MEV 442: Introduction to Robotics - Module 3 INTRODUCTION TO ROBOT PATH PLANNING

Transcription

1 MEV 442: Introduction to Robotics - Module 3 INTRODUCTION TO ROBOT PATH PLANNING

2 THE PATH PLANNING PROBLEM The robot should find out a path enables the continuous motion of a robot from an initial configuration to a final configuration without colliding with any obstacles present in its environment The fundamental tasks in path planning Obstacle avoidance Path optimisation The two path planning approaches are Global path planning Local path planning

3 GLOBAL PATH PLANNING APPROACH When the environment is completely known and static. ti The algorithm generates a complete path from the start point to the destination point before the robot starts its motion. Off-line path planning

4 CONFIGURATION SPACE The concept of configuration space is used to provide a complete description of the geometry of a robot and its workspace The configuration space, or C-space is the space of all possible configurations of the robot in the workspace. A robot of complex geometry can be mapped to a single point in the C-space The dimension of thec-space is equal to the number of degrees of freedom (if environment is static)

5 LOCAL PATH PLANNING APPROACH When the environment is not completely known Path planning is done while robot moving The algorithm is capable changing the path while moving with respect to the changes in environment Suitable for on-line implementation

6 PATH PLANNING METHODS Widely used path planning approaches include Cell decomposition Roadmaps Potential field Heuristic methods Soft computing techniques

7 CELL DECOMPOSITION METHODS The planning space is broken up into discrete, non- overlapping regions which are subsets of the configuration space and whose union is makes up exactly the entire c- space. A graph is formed in which each cell is adjacent to other cells. The methods for traversing from one cell to adjacent cells is called the connectivity graph. A planner searches through the connectivity graph and the path generated is a sequence of cells the robot should traverse to reach the goal

8 CELL DECOMPOSITION METHODS In approximate cell decomposition approach the free space is divided using some predefined regular shapes whose union is a subset of free space. The figure on top shows the exact decomposition of free space using trapezoidal cells and different collision free paths possible The bottom figures shows rectangular cells with eight connectivity and four connectivity

9 ROADMAPS Captures the connectivity of the robot s free space in a graph of partially connected low-dimensional curves called a roadmap The roadmaps are used as a set of standarized paths for robot navigation A path is generated by connecting the initial and final robot positions or configurations to points in the roadmap and subsequently connecting these points by a graph search of the roadmap Roadmap algorithms include visibility graph, Voronoi diagram, silhouette, subgoal network and probabilistic roadmap

10 PROBABILISTIC ROADMAP In the case of high dimensional confihuration spaces (high DOF robots), the construction of a complete roadmap is practically impossible In such cases Probabilistic RoadMap (P RM ) method, which creates a roadmap by randomly sampling configurations from the configuration space is used to find out an existing path between a given start and goal configuration If these configurations are collision-free, they are added as nodes to the roadmap and are connected The validity of connection is verified by collisionchecking of intermediate configurations

11 POTENTIAL FIELD METHODS In PFM the robot is assumed as a particle that moves in the configuration space under the influence of an artificial force field Target location exerts a force that attracts the particle Fatt, the obstacles exert repulsive forces Frep At each time t, the motion is computed to follow the direction of the artificial force induced by the sum of both potentials Ftot (qt ) = Fatt (qti ) + Fre(qt)

12 PFM Local minima problem The robot stops in an configuration in which the total force is zero and the target still attracts the goal

13 SOFT COMPUTING TOOLS A collection of tools shared by artificial neural network, fuzzy logic, genetic algorithm etc. are known as soft computing tools These tools are used independently or jointly in collision avoidance problems, mostly in sensor based navigation ANN and fuzzy logic are generally used for decision making where a robot has to decide its path based on ambiguous sensor data

14 MEV 442- Module 3 Robot Programming additional Slides Dr. Santhakumar Mohan Assistant Professor, NITC.

15 The levels of Robot Programming Teach by Showing Lead through method Teach pendant Explicit Robot Programming g Languages g (RPLs) Specialized manipulation languages Robot library for an existing computer language g Robot library for a new general purpose language Task level programming languages Dr. SanthaKumar Mohan, NITC. 2

16 Requirements of a robot programming World modelling language Motion specification Flow of execution Programming environment Sensor integration Dr. SanthaKumar Mohan, NITC. 3

17 Problems peculiar to robot programming languages Internal world model versus external reality Context sensitivity Bottom up programming Error recovery Error detection Dr. SanthaKumar Mohan, NITC. 4

18 Off-Line Programming (OLP) systems Central issues in OLP systems User interface 3-D modelling Kinematic emulation Path planning emulation Dynamic emulation Multi-process simulation Simulation of sensors Language translation to target system Work cell calibration Dr. SanthaKumar Mohan, NITC. 5

19 Automatic subtasks in OLP systems Automatic robot placement Collision avoidance and path optimization Automatic planning of coordinated motion Force-control simulation Automatic scheduling Automatic assessment of errors and tolerances Dr. SanthaKumar Mohan, NITC. 6