); inflate(mapInflated,robotRadius);
Display inflated map
Now you need to define a path planner. Create a
mobileRobotPRM object and define the associated attributes.
prm = mobileRobotPRM;
Assign the inflated map to the PRM object
prm.Map = mapInflated;
Define the number of PRM nodes to be used during PRM construction. PRM constructs a roadmap using a given number of nodes on the given map. Based on the dimension and the complexity of the input map, this is one of the primary attributes to tune in order to get a solution between two points on the map. A large number of nodes create a dense roadmap and increases the probability of finding a path. However, having more nodes increases the computation time for both creating the roadmap and finding a solution.
prm.NumNodes = 50;
Define the maximum allowed distance between two connected nodes on the map. PRM connects all nodes separated by this distance (or less) on the map. This is another attribute to tune in the case of larger and/or complicated input maps. A large connection distance increases the connectivity between nodes to find a path easier, but can increase the computation time of roadmap creation.
prm.ConnectionDistance = 5;
Define start and end locations on the map for the path planner to use.
startLocation = [2 1]; endLocation = [12 10];
Search for a path between start and end locations using the
findpath function. The solution is a set of waypoints from start location to the end location. Note that the
path will be different due to probabilistic nature of the PRM algorithm.
path = findpath(prm, startLocation, endLocation)
path = 7×2 2.0000 1.0000 1.9569 1.0546 1.8369 2.3856 3.2389 6.6106 7.8260 8.1330 11.4632 10.5857 12.0000 10.0000
Display the PRM solution.
Use the imported
complexMap data, which represents a large and complicated floor plan, and construct a binary occupancy grid representation with a given resolution (1 cell per meter)
map = binaryOccupancyMap(complexMap,1);
Display the map.
Copy and inflate the map to factor in the robot's size for obstacle avoidance
mapInflated = copy(map); inflate(mapInflated, robotRadius);
Display inflated map.
Update PRM object with the newly inflated map and define other attributes.
prm.Map = mapInflated;
NumNodes and the
prm.NumNodes = 20; prm.ConnectionDistance = 15;
Display PRM graph.
Define start and end location on the map to find an obstacle-free path.
startLocation = [3 3]; endLocation = [45 35];
Search for a solution between start and end location. For complex maps, there may not be a feasible path for a given number of nodes (returns an empty path).
path = findpath(prm, startLocation, endLocation);
Since you are planning a path on a large and complicated map, larger number of nodes may be required. However, often it is not clear how many nodes will be sufficient. Tune the number of nodes to make sure there is a feasible path between the start and end location.
while isempty(path) % No feasible path found yet, increase the number of nodes prm.NumNodes = prm.NumNodes + 10; % Use the |update| function to re-create the PRM roadmap with the changed % attribute update(prm); % Search for a feasible path with the updated PRM path = findpath(prm, startLocation, endLocation); end
path = 12×2 3.0000 3.0000 4.2287 4.2628 7.7686 5.6520 6.8570 8.2389 19.5613 8.4030 33.1838 8.7614 31.3248 16.3874 41.3317 17.5090 48.3017 25.8527 49.4926 36.8804 ⋮
Display PRM solution.