Showing papers on "Any-angle path planning published in 2006"

Using interpolation to improve path planning: The Field D* algorithm

Abstract: We present an interpolation-based planning and replanning algorithm for generating low-cost paths through uniform and nonuniform resolution grids. Most grid-based path planners use discrete state transitions that artificially constrain an agent's motion to a small set of possible headings (e.g., 0, π/4, π/2, etc.). As a result, even “optimal” grid-based planners produce unnatural, suboptimal paths. Our approach uses linear interpolation during planning to calculate accurate path cost estimates for arbitrary positions within each grid cell and produce paths with a range of continuous headings. Consequently, it is particularly well suited to planning low-cost trajectories for mobile robots. In this paper, we introduce a version of the algorithm for uniform resolution grids and a version for nonuniform resolution grids. Together, these approaches address two of the most significant shortcomings of grid-based path planning: the quality of the paths produced and the memory and computational requirements of planning over grids. We demonstrate our approaches on a number of example planning problems, compare them to related algorithms, and present several implementations on real robotic systems.

Anytime path planning and replanning in dynamic environments

Abstract: We present an efficient, anytime method for path planning in dynamic environments. Current approaches to planning in such domains either assume that the environment is static and replan when changes are observed, or assume that the dynamics of the environment are perfectly known a priori. Our approach takes into account all prior information about both the static and dynamic elements of the environment, and efficiently updates the solution when changes to either are observed. As a result, it is well suited to robotic path planning in known or unknown environments in which there are mobile objects, agents or adversaries

An integrated approach to inverse kinematics and path planning for redundant manipulators

Abstract: We propose a novel solution to the problem of inverse kinematics for redundant robotic manipulators for the purposes of goal selection for path planning. We unify the calculation of the goal configuration with searching for a path in order to avoid the uncertainties inherent to selecting goal configurations which may be unreachable because they currently lie in components of the free configuration space disconnected from the initial configuration. We adopt workspace heuristic functions that implicitly define goal regions of the configuration space and guide the extension of rapidly-exploring random trees (RRTs), which are used to search for these regions. The algorithm has successfully been used to efficiently plan reaching and grasping motions for a humanoid robot equipped with redundant manipulator arms

An Overview of Autonomous Mobile Robot Path Planning Algorithms

Abstract: Determination of a collision free path for a robot between start and goal positions through obstacles cluttered in a workspace is central to the design of an autonomous robot path planning. This paper presents an overview of autonomous mobile robot path planning focusing on algorithms that produce an optimal path for a robot to navigate in an environment. To complete the navigation task, the algorithms will read the map of the environment or workspace and subsequently attempts to create free paths for the robot to traverse in the workspace without colliding with objects and obstacles. Appropriate or correct and suitable algorithms will fulfill its function fast enough, that is, to find an optimal path for the robot to traverse in, even if there are a large number of obstacles cluttered in a complex environment. To achieve this, various approaches in the design of algorithms used to develop an ideal path planning system for autonomous mobile robots have been proposed by many researchers. Simulation and experimental results from previous research shows that algorithms play an important role to produce an optimal path (short, smooth and robust) for autonomous robot navigation and simultaneously it prove that appropriate algorithms can run fast enough to be used practically without time-consuming problem. This paper presents an overview and discusses the strength and weakness of path planning algorithms developed and used by previous and current researchers.

An obstacle-based rapidly-exploring random tree

Abstract: Tree-based path planners have been shown to be well suited to solve various high dimensional motion planning problems. Here we present a variant of the Rapidly-Exploring Random Tree (RRT) path planning algorithm that is able to explore narrow passages or difficult areas more effectively. We show that both workspace obstacle information and C-space information can be used when deciding which direction to grow. The method includes many ways to grow the tree, some taking into account the obstacles in the environment. This planner works best in difficult areas when planning for free flying rigid or articulated robots. Indeed, whereas the standard RRT can face difficulties planning in a narrow passage, the tree based planner presented here works best in these areas

3D Field D: Improved Path Planning and Replanning in Three Dimensions

Abstract: We present an interpolation-based planning and replanning algorithm that is able to produce direct, low-cost paths through three-dimensional environments. Our algorithm builds upon recent advances in 2D grid-based path planning and extends these techniques to 3D grids. It is often the case for robots navigating in full three-dimensional environments that moving in some directions is significantly more difficult than others (e.g. moving upwards is more expensive for most aerial vehicles). Thus, we also provide a facility to incorporate such characteristics into the planning process. Along with the derivation of the 3D interpolation function used by our planner, we present a number of results demonstrating its advantages and real-time capabilities.

