Obstacle Avoidance: Static Obstacles

Shanmugavel, Madhavan; Tsourdos, Antonios; White, Brian

doi:10.1002/9780470686652.eae555

Cited by 2 publications

(1 citation statement)

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“…Numerous existing researches are focused on the development of navigation algorithms in static environments (see e.g. [61,104,115,163]), which have limited applications in many real world scenarios where the obstacle are dynamically moving. The problem of safe navigation in dynamic environments poses a serious challenge in the field of mobile robotics.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

2014

Preprint

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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-ii sented in this paper. A constructive and easy-to-implement decentralised control is proposed for a formation building of a group of random positioned objects. Furthermore, the problem of formation building with anonymous objects is addressed. This randomised decentralised navigation algorithm achieves the convergence to a desired configuration with probability 1. Contents Contents iii List of Figures vii List of Tables xiiiList of Tables 5.1 Navigation time (in sec) for BINA, ENA and NAIER over 25 experiment runs (obstacles moving with constant speeds) . . . . . . . . . . 5.2 Navigation time (in sec) for BINA, ENA and NAIER over 20 experiment runs (obstacles moving with non-linear velocities

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Section: Introductionmentioning

confidence: 99%