Towards Randomized Path Planning: A Tutorial (original) (raw)
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An Efficient Random Walk Strategy for Sampling based Robot Motion Planners
Abstract. Sampling based planners have been successful in path planning of robots with many degrees of freedom, but still remains ineffective when the configuration space has a narrow passage. We present a new technique based on a random walk strategy to generate samples in narrow regions quickly, thus improving efficiency of Probabilistic Roadmap Planners. The algorithm substantially reduces instances of collision checking and thereby decreases computational time. The method is powerful even for cases where the structure of the narrow passage is not known, thus giving significant improvement over other known methods.
Randomized path planning with preferences in highly complex dynamic environments
Robotica, 2013
SUMMARYIn this paper we consider the problem of planning paths for articulated bodies operating in workplaces containing obstacles and regions with preferences expressed as degrees of desirability. Degrees of desirability could specify danger zones and desire zones. A planned path should not collide with the obstacles and should maximize the degrees of desirability. Region desirability can also convey search-control strategies guiding the exploration of the search space. To handle desirability specifications, we introduce the notion of flexible probabilistic roadmap (flexible PRM) as an extension of the traditional PRM. Each edge in a flexible PRM is assigned a desirability degree. We show that flexible PRM planning can be achieved very efficiently with a simple sampling strategy of the configuration space defined as a trade-off between a traditional sampling oriented toward coverage of the configuration space and a heuristic optimization of the path desirability degree. For path pl...
Randomized robot navigation algorithms
1996
We consider the problem faced by a mobile robot that has to reach a given target by traveling through an unmapped region in the plane containing oriented rectangular obstacles. We assume the robot has no prior knowledge about the positions or sizes of the obstacles, and acquires such knowledge only when obstacles are encountered. Our goal is to minimize the distance the robot must travel, using the competitive ratio as our measure.
Motion planning: Recent developments
A key trait of an autonomous robot is the ability to plan its own motion in order to accomplish specified tasks. Often, the objective of motion planning is to change the state of the world by computing a sequence of admissible motions for the robot. For example, in the path planning problem, we compute a collision-free path for a robot to go from an initial position to a goal position among static obstacles. This is the simplest type of motion planning problems; yet it is 1 provably hard computationally . Sometimes, instead of changing the state of the world, our objective is to maintain a set of constraints on the state of the world (e.g., following a target and keeping it in view), or to achieve a certain state of knowledge about the world (e.g., exploring and mapping an unknown environment).
RRT-connect: An efficient approach to single-query path planning
2000
A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two Rapidly-exploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.
2011
Existing sampling-based robot motion planning methods are often inefficient at finding trajectories for kinodynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines elements from several prior approaches and may incorporate a cost model based on the LQG-MP framework to estimate the probability of collision under uncertainty in control and sensing. We compare the performance of EG-RRT with several prior approaches on challenging sample problems. Results suggest that EG-RRT offers significant improvements in performance.
Mobile Robot Path Planning by RRT* in Dynamic Environments
International Journal of Intelligent Systems and Applications, 2015
Robot navigation is challenging for mobile robots technology in environments with maps. Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments. The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm. Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances.
Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions
International Journal of Advanced Computer Science and Applications, 2016
Optimal path planning refers to find the collision free, shortest, and smooth route between start and goal positions. This task is essential in many robotic applications such as autonomous car, surveillance operations, agricultural robots, planetary and space exploration missions. Rapidly-exploring Random Tree Star (RRT*) is a renowned sampling based planning approach. It has gained immense popularity due to its support for high dimensional complex problems. A significant body of research has addressed the problem of optimal path planning for mobile robots using RRT* based approaches. However, no updated survey on RRT* based approaches is available. Considering the rapid pace of development in this field, this paper presents a comprehensive review of RRT* based path planning approaches. Current issues relevant to noticeable advancements in the field are investigated and whole discussion is concluded with challenges and future research directions.
Sampling based path planning for high DoF manipulators without goal configuration
IFAC Proceedings Volumes, 2011
This paper presents new methods for motion planning for manipulators needing to move in a difficult environment with high probability of collisions. The paper uses the Rapidly exploring Random Trees (RRT) idea, Nd-Cuboid domains for reducing the exploration and produces new methods for path planning without having goal configuration. One method is based on pseudo-inverse Jacobian (J +) with weighted least-norm(WLN)(named here as J W LN) method which is used instead of normal J + for better behavior of the robot arm around joint limits. The second algorithm uses inverse kinematics, which help to drive the manipulator to a goal pose. The third one generates neighbor cells in Cartesian space and selects the best one for random expansion. Inverse kinematics are used in order to create a configuration for a cell. All variations are forward directed trees, since goal configuration is not present. The experiment results, done in a 7 degree-of-freedom (DoF) robot arm, show that all variations provide significant results, with the advantage to use them in grasping scenarios.