Robot Path Planning using Particle Swarm Optimization of Ferguson Splines

Abstract: Robot path planning problem is one of most important task mobile robots. This paper proposes an original approach using a path description by string of cubic splines. Such path is easy executable and natural for car-like robot. Furthermore, it is possible to ensure smooth derivation in connections of particular splines. In this case, the path planning is equivalent to optimization of parameters of splines. An evolutionary technique called Particle Swarm Optimization (PSO) was used hereunder due to its' relatively fast convergence and global search character. Various settings of PSO parameters were tested and the best setting was compared to two classical mobile robot path planning algorithms.

Robot Path Planning Based on Artificial Potential Field Approach with Simulated Annealing

Abstract: The artificial potential field (APF) approach provides a simple and effective motion planning method for practical purpose. However, artificial potential field approach has a major problem, which is that the robot is easy to be trapped at a local minimum before reaching its goal. The avoidance of local minimum has been an active research topic in path planning by potential field. In this paper, we introduce several methods to solve this problem, emphatically, introduce and evaluate the artificial potential field approach with simulated annealing (SA). As one of the powerful techniques for escaping local minimum, simulated annealing has been applied to local and global path planning.

Path Planning using a Dynamic Vehicle Model

Abstract: This paper addresses the problem of path planning using a dynamic vehicle model. Previous works which include a basic kinematic model generate paths that are only realistic at very low speed. By considering higher vehicle speed during navigation, the vehicle can significantly deviate from the planned trajectory. Consequently, the planned path becomes unusable for the mission achievement. So, to bridge a gap between planning and navigation, we propose a realistic path planner based on a dynamic vehicle model.

Safe Path Planning in an Uncertain-Configuration Space using RRT

Abstract: This paper addresses the problem of safe path planning in an uncertain-configuration space. We consider the case of a car-like robot moving in an indoor environment (three-dimensional space). The Extended Kalman Filter (EKF) is a popular way to localize such a robot and to estimate its configuration uncertainty during navigation. Consequently, we supply an EKF with simulated measurements in order to compute realistic uncertainties (in a four-dimensional space) during path planning. We show that our Safe-RRT algorithm, based upon Rapidly-exploring Random Trees (RRT), is an efficient way to find a path in the resulting seven-dimensional uncertain-configuration space.

Smooth Path Planning of a Mobile Robot Using Stochastic Particle Swarm Optimization

Abstract: This paper proposes a new approach using improved particle swarm optimization (PSO) to optimize the path of a mobile robot through an environment containing static obstacles. Relative to many optimization methods that produce nonsmooth paths, the PSO method developed in this paper can generate smooth paths, which are more preferable for designing continuous control technologies to realize path following using mobile robots. To reduce computational cost of optimization, the stochastic PSO (S-PSO) with high exploration ability is developed, so that a swarm with small size can accomplish path planning. Simulation results validate the proposed algorithm in a mobile robot path planning.

Hierarchical map building and planning based on graph partitioning

Abstract: Mobile robot localization and navigation requires a map - the robot's internal representation of the environment. A common problem is that path planning becomes very inefficient for large maps. In this paper we address the problem of segmenting a base-level map in order to construct a higher-level representation of the space which can be used for more efficient planning. We represent the base-level map as a graph for both geometric and appearance based space representations. Then we use a graph partitioning method to cluster nodes of the base-level map and in this way construct a high-level map, which is also a graph. We apply a hierarchical path planning method for stochastic tasks based on Markov decision processes (MDPs) and investigate the effect of choosing different numbers of clusters

Cooperative Multi-Robot Path Planning by Heuristic Priority Adjustment

Abstract: In this paper, a new algorithm for cooperative path planning in a multi-robot system is introduced. The algorithm is specially designed for a distributed system with a large number of robots, all of which should be able to reach their respective goal positions without blocking each other, even if the environment is heavily constricted by obstacles. The basic idea of the approach is based on fully distributed path planning without any central instance, but with the ability of communication and cooperation between the robots. Path planning is done on local sections of the time-space configuration space. Dynamic conflicts between the robots are solved by the heuristic adjustment of priority values. By a continuous enhancement of all plans the algorithm is very robust against dynamic changes and erroneous robot behavior.

