• Cognition / Reasoning : is the ability to decide what actions are required to achieve a certain goal in a given situation (belief state). decisions ranging from what path to take to what information on the environment to use.
• Today’s industrial robots can operate without any cognition(reasoning) because their environment is static and very structured.
• In mobile robotics, cognition and reasoning is primarily of geometric nature, such as picking safe path or determining where to go next.
already been largely explored in literature for cases in which complete information about the current situation and the environment exists (e.g. traveling salesman problem).
• Assumption: there exists a good enough map of the environment for navigation.
Topological or metric or a mixture between both. • First step:
Representation of the environment by a road-map (graph), cells or apotential field. The resulting discrete locations or cells allow then to use standard planning algorithms.
• Examples:Visibility GraphVoronoi DiagramCell Decomposition -> Connectivity GraphPotential Field
• Notes:Local minima problem existsProblem becomes more complex if the robot is not considered as a point massIf objects are convex there exists situations where several minimal distances exist → can result in oscillations
Potential Field Path Planning: Extended Potential Field Method
• Additionally a rotation potential field and a task potential field in introduced
• Rotation potential fieldforce is also a function of robot’s orientation to the obstacle
• Task potential fieldFilters out the obstacles that should not influence the robot’s movements,i.e. only the obstaclesin the sector Z in front of the robot are considered
• The goal of the obstacle avoidance algorithms is to avoid collisions with obstacles
• It is usually based on local map• Often implemented as a more or less independent task• However, efficient obstacle avoidance
should be optimal with respect to the overall goalthe actual speed and kinematics of the robotthe on board sensorsthe actual and future risk of collision
• Environment represented in a grid (2 DOF)cell values equivalent to the probability that there is an obstacle
• Reduction in different steps to a 1 DOF histogramcalculation of steering directionall openings for the robot to pass are foundthe one with lowest cost function G is selected
• Notes:Limitation if narrow areas (e.g. doors) have to be passedLocal minimum might not be avoidedReaching of the goal can not be guaranteedDynamics of the robot not really considered
• Bubble = Maximum free space which can be reached without any risk of collision
generated using the distance to the object and a simplified model of the robotbubbles are used to form a band of bubbles which connects the start point with the goal point
• Adding physical constraints from the robot and the environment on the velocity space (v, ω) of the robot
Assumption that robot is traveling on arcs (c= ω / v)Acceleration constraints: Obstacle constraints: Obstacles are transformed in velocity spaceObjective function to select the optimal speed
Obstacle Avoidance: Lane Curvature Velocity Methods (CVM)
• Improvement of basic CVMNot only arcs are consideredlanes are calculated trading off lane length and width to the closest obstacles Lane with best properties is chosen using an objective function
• Note:Better performance to pass narrow areas (e.g. doors)Problem with local minima persists
• The kinematics of the robot is considered by searching a well chosen velocity spacevelocity space -> some sort of configuration spacerobot is assumed to move on arcsensures that the robot comes to stop before hitting an obstacleobjective function is chosen to select the optimal velocity
Obstacle Avoidance: Global Dynamic Window Approach
• Global approach:This is done by adding a minima-free function named NF1 (wave-propagation) to the objective function O presented above.Occupancy grid is updated from range measurements
• Some sort of a variation of the dynamic window approchtakes into account the shape of the robotCartesian grid and motion of circular arcsNF1 plannerreal time performance achievedby use of precalculated table
• Dynamic window approach with global path planingGlobal path generated in advancePath adapted if obstacles are encountereddynamic window considering also the shape of the robotreal-time because only max speed is calculated
• Selection (Objective) Function:
speed = v / vmax
dist = L / Lmax
goal_heading = 1- (α - ωT) / π
• Matlab-Demostart Matlabcd demoJan (or cd E:\demo\demoJan)demoX
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