The Integration of Fuzzy Logic System for Obstacle Avoidance Behavior of Mobile Robot

A mobile robot has a capability of sensing its location under uncertain environment, planning a real-time path as well as controlling its steering angle and speed to reach the target location. A robust controller is embedded in mobile robot whilst analyzing the input and output that help it to navigate without colliding with any obstacles. Meanwhile, Fuzzy Logic Controllers (FLC) is an intelligent technique that proves to be the one of the most reliable controllers that suits well for nonlinear system like robot due to the simple control based on user input without any prior knowledge to the mathematical model. In this paper, the Mamdani and Sugeno FLC are developed for a mobile robot. The smoothness and efficiency that generated from these FLC is analyzed based on simulation of Pioneer P3-DX robot in virtual robotic software for single and multirobot environments under static obstacles environment. Simulation results for the Pioneer P3-DX robot shows the Sugeno FLC able to produce smoother path and reach the goal faster than Mamdani FLC

D* lite algorithm based path planning of mobile robot in static Environment

In this paper, we study the path planning for khepera II mobile robot in an unknown environment. The well known heuristic D* lite algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. and to perform efficient re-planning during exploration. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance metric as the cost functio

Contingent task and motion planning under uncertainty for human–robot interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version