Static and Dynamic Path Planning Using Incremental Heuristic Search

Path planning is an important component in any highly automated vehicle system. In this report, the general problem of path planning is considered first in partially known static environments where only static obstacles are present but the layout of the environment is changing as the agent acquires new information. Attention is then given to the problem of path planning in dynamic environments where there are moving obstacles in addition to the static ones. Specifically, a 2D car-like agent traversing in a 2D environment was considered. It was found that the traditional configuration-time space approach is unsuitable for producing trajectories consistent with the dynamic constraints of a car. A novel scheme is then suggested where the state space is 4D consisting of position, speed and time but the search is done in the 3D space composed by position and speed. Simulation tests shows that the new scheme is capable of efficiently producing trajectories respecting the dynamic constraint of a car-like agent with a bound on their optimality.Comment: Internship Repor

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 for a 7-DOF Robot Arm

Klanke S, Lebedev DV, Haschke R, Steil JJ, Ritter H. Dynamic Path Planning for a 7-DOF Robot Arm. In: Int. Conf. Intelligent Robots and Systems. IEEE; 2006: 3879-3884

A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles

Dynamic path planning is one of the key procedures for unmanned aerial vehicles (UAV) to successfully fulfill the diversified missions. In this paper, we propose a new algorithm for path planning based on ant colony optimization (ACO) and artificial potential field. In the proposed algorithm, both dynamic threats and static obstacles are taken into account to generate an artificial field representing the environment for collision free path planning. To enhance the path searching efficiency, a coordinate transformation is applied to move the origin of the map to the starting point of the path and in line with the source-destination direction. Cost functions are established to represent the dynamically changing threats, and the cost value is considered as a scalar value of mobile threats which are vectors actually. In the process of searching for an optimal moving direction for UAV, the cost values of path, mobile threats, and total cost are optimized using ant optimization algorithm. The experimental results demonstrated the performance of the new proposed algorithm, which showed that a smoother planning path with the lowest cost for UAVs can be obtained through our algorithm. (PDF) A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles. Available from: https://www.researchgate.net/publication/328765418_A_New_Dynamic_Path_Planning_Approach_for_Unmanned_Aerial_Vehicles [accessed Nov 20 2018]

Dynamic Path Planning for Mobile Robots with Cellular Learning Automata

In this paper we propose a new approach to path planning for mobile robots with cellular automata and cellular learning automata. We divide the planning into two stages. In the first stage, global path planning is performed by cellular automata from an initial position to a goal position. In this stage, the minimum distance is computed. To compute the path, we use a particular two-dimensional cellular automata rule. The process of computation is performed using simple arithmetic operations, hence it can be done efficiently. In the second stage, local planning is used to update the global path. This stage is required to adapt to changes in a dynamic environment. This planning is implemented using cellular learning automata to optimize performance by collecting information from the environment. This approach yields a path that stays near to the obstacles and therefore the total time and distance to the goal can be optimized

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

Dynamic path planning for reconfigurable rovers using a multi-layered grid

Autonomy on rovers is desirable in order to extend the traversed distance, and therefore, maximize the number of places visited during the mission. It depends heavily on the information that is available for the traversed surface on other planet. This information may come from the vehicle’s sensors as well as from orbital images. Besides, future exploration missions may consider the use of reconfigurable rovers, which are able to execute multiple locomotion modes to better adapt to different terrains. With these considerations, a path planning algorithm based on the Fast Marching Method is proposed. Environment information coming from different sources is used on a grid formed by two layers. First, the Global Layer with a low resolution, but high extension is used to compute the overall path connecting the rover and the desired goal, using a cost function that takes advantage of the rover locomotion modes. Second, the Local Layer with higher resolution but limited distance is used where the path is dynamically repaired because of obstacle presence. Finally, we show simulation and field test results based on several reconfigurable and non-reconfigurable rover prototypes and a experimental terrain

Dynamic path planning of initially unknown environments using an RGB-D camera

In this thesis an RGB-D camera was used with the goal to perform dynamic path planning in an initially unknown environment. Depth data from an RGB-D camera together with a discretizising algorithm is continuously used for maintaining an obstacle map of the environment which within the path planning algorithm D* Lite [S. Koening, 2005] is performed on the flight. Experiments were conducted on two different systems, on Combine’s hexacopter and on a Gantry Tau robot at the Robot Lab of the Department of Automatic Control, LTH. On Combine’s hexacopter different tracking algorithms such as ICP, Translation Approximation and SDF where evaluated for 3D positioning while the robots internal positioning where used on the Gantry Tau robot. For discretization purposes we compare the use of Box Approximation and Signed Distance Function (SDF) for creating the obstacle map