Robohub.org
 

Real-time behavior-based control


by
22 February 2011



share this:

Using behavior-based controllers, robots are theoretically able to rapidly react to their environment. This is typically done by having several behaviors, that map sensory input to actuator commands, run concurrently on the robot. A hierarchy then determines which behavior has access to the actuators.

If your robot needs to navigate a room, you might implement a trajectory planning behavior and a simple obstacle avoidance behavior with high priority to avoid any accidents. If the robot only has one processor, then both behaviors might run in “parallel” as threads. However, if one of your behaviors entails heavy processing, it might hog all the CPU power and impeach the high-priority behaviors from being executed at the right time. In the example above, this might lead to the robot crashing into obstacles. One solution consists in increasing the processing power although this might be incompatible with the size and cost constraints of your robot.

As an alternative, Woolley et al. propose a “Real-Time Unified Behavior Framework” to cope with real-time constraints in behavior-based systems. The framework allows time-critical reactive behaviors to be run at a desired time and in a periodic fashion. Instead, demanding processing tasks that are not critical to the safe operation of the robot are executed whenever possible. This is done by moving time-critical behaviors out of the Linux environment (which can not execute real-time tasks) and into an environment managed by a real-time scheduler.

Real-time tasks bypass Linux and run on the real-time scheduler.

Experiments were conducted on a Pioneer P2-AT8 robot equipped with 16 sonars, odometry, a SICK LMS200 laser scanner and a 1294 camera. The robot was programed to follow an orange cone through a hallway while avoiding obstacles. Results show that the robot was able to meet hard real-time constraints while running computationally demanding processes including FastSLAM.




Sabine Hauert is President of Robohub and Associate Professor at the Bristol Robotics Laboratory
Sabine Hauert is President of Robohub and Associate Professor at the Bristol Robotics Laboratory


Subscribe to Robohub newsletter on substack



Related posts :

Robot Talk Episode 145 – Robotics and automation in manufacturing, with Agata Suwala

  20 Feb 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Agata Suwala from the Manufacturing Technology Centre about leveraging robotics to make manufacturing systems more sustainable.

Reversible, detachable robotic hand redefines dexterity

  19 Feb 2026
A robotic hand developed at EPFL has dual-thumbed, reversible-palm design that can detach from its robotic ‘arm’ to reach and grasp multiple objects.

“Robot, make me a chair”

  17 Feb 2026
An AI-driven system lets users design and build simple, multicomponent objects by describing them with words.

Robot Talk Episode 144 – Robot trust in humans, with Samuele Vinanzi

  13 Feb 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Samuele Vinanzi from Sheffield Hallam University about how robots can tell whether to trust or distrust people.

How can robots acquire skills through interactions with the physical world? An interview with Jiaheng Hu

and   12 Feb 2026
Find out more about work published at the Conference on Robot Learning (CoRL).

Sven Koenig wins the 2026 ACM/SIGAI Autonomous Agents Research Award

  10 Feb 2026
Sven honoured for his work on AI planning and search.

Robot Talk Episode 143 – Robots for children, with Elmira Yadollahi

  06 Feb 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Elmira Yadollahi from Lancaster University about how children interact with and relate to robots.

New frontiers in robotics at CES 2026

  03 Feb 2026
Henry Hickson reports on the exciting developments in robotics at Consumer Electronics Show 2026.



Robohub is supported by:


Subscribe to Robohub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence