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APPLICATION OF ROBOTICS
FOR PATH PLANNING
Hitesh Mohapatra
https://www.linkedin.com/in/hiteshmohapatra/
WHAT IS ROBOTICS?
 Robotics is the engineering science and technology which
involves the conception , design , operation and manufacture of
robots and computer system for their control , sensory
feedback, and information processing .
 Electronics , mechanics and software are brought together by
robotics .
APPLICATION OF ROBOTICS
 Robots are used for jobs that are dirty ,dull and dangerous.
 Today robotics have many different application areas. some of those
are
 Outer space applications
 Military applications
 Intelligent home applications
 Industry
 Health service
Robotics of path planning
 Path planning is an important primitive for autonomous mobile robots
that lets robots find the shortest or otherwise optimal path between
two points .
 Path planning requires a map of the environment and the robot to be
aware of its location with respect the map.
 The goals of the path planning are
 Introduce suitable map representation
 Explain basic path-planning algorithms ranging from Dijkstra, to
A*, D* and RRT
 Introduce variation of the path planning problem, such as
coverage path planning .
Map representation
 Map representation have two complementary approaches: discrete
and continuous approximations.
 In a discrete approximation, a map is sub-divided into chunks of
equal (e.g., a grid or hexagonal map) or differing sizes (e.g., rooms in
a building). The latter maps are also known as topological maps.
Discrete maps lend themselves well to a graph representation. Here,
every chunk of the map corresponds to a vertex (also known as
“node”), which are connected by edges, if a robot can navigate from
one vertex to the other. For example a road-map is a topological map,
with intersections as vertices and roads as edges. Computationally, a
graph might be stored as an adjacency or incidence list/matrix.
A continuous approximation requires the definition of inner (obstacles)
and outer boundaries, typically in the form of a polygon, whereas paths
can be encoded as sequences of real numbers. Discrete maps are the
dominant representation in robotics.
 The most common map is the occupancy grid map. In a grid map, the
environment is discretized into squares of arbitrary resolution, e.g.
1cm x 1cm, on which obstacles are marked. In a probabilistic
occupancy grid, grid cells can also be marked with the probability
that they contain an obstacle. This is particularly important when the
position of the robot that senses an obstacle is uncertain.
Disadvantages of grid maps are their large memory requirements as
well as computational time to traverse data structures with large
numbers of vertices. A solution to the latter problem are topological
maps that encode entire rooms as vertices and use edges to indicate
navigable connections between them. There is no silver bullet, and
each application might require a different solution that could be a
combination of different map types.
Path-Planning Algorithms
 The problem to find a “shortest” path from one vertex to another
through a connected graph is of interest in multiple domains, most
prominently in the internet, where it is used to find an optimal route
for a data packet. The term “shortest” refers here to the minimum
cumulative edge cost, which could be physical distance (in a robotic
application), delay (in a networking application) or any other metric
that is important for a specific application.
One of the earliest and simplest algorithms is Dijkstra’s
algorithm. Starting from the initial vertex where the path
should start, the algorithm marks all direct neighbours of the
initial vertex with the cost to get there. It then proceeds from
the vertex with the lowest cost to all of its adjacent vertices
and marks them with the cost to get to them via itself if this
cost is lower.
Once all neighbours of a vertex have been checked, the
algorithm proceeds to the vertex with the next lowest cost. A
more detailed description of this algorithm is provided here .
Once the algorithm reaches the goal vertex, it terminates and
the robot can follow the edges pointing towards the lowest
edge cost.
Dijkstra’s algorithm for robotic path
planning,
Illustration of A* planning,
A* and D* become computationally expensive when either the search space is
large, e.g., due to a fine-grain resolution required for the task, or the dimensions
of the search problem are high, e.g. when planning for an arm with multiple
degrees of freedom. A more recent development known as Rapidly-Exploring
Random Trees (RRT) addresses this problem by using a randomized approach that
aims at quickly exploring a large area of the search space with iterative
refinement. For this, the algorithm selects a random point in the environment
and connects it to the initial vertex.
 Subsequent random points are then connected to the closest vertex in the
emerging graph. The graph is then connected to the goal node, whenever a
point in the tree comes close enough given some threshold. Although
generally a coverage algorithm (see also below), RRT can be used for path-
planning by maintaining the cost-to-start on each added point, and biasing
the selection of points to occasionally falling close to the goal. RRT can also
be used to take into account non-holonomic contraints of a specific platform
when generating the next random way-point. One way to implement this is to
calculate the next random point by applying random values to the robot’s
actuators and use the forward kinematics to calculate the next point. Most
recently, variations of RRT have been proposed that will eventually find an
optimal solution. Nevertheless, although RRT quickly finds some solution,
smooth paths usually require additional search algorithms that start from an
initial estimate provided by RRT.
Example of RRT
Robot embodiment
 In order to deal with the physical embodiment of the robot, which
complicates the path-planning process, the robot is reduced to a
point-mass and all the obstacles in the environment are grown by half
of the longest extension of the robot from its centre. This
representation is known as configuration space as it reduces the
representation of the robot to its x and y coordinates in the plane.
Most planning algorithms also do not consider the orientation of the
robot. If this is desired, an additional dimension needs to be
introduced into the configuration space.
Other path-planning applications
 Once the environment has been discretized into a graph we can employ other
algorithms from graph theory to plan desirable robot trajectories
 For example, floor coverage can be achieved by performing a depth-first
search (DFS) or a breadth-first-search (BFS) on a graph where each vertex has
the size of the coverage tool of the robot.
