The tangent bug algorithm is actually the imroved version of Bug1 and Bug2 algorithms. Unlike these methods, tangent bug algorithm depends on the existence of a range sensor that is mounted on the point robot in the map. By only investigating the output ot this range sensor, and including the knowledge of the robot's current pose and goal's pose, the robot plans actions to reach to the goal.

This web page consists of information about the tangent bug algorithm and its implementation.

This course is a seminar-style hands-on survey of approaches to control and learning in single and multi-robot systems. We will read original seminal papers that track the development of the field and overview the different state-of-the-art approaches to mobile robot control, including reactive, hybrid, and behavior-based based systems. The discussion will focus on the issues of resolving the fundamental conflict between thinking and acting, i.e., high-level deliberation and real-time control. Different approaches and robot control architectures for addressing this issue will be covered and discussed. In the second part of the course we will discuss scaling up robot control to multi-robot systems and swarms of robots. The control architectures discussed in the first part of the semester will be revisited in the context of scaling up to distributed systems. Finally, we will address adaptation and learning in single and multi-robot systems, and deal with the many challenges those problems present. Several other relevant topics will be covered at least briefly, including biological inspirations for robot control and philosophical foundations. All topics will be illustrated with implemented systems and demonstrated with videos.

18/April/2007

In this report we discuss our implementation of a local path planning algorithm based on virtual potential field described in [1]. The algorithm uses virtual forces to avoid being trapped in a local minimum. Simulation and experiments are performed, and compared to the results presented in the paper. They show good performance and ability to avoid the local minimum problem in most of the cases.