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Exteroceptive sensing and Reactive-based Obstacle Avoidance

Motivations and Goals
Become familiar with direct sensory-motor control of the robot
Become familiar with collision avoidance and be aware of robot sensors
Be able to implement reactive-based control of mobile robots
Learn the principle of the occupancy grid map construction
Task t1b-react - Implement collision avoidance system for autonomous reactive-based navigation of hexapod walking robot

Reactive Navigation and Obstacle Avoidance Using Sensory-Motor Feedback

Reactive navigation assumes only local knowledge about the environment. For obstacle avoidance it uses only two motion primitives:

  • Follow a wall (i.e. avoid the obstacle)
  • Move towards the goal

An example of reactive obstacle avoidance is the bug algorithm, which has three variants

  • Bug algorithm 1
  • Bug algorithm 2
    • Head towards the goal along the straight line $m$ connecting start and goal
    • Circumnavigate obstacles until you encounter the $m$ line again
    • Continue towards the goal along the $m$ line
    • Repeat

AI Model of Braitenberg Vehicles

Braitenberg vehicles are very simple autonomous agents that use basic sensory-motor connections to produce seemingly cognitive behaviors 1). By adjusting the sensory-motor connections, the robots exhibit different behavior. The vehicle has a differential steering and two sensors at the front of the robot capable of sensing the quantity of stimuli. Each sensor is directly connected to the actuator using sensory-motor connections based either on inhibition or excitation, which gives four basic behaviors.

Vehicle 2a Vehicle 2b Vehicle 3a Vehicle 3b
breitenberg_2a.jpg breitenberg_2b.jpg breitenberg_3a.jpg breitenberg_3b.jpg
Connection excitatory excitatory inhibitory inhibitory
Nick Fear Aggression Love Explorer
Properties Avoids the stimuli Intercepts the stimuli Come to rest facing the stimuli Come to rest facing away from the stimuli

A combination of individual vehicle types is referred to as Vehicle 3c that exhibit in a complex environment with several sources of stimulus complex and dynamic behavior that resemble a system of values 2) Adding a non-linear activation function to the sensors in Vehicle 4 may produce even more sophisticated behaviors.

AI Model of Braitenberg Vehicles in Collision Avoidance

The robot senses the distance to the obstacles in front of it. Let the distance to the closest obstacle at the left and right halfplanes of the field of view represent the robot stimuli. Using the Braitenberg vehicle model, this sensing can be directly translated into the control command. E.g., using Vehicle 2a the robot will avoid obstacles as it turns away from the stimuli, i.e., it fears the obstacles.

courses/crl-courses/redcp/tutorials/reactive.txt · Last modified: 2022/11/25 13:22 by faiglj