Dynamic Path Planning and Replanning for Mobile Robots using RRT*

It is necessary for a mobile robot to be able to efficiently plan a path from its starting, or current, location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing it's path. A method of dynamic replanning using RRT* is presented. The robot will modify it's current plan when an unknown random moving obstacle obstructs the path. Various experimental results show the effectiveness of the proposed method

Dynamic Path Planning and Replanning for Mobile Robot Team Using RRT*

It is necessary for a mobile robot to be able to efficiently plan a path from itsstarting or current location to a desired goal location. This is a trivial task when theenvironment is static. However, the operational environment of the robot is rarelystatic, and it often has many moving obstacles. The robot may encounter one, ormany, of these unknown and unpredictable moving obstacles. The robot will need todecide how to proceed when one of these obstacles is obstructing it's path. A methodof dynamic replanning using RRT* is presented. The robot will modify its currentplan when an unknown random moving obstacle obstructs the path. In multi-robotscenarios it is important to efficiently develop path planning solutions. A methodof node sharing is presented to quickly develop path plans for a multi-robot team.Various experimental results show the effectiveness of the proposed methods

Adaptive dynamic path re-planning RRT algorithms with game theory for UAVs

The main aim of this paper is to describe an adaptive re-planning algorithm based on a RRT and Game Theory to produce an efficient collision free obstacle adaptive Mission Path Planner for Search and Rescue (SAR) missions. This will provide UAV autopilots and flight computers with the capability to autonomously avoid static obstacles and No Fly Zones (NFZs) through dynamic adaptive path replanning. The methods and algorithms produce optimal collision free paths and can be integrated on a decision aid tool and UAV autopilots

Real-Time Long Range Trajectory Replanning for MAVs in the Presence of Dynamic Obstacles

Real-time long-range local planning is a challenging task, especially in the presence of dynamics obstacles. We propose a complete system which is capable of performing the local replanning in real-time. Desired trajectory is needed in the system initialization phase; system starts initializing sub-components of the system including point cloud processor, trajectory estimator and planner. Afterwards, the multi-rotary aerial vehicle starts moving on the given trajectory. When it detects obstacles, it replans the trajectory from the current pose to pre-defined distance incorporating the desired trajectory. Point cloud processor is employed to identify the closest obstacles around the vehicle. For replanning, Rapidly-exploring Random Trees (RRT*) is used with two modifications which allow planning the trajectory in milliseconds scales. Once we replanned the desired path, velocity components(x,y and z) and yaw rate are calculated. Those values are sent to the controller at a constant frequency to maneuver the vehicle autonomously. Finally, we have evaluated each of the components separately and tested the complete system in the simulated and real environments