Obstacle-avoidance Path Planning for Soccer Robots Using Particle Swarm Optimization

Abstract: Optimal path planning for mobile robots plays an important role in the field of robotics. At present, there are many advanced algorithms used to solve this optimization problem. In this paper a dynamic obstacle-avoidance path planning approach for soccer robot based on particle swarm optimization is presented considering the shape of robot. The mathematical model has been build and a simple and effective coding scheme has been proposed. At the same time, a perfect fitness function has been defined. The experiments indicate the algorithm has low complexity and rapid convergence. The simulation result shows this method of path planning for robot is effectively.

Improved Genetic Algorithms Based Path planning of Mobile Robot Under Dynamic Unknown Environment

Abstract: Aiming at path planning of mobile robot under part of dynamic unknown environment, there are some shortages in the aspects of produce of initial population and the structure of specific genetic operator in current used genetic algorithms. In this paper, using the position feedback and forecast of moving direction of obstacle, we present a new method of robot path planning based on improved genetic algorithms combined with numerical potential field. The problem of path planning and avoiding obstacles under dynamic environment was resolved by path re-planning. The shape of obstacle is not limited, and the research is close to the real work environment of robot. The specific genetic operator, fitness function and real coded were designed in this paper. The simulation instances under multi various complex dynamic environments verify that our algorithm of robot path planning is high efficient, and the operation speed and accuracy are improved. http://www.ieee-icma.org

A solution to simultaneous arrival of multiple UAVs using Pythagorean hodograph curves

Abstract: This paper presents a solution to the problem of simultaneous arrival of a swarm of UAVs by safe and continuously flyable paths. Continuously flyable-paths are generated by satisfying the curvature constraint throughout the path-length. The flyable paths ensure the safety of the UAVs by changing the shape of the flyable-paths by adjusting curvature of the paths. The main idea used in this paper is that a specific type of path is used in the first place for path planning and the shape of the path is varied to meet the multiple constraints. Pythagorean hodograph curves are used for the path planning algorithm. The principle of differential geometry that a planar curve is completely determined by its curvature is used for changing the shape of the path. The multiple constraints are: (i) curvature constraints (ii) minimum-separation-distance and (iii) non-intersection of paths at equal length.

System and method for path based tree matching

Abstract: A system and method for tree matching are provided. The method for tree matching includes: acquiring tree-like structures representing a physical object or model; extracting a path from a first tree-like structure and a path from a second tree-like structure; comparing the paths of the first and second tree-like structures by computing a similarity measurement for the paths; and determining if the paths match based on the similarity measurement.

Support Vector Path Planning

Abstract: This paper describes a unique approach of applying a pattern classification technique to robot path planning. A collision-free path connecting a start and a goal point provides information on the division of the space. In the case of 2D path planning, for example, the path divides the space into two regions. This suggests a dual problem of first dividing the whole space into such two regions and then picking up the boundary as a path. We develop a method of solving this dual problem using support vector machine (SVM). SVM generates a non-linear separating surface based on the margin maximization principle. This property is suitable for the purpose of usual path planning problems, that is, generating a safe and smooth path. The details of the path planning methods in 2D and 3D spaces are described with several planning results. Future possibilities of combining the proposed concept with other path planning methodologies are also discussed.

Dynamic Path Planning for a 7-DOF Robot Arm

Abstract: We present an on-line, robust, and efficient path planner for the redundant Mitsubishi PA-10 arm with 7 degrees of freedom (DOF) in non-stationary environments. Because of the specific kinematic model of the arm, path planning can be first reduced to a redundant 6-DOF problem in a 5D configuration space, which can be further decomposed into two problems: (i) 3D position planning in Cartesian space and (ii) planning in a 3D space composed of two orientation angles and an explicit parameterization of the arms redundancy. Position and orientation planning are interweaving and performed "on-the-fly" without explicit global knowledge of the environment using two instances of the dynamic wave expansion neural network (DWENN), an effective method for path generation in arbitrarily changing environments. The dynamic and explorative nature of the DWENN algorithm allows to treat stationary and dynamic obstacles in a unified manner. Through a number of simulative tests, we show that the planner is capable of reaching both a satisfactory robustness level and real-time performance, as required by many practical applications.

