Algorithm For Collision-free Navigation Research Articles

Collision avoidance approaches for autonomous mobile robots to tackle the problem of pedestrians roaming on campus road

A novel algorithm for collision-free navigation of a mobile robot on the campus road with pedestrian roaming is described in this paper. Proposed method uses the RGB-D depth sensor, utilizing optical flow estimation and object detection, to predict pedestrian locations. Given these environmental uncertainties, we present a Velocity Obstacle (VO) algorithm based on mobility rules to calculate the velocity has been presented. It is proposed to use the Markov Decision Process (MDP) for decision-making (to maneuver the robot whenever it approaches the target). The proposed algorithm is a hybrid combination of deep learning and model-based techniques and provides better results in terms of navigation time and collision avoidance success rate than conventional algorithms. The real-time performance of the proposed algorithm is highlighted using a real-world dynamic scenario for an up-to-date kid ride car that has been redesigned to work as an autonomous mobile robot.

Seeking a path through the crowd: Robot navigation in unknown dynamic environments with moving obstacles based on an integrated environment representation

We present a novel algorithm for collision free navigation of a non-holonomic robot in unknown complex dynamic environments with moving obstacles. Our approach is based on an integrated representation of the information about the environment which does not require to separate obstacles and approximate their shapes by discs or polygons and is very easy to obtain in practice. Moreover, the proposed algorithm does not require any information on the obstacles’ velocities. Under our navigation algorithm, the robot efficiently seeks a short path through the crowd of moving or steady obstacles. A mathematically rigorous analysis of the proposed approach is provided. The performance of the algorithm is demonstrated via experiments with a real robot and extensive computer simulations.

A simple biologically inspired algorithm for collision-free navigation of a unicycle-like robot in dynamic environments with moving obstacles

SUMMARYWe present a simple biologically inspired strategy for the navigation of a unicycle-like robot towards a target while avoiding collisions with moving obstacles. A mathematically rigorous analysis of the proposed approach is provided. The performance of the algorithm is demonstrated via experiments with a real robot and extensive computer simulations.

Interactive Navigation of Heterogeneous Agents Using Adaptive Roadmaps

We present a novel algorithm for collision-free navigation of a large number of independent agents in complex and dynamic environments. We introduce adaptive roadmaps to perform global path planning for each agent simultaneously. Our algorithm takes into account dynamic obstacles and interagents interaction forces to continuously update the roadmap based on a physically-based dynamics simulator. In order to efficiently update the links, we perform adaptive particle-based sampling along the links. We also introduce the notion of 'link bands' to resolve collisions among multiple agents. In practice, our algorithm can perform real-time navigation of hundreds and thousands of human agents in indoor and outdoor scenes.