This paper proposes a novel obstacle avoidance algorithm for autonomous mobile robot control. The proposed approach brings a solution to the problem of robot traversal in critical shaped environments and offers several advantages compared to the reported approaches. The algorithmic approach, named as, Intelligent Follow the Gap Method (IFGM) is based on improved Intelligent Bug Algorithm (IBA) and Follow the Gap Method (FGM). The robot field of view is taken into consideration. The IBA avoids obstacles by following their edge and scanning the path to destination, thus making the approach goal-oriented avoiding local minimum problem. To characterize the performance of IFGM, various scenarios of obstacles are considered. These scenarios range from having obstacles defined by simple and symmetrical shapes to critical shaped obstacles. The simulation results demonstrate that the algorithm results in safer and smoother trajectories in the presence of obstacles. It offers fast convergence and does not suffer from local minima. Finally, the performance comparison of the proposed algorithm with that of the reported approaches in terms of distance-time plots confirms the efficacy of the presented approach. The proposed algorithm lends itself to future implementations in the navigation of mobile and industrial robots, especially in applications exhibiting crucial time and critical obstacles including disaster management, spy, elderly people assistance and soccer games.

This research proposed an intelligent obstacle avoidance algorithm to navigate an autonomous mobile robot. The presented Intelligent Bug Algorithm (IBA) over performs and reaches the goal in relatively less time as compared to existing Bug algorithms. The improved algorithm offers a goal oriented strategy by following smooth and short trajectory. This has been achieved by continuously considering the goal position during obstacle avoidance. The proposed algorithm is computationally inexpensive and easy to tune. The paper also presents the performance comparison of IBA and reported Bug algorithms. Simulation results of robot navigation in an environment with obstacles demonstrate the performance of the improved algorithm.

This paper presents kinematic model of two configurations of a wheeled mobile robot. Two-wheeled robot with castor and four-wheeled robot are considered for modeling. Kinematic equations, modeled in MATLAB/Simulink, represent the position and angle of the mobile robots. Simulation results illustrate the actual trajectory followed by the 'soft' robot. The potential use of the derived kinematic model is two folds; in research as well as in academia. The model can be employed to test and validate advanced algorithms related with mobile robots e.g. for collision avoidance, path-planning, navigation etc while in an educational environment, it can assist students to study the behavior and nonholonomic constraints of their robots prior to their fabrication for competitions. As a case study to demonstrate the application of the developed model, the present research proposed a novel collision avoidance algorithm named as Intelligent Bug Algorithm (IBA). Preliminary comparative results dictate that IBA outperforms the reported Bug algorithms.

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