Collision free autonomous navigation and formation building for non-holonomic ground robots

The primary objective of a safe navigation algorithm is to guide the object from its current position to the target position while avoiding any collision with the enroute obstacles, and the appropriate obstacle avoidance strategies are the key factors to ensure safe navigation tasks in dynamic environments. The basic requirement for an appropriate obstacle avoidance strategy is to sense or detect obstacles and make proper decisions when the obstacles are nearby. By fulfilling the basic requirement, the more advanced obstacle algorithms should have other additional features. In this report, three different obstacle avoidance strategies for safe navigation in dynamic environments have been presented. All of them are applicable in the non-holonomic systems by which motions of many objects can be described. The biologically-inspired navigation algorithm (BINA) is efficient in terms of avoidance time, it is also simple and easy to compute. The equidistant based navigation algorithm (ENA) is able to achieve navigation task with in uncertain dynamic environments, and it is suitable for a variety of situations due to its flexibility. The navigation algorithm algorithm based on an integrated environment representation (NAIER) allows the object to seek a safe path through obstacles in unknown dynamic environment in a human-like fashion and it is very efficient in numerous particular scenarios where other algorithm are found inefficient or even impossible to solve. The performances and features of the proposed navigation algorithms are confirmed by extensive simulation results and experiments with a real non-holonomic mobile robot. Furthermore, the performance of these algorithms are compared with each other in various aspects. The algorithms have been implemented on two real control systems: intelligent wheelchair and robotic hospital bed. The performance of the proposed algorithms with SAM and Flexbed demonstrate their capabilities to achieve navigation tasks in complicated real time scenarios. The proposed algorithms are easy to be implemented in real time and costly efficient. An extra study on networked multi-robots formation building algorithm is pre-