Graph-based path planning for mobile robots

Abstract: In this thesis, questions of navigation, planning and control of real-world mobile robotic systems are addressed. Chapter II contains the first contribution in this thesis, which is a modification of the canonical two-layer hybrid architecture: deliberative planning on top, with reactive behaviors underneath. Deliberative is used to describe higher-level reasoning that includes experiential memory and regional or global objectives. Alternatively, reactive describes low-level controllers that operate on information spatially and temporally immediate to the robot. In the traditional architecture, information is passed top down, with the deliberative layer dictating to the reactive layer. Chapter II presents our work on introducing feedback in the opposite direction, allowing the behaviors to provide information to the planning module(s). The path planning problem, particularly as it as solved by the visibility graph, is addressed first in Chapter III. Our so-called oriented visibility graph is a combinatorial planner with emphasis on dynamic re-planning in unknown environments at the expensive of guaranteed optimality at all times. An example of single source planning---where the goal location is known and static---this approach is compared to related approaches (e.g. the reduced visibility graph). The fourth chapter further develops the work presented in the Chapter III; the oriented visibility graph is extended to the hierarchical oriented visibility graph. This work directly addresses some of the limitations of the oriented visibility graph, particularly the loss of optimality in the case where obstacles are non-convex and where the convex hulls of obstacles overlap. This results in an approach that is a kind of middle-ground between the oriented visibility graph which was designed to handle dynamic updates very fast, and the reduced visibility graph, an old standard in path planning that guarantees optimality. Chapter V investigates path planning at a higher level of abstraction. Given is a weighted colored graph where vertices are assigned a color (or in other words class) that indicates a feature or quality of the environment associated with that vertex. The question is then asked, ''what is the globally optimal path through this weighted colored graph?" We answer this question with a mapping from classes and edge weights to a real number, and use Dijkstra's Algorithm to compute the best path. Correctness is proven and an implementation is highlighted.

Multiresolution approach for motion planning under differential constraints

Abstract: In this paper, we present an incremental, multiresolution motion planning algorithm designed for systems with differential constraints. Planning for these systems is more difficult than ordinary path planning due to the presence of momentum (drift) or nonholonomic velocity constraints. Given a motion planning problem for such a system and that a solution to the problem exists, then a finite reachability graph containing a solution trajectory is guaranteed to exist, under very reasonable conditions. In general, this graph can be generated using sufficiently dense input space sampling, sufficiently small time step, and sufficiently large tree depth. We show how to find and search such a tree in an incremental, multiresolution way. We prove the completeness of our algorithm, discuss related practical concerns, and show experimental results for several systems

Finding Efficient Robot Path for the Complete Coverage of A Known Space

Abstract: In robot planning problems, the complete coverage path planning refers to the problem of determining a path that a robot must take in order to pass over each point in an environment and avoid obstacles. Applications include demining, sweeping, cleaning, vacuuming, inspection robots. For this kind of problem, not only the complete coverage should be performed, but an efficient path is highly desired. Using traditional sweeping line strategy often requires several relocation moves, leading to poor efficiency. In this paper we describe a geometric algorithm for generating paths by using a modified sweeping line strategy that attempts to minimize extra relocation moves. Several examples are used in the paper to show the advantage of our algorithm over traditional approaches.

A Hierarchical Global Path Planning Approach for AUV Based on Genetic Algorithm

Abstract: Global path planning is one of the key technologies of AUV (Autonomous Underwater Vehicle), and it represents the intelligence level of AUV in some ways. This paper proposes a hierarchical global path planning approach for Autonomous Underwater Vehicle in ocean. This approach consists of successive decomposition of the robot's workspace and searching for a path at each level of decomposition. To get a global shortest path, the Genetic Algorithm is used in the algorithm for its efficient on optimization. The approach proves to be flexible and practicable, and can reduce memory consumption significantly.

Path summaries and path partitioning in modern XML databases

Abstract: XML path summaries are compact structures representing all the simple parent-child paths of an XML document. Such paths have also been used in many works as a basis for partitioning the document's content in a persistent store, under the form of path indices or path tables. We revisit the notions of path summaries and path-driven storage model in the context of current-day XML databases. This context is characterized by complex queries, typically expressed in an XQuery subset, and by the presence of efficient encoding techniques such as structural node identifiers. We review a path summary's many uses for query optimization, and given them a common basis, namely relevant paths. We discuss summary-based tree pattern minimization and present some efficient summary-based minimization heuristics. We consider relevant path computation and provide a time- and memory-efficient computation algorithm. We combine the principle of path partitioning with the presence of structural identifiers in a simple path-partitioned storage model, which allows for selective data access and efficient query plans. This model improves the efficiency of twig query processing up to two orders of magnitude over the similar tag-partitioned indexing model. We have implemented the path-partitioned storage model and path summaries in the XQueC compressed database prototype [8]. We present an experimental evaluation of a path summary's practical feasibility and of tree pattern matching in a path-partitioned store.