THANK YOU

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Robotics for Path Planning

  • 1. APPLICATION OF ROBOTICS FOR PATH PLANNING Hitesh Mohapatra https://www.linkedin.com/in/hiteshmohapatra/
  • 2. WHAT IS ROBOTICS?  Robotics is the engineering science and technology which involves the conception , design , operation and manufacture of robots and computer system for their control , sensory feedback, and information processing .  Electronics , mechanics and software are brought together by robotics .
  • 3. APPLICATION OF ROBOTICS  Robots are used for jobs that are dirty ,dull and dangerous.  Today robotics have many different application areas. some of those are  Outer space applications  Military applications  Intelligent home applications  Industry  Health service
  • 4. Robotics of path planning  Path planning is an important primitive for autonomous mobile robots that lets robots find the shortest or otherwise optimal path between two points .  Path planning requires a map of the environment and the robot to be aware of its location with respect the map.  The goals of the path planning are  Introduce suitable map representation  Explain basic path-planning algorithms ranging from Dijkstra, to A*, D* and RRT  Introduce variation of the path planning problem, such as coverage path planning .
  • 5. Map representation  Map representation have two complementary approaches: discrete and continuous approximations.  In a discrete approximation, a map is sub-divided into chunks of equal (e.g., a grid or hexagonal map) or differing sizes (e.g., rooms in a building). The latter maps are also known as topological maps. Discrete maps lend themselves well to a graph representation. Here, every chunk of the map corresponds to a vertex (also known as “node”), which are connected by edges, if a robot can navigate from one vertex to the other. For example a road-map is a topological map, with intersections as vertices and roads as edges. Computationally, a graph might be stored as an adjacency or incidence list/matrix.
  • 6. A continuous approximation requires the definition of inner (obstacles) and outer boundaries, typically in the form of a polygon, whereas paths can be encoded as sequences of real numbers. Discrete maps are the dominant representation in robotics.  The most common map is the occupancy grid map. In a grid map, the environment is discretized into squares of arbitrary resolution, e.g. 1cm x 1cm, on which obstacles are marked. In a probabilistic occupancy grid, grid cells can also be marked with the probability that they contain an obstacle. This is particularly important when the position of the robot that senses an obstacle is uncertain. Disadvantages of grid maps are their large memory requirements as well as computational time to traverse data structures with large numbers of vertices. A solution to the latter problem are topological maps that encode entire rooms as vertices and use edges to indicate navigable connections between them. There is no silver bullet, and each application might require a different solution that could be a combination of different map types.
  • 7. Path-Planning Algorithms  The problem to find a “shortest” path from one vertex to another through a connected graph is of interest in multiple domains, most prominently in the internet, where it is used to find an optimal route for a data packet. The term “shortest” refers here to the minimum cumulative edge cost, which could be physical distance (in a robotic application), delay (in a networking application) or any other metric that is important for a specific application.
  • 8. One of the earliest and simplest algorithms is Dijkstra’s algorithm. Starting from the initial vertex where the path should start, the algorithm marks all direct neighbours of the initial vertex with the cost to get there. It then proceeds from the vertex with the lowest cost to all of its adjacent vertices and marks them with the cost to get to them via itself if this cost is lower. Once all neighbours of a vertex have been checked, the algorithm proceeds to the vertex with the next lowest cost. A more detailed description of this algorithm is provided here . Once the algorithm reaches the goal vertex, it terminates and the robot can follow the edges pointing towards the lowest edge cost.
  • 9. Dijkstra’s algorithm for robotic path planning,
  • 10. Illustration of A* planning,
  • 11. A* and D* become computationally expensive when either the search space is large, e.g., due to a fine-grain resolution required for the task, or the dimensions of the search problem are high, e.g. when planning for an arm with multiple degrees of freedom. A more recent development known as Rapidly-Exploring Random Trees (RRT) addresses this problem by using a randomized approach that aims at quickly exploring a large area of the search space with iterative refinement. For this, the algorithm selects a random point in the environment and connects it to the initial vertex.  Subsequent random points are then connected to the closest vertex in the emerging graph. The graph is then connected to the goal node, whenever a point in the tree comes close enough given some threshold. Although generally a coverage algorithm (see also below), RRT can be used for path- planning by maintaining the cost-to-start on each added point, and biasing the selection of points to occasionally falling close to the goal. RRT can also be used to take into account non-holonomic contraints of a specific platform when generating the next random way-point. One way to implement this is to calculate the next random point by applying random values to the robot’s actuators and use the forward kinematics to calculate the next point. Most recently, variations of RRT have been proposed that will eventually find an optimal solution. Nevertheless, although RRT quickly finds some solution, smooth paths usually require additional search algorithms that start from an initial estimate provided by RRT.
  • 13. Robot embodiment  In order to deal with the physical embodiment of the robot, which complicates the path-planning process, the robot is reduced to a point-mass and all the obstacles in the environment are grown by half of the longest extension of the robot from its centre. This representation is known as configuration space as it reduces the representation of the robot to its x and y coordinates in the plane. Most planning algorithms also do not consider the orientation of the robot. If this is desired, an additional dimension needs to be introduced into the configuration space.
  • 14. Other path-planning applications  Once the environment has been discretized into a graph we can employ other algorithms from graph theory to plan desirable robot trajectories  For example, floor coverage can be achieved by performing a depth-first search (DFS) or a breadth-first-search (BFS) on a graph where each vertex has the size of the coverage tool of the robot.