Path Planning Method Design for Mobile Robots

[[abstract]]In this paper, a path planning method for autonomous mobile robots in a known indoor environment is proposed. A traditional A* algorithm modified by a weighted cost function is proposed. The factors of distance and safety are considered simultaneously in the cost function so that the path planning can let the mobile robot reach its goal safely and quickly. Some simulation results are presented to illustrate the proposed method has a good path planning for mobile robots. The proposed method has also been implemented and tested on a real mobile robot. The experiment results illustrate that the proposed method can let the autonomous mobile robot have a safe path-planning in a known environment.[[sponsorship]]The Society of Instrument and Control Engineers[[sponsorship]]The Society of Instrument and Control Engineers (SICE)[[conferencetype]]國際[[conferencedate]]20110913~20110918[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Wavefront Propagation and Fuzzy Based Autonomous Navigation

Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments

Path Planning, Motion Control and Obstacle Detection of Indoor Mobile Robot

In this paper, A* path planning algorithm has been represented for a mobile robot to be able to follow a constructed path from its current position to a specified goal within its environment. To ensure that the mobile robot follow the constructed path by path planning algorithm, a motion control algorithm has been built. In the same time, to detect static obstacles and avoid collision with them, an obstacle detection algorithm has been used as a final algorithm that will be used as a part of the whole system to give the robot the ability to move from its initial known position to a specific goal in an optimum way.

Modified spline-based navigation: Guaranteed safety for obstacle avoidance

© 2017, Springer International Publishing AG. Successful interactive collaboration with a human demands mobile robots to have an advanced level of autonomy, which basic requirements include social interaction, real time path planning and navigation in dynamic environment. For mobile robot path planning, potential function based methods provide classical yet powerful solutions. They are characterized with reactive local obstacle avoidance and implementation simplicity, but suffer from navigation function local minima. In this paper we propose a modification of our original spline-based path planning algorithm, which consists of two levels of planning. At the first level, Voronoi-based approach provides a number sub-optimal paths in different homotopic groups. At the second, these paths are optimized in an iterative manner with regard to selected criteria weights. A new safety criterion is integrated into both levels of path planning to guarantee path safety, while further optimization of a safe path relatively to other criteria is secondary. The modified algorithm was implemented in Matlab environment and demonstrated significant advantages over the original algorithm

Socially aware path planning for mobile robots

© 2014 Cambridge University Press. Human-robot interaction is an emerging area of research where a robot may need to be working in human-populated environments. Human trajectories are generally not random and can belong to gross patterns. Knowledge about these patterns can be learned through observation. In this paper, we address the problem of a robot's social awareness by learning human motion patterns and integrating them in path planning. The gross motion patterns are learned using a novel Sampled Hidden Markov Model, which allows the integration of partial observations in dynamic model building. This model is used in the modified A∗ path planning algorithm to achieve socially aware trajectories. Novelty of the proposed method is that it can be used on a mobile robot for simultaneous online learning and path planning. The experiments carried out in an office environment show that the paths can be planned seamlessly, avoiding personal spaces of occupants