Evolving A Diverse Collection of Robot Path Planning Problems

Abstract: This study presents an evolutionary computation system that can generate grid robot path planning problems. An evolvable cellular representation that specifies how to build a PPP is used. Also presented is a technique for taxonomizing path planning problems so that the vast number of problems that can be generated with the evolutionary computation system can be subsequently winnowed into a collection of substantially different problems of specified size. In this study the most difficult path planning problems, according to three different criteria, are evolved and those results are used to demonstrated the taxonomic technique. The hardness criteria are (i) the minimum number of turns a robot must make, (ii) the minimum number of forward moves it must make, and (iii) the sum of these quantities. A dynamic programming algorithm is used to compute these quantities for a given path planning program. The technique can be generalized to find cases of a specified hardness. The size of the board and maximum number of obstacles used are transparently specifiable.

Metrics for analyzing the evolution of C-space models

Abstract: There are many sampling-based motion planning methods that model the connectivity of a robot's configuration space (C-space) with a graph whose nodes are valid configurations and whose edges represent valid transitions between nodes. One of the biggest challenges faced by users of these methods is selecting the right planner for their problem. While researchers have tried to compare different planners, most accepted metrics for comparing planners are based on efficiency, e.g., number of collision detection calls or samples needed to solve a particular set of queries, and there is still a lack of useful and efficient quantitative metrics that can be used to measure the suitability of a planner for solving a problem. That is, although there is great interest in determining which planners should be used in which situations, there are still many questions we cannot answer about the relative performance of different planning methods. In this paper we make some progress towards this goal. We propose a metric that can be applied to each new sample considered by a sampling-based planner to characterize how that sample improves, or not, the planner's current C-space model. This characterization requires only local information and can be computed quite efficiently, so that it can be applied to every sample. We show how this characterization can be used to analyze and compare how different planning strategies explore the configuration space. In particular, we show that it can be used to identify three phases that planners go through when building C-space models: quick learning (rapidly building a coarse model), model enhancement (refining the model), and learning decay (oversampling - most samples do not provide additional information). Hence, our work can also provide the basis for determining when a particular planning strategy has 'converged' on the best C-space model that it is capable of building

A Complete Coverage Path Planning Method for Mobile Robot in Uncertain Environments

Abstract: In this paper, a novel complete coverage path planning method based on the biologically inspired neural networks, rolling path planning and heuristic searching approach is presented for mobile robot motion planning with obstacles avoidance. The biologically inspired neural network is used to model the environment and calculate the environment information, while the rolling planning technique and the heuristic searching algorithm are utilized for the path planning,. Simulation studies show that the proposed method is very effective for the dynamic uncertain environments.

Maximizing Path Durations in Mobile Ad-Hoc Networks

Abstract: We study the problem of path selection in mobile ad-hoc networks. Based on our earlier results we propose a novel scheme that (i) maximizes the expected durations of selected paths and (ii) performs local path recovery by computing the probability a cached alternative path is still available in the event of a failure of a primary path in use. We evaluate the performance of our proposed scheme using ns-2 simulation and show that it yields a significant improvement in path durations.

Optimal path planning under defferent norms in continuous state spaces

Abstract: Optimal path planning under full state and map knowledge is often accomplished using some variant of Dijkstra's algorithm, despite the fact that it represents the path domain as a discrete graph rather than as a continuous space. In this paper we compare Dijkstra's discrete algorithm with a variant (often called the fast marching method) which more accurately treats the underlying continuous space. Analytically, both generate a value function free of local minima, so that optimal path generation merely requires gradient descent. We also investigate the use of optimality metrics other than Euclidean distance for both algorithms. These different norms better represent optimal paths for some types of problems, as demonstrated by planning optimal collision-free paths for a multiple robot scenario. When considering approximations consistent with the underlying state space, our conclusion is that fast marching places fewer constraints upon grid connectivity, and that it achieves better accuracy than Dijkstra's discrete algorithm in many but not all cases