teaching_robots_navigation

Navigation

• navigation and planning

• relative to other locations

• path planning

• self localisation

• map is a mapping from the real world to some internal representation

• PID controller Proportional Integrative and Derivative

• odometry

• these get translated to right and left wheel velocities

• this is what guides the robot from where it is to where it needs to go

• generate a series of x, y values to guide a robot

• self localization

• path planner

• path planning is different from trajectory planning

• aerial drones have 6 degrees of freedom pitch, roll and yaw

• balance shorter term goals with long term for path planning

• path planning is a subset of trajectory planning

• some path planning techniques

• reinforcement learning: control strategies that work get reinforced

What is required

• a global map
• robot must know where it is in the map
• start
• goal

Challenge question

• how does a submarine navigate underwater without GPS?

Occupancy map

• exploration of environment

• position estimation using robot pose

• sensor values interpret (LIDAR, sonar, vision, etc.)

• integration of sensor values into map (based on distance from robot and pose estimation of robot)

• what happens to table? robot can go underneath. the choice is with you

• divide into grids

• binary (occupied/not occupied) within each cell

Artificial potential fields

• force that will push it away

\[V_{Left} = V_{des} - \frac{B \theta_{des}}{2}\] \[V_{Right} = V_{des} + \frac{B \theta_{des}}{2}\] \[V_{des} = k F_{linear}\] \[\omega_{des} = k F_{angular}\]

B = length between the wheels

• In the APF approach for mobile robot navigation, the goal and obstacles act like charged surfaces and the total potential creates the imaginary force on the robot.
• This imaginary force attracts the robot towards the goal and keeps it away from the obstacle as shown above.

• The robot follows the negative gradient of the total potential field, effectively moving along the path of least resistance, avoiding the obstacle and seeking the target point.

• 🤔 ❓disadvantage

• need to know map before you setup potential field

• what will happen when you change target

• all stored in memory

• number of cells required ; how will this scale as you go to bigger environments

• 🎥 video of delivery robot

• 🤔 ❓how does this delivery robot navigate?

• 🎥 video explaining how delivery robots navigate using artificial magnets

Grid based techniques

• A* algorithm

• Dijkstra

• compute path that avoids obstacles

Next resource

• now see SLAM resource

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