Showing papers on "Any-angle path planning published in 2008"

Roadmap-Based Path Planning - Using the Voronoi Diagram for a Clearance-Based Shortest Path

Abstract: Path planning still remains one of the core problems in modern robotic applications, such as the design of autonomous vehicles and perceptive systems. The basic path-planning problem is concerned with finding a good-quality path from a source point to a destination point that does not result in collision with any obstacles. In this article, we chose the roadmap approach and utilized the Voronoi diagram to obtain a path that is a close approximation of the shortest path satisfying the required clearance value set by the user. The advantage of the proposed technique versus alternative path-planning methods is in its simplicity, versatility, and efficiency.

A Mobile Robot Path Planning Using Genetic Algorithm in Static Environment

Abstract: In this study we present our initial idea for using genetic algorithms to help a controllable mobile robot to find an optimal path between a starting and ending point in a grid environment. The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. GAs can overcome many problems encountered by traditional search techniques such as the gradient based methods. The proposed controlling algorithm allows four-neighbor movements, so that path-planning can adapt with complicated search spaces with low complexities. The results are promising.

Tie-breaking in shortest path determination

Abstract: A consistent tie-breaking decision between equal-cost shortest (lowest cost) paths is achieved by comparing an ordered set of node identifiers for each of a plurality of end-to-end paths. Alternatively, the same results can be achieved, on-the-fly, as a shortest path tree is constructed, by making a selection of an equal-cost path using the node identifiers of the diverging branches of the tree. Both variants allow a consistent selection to be made of equal-cost paths, regardless of where in the network the shortest paths are calculated. This ensures that traffic flow between any two nodes, in both the forward and reverse directions, will always follow the same path through the network.

Exploiting subgraph structure in multi-robot path planning

Abstract: Multi-robot path planning is dificult due to the combinatorial explosion of the search space with every new robot added Complete search of the combined state-space soon becomes intractable In this paper we present a novel form of abstraction that allows us to plan much more eficiently The key to this abstraction is the partitioning of the map into subgraphs of known structure with entry and exit restrictions which we can represent compactly Planning then becomes a search in the much smaller space of subgraph configurations Once an abstract plan is found, it can be quickly resolved into a correct (but possibly sub-optimal) concrete plan without the need for further search We prove that this technique is sound and complete and demonstrate its practical effiectiveness on a real map A contending solution, prioritised planning, is also evaluated and shown to have similar performance albeit at the cost of completeness The two approaches are not necessarily conflicting; we demonstrate how they can be combined into a single algorithm which out-performs either approach alone

Transition-based RRT for path planning in continuous cost spaces

Abstract: This paper presents a new method called Transition-based RRT (T-RRT) for path planning in continuous cost spaces. It combines the exploration strength of the RRT algorithm that rapidly grow random trees toward unexplored regions of the space, with the efficiency of stochastic optimization methods that use transition tests to accept or to reject a new potential state. This planner also relies on the notion of minimal work path that gives a quantitative way to compare path costs. The method also integrates self tuning of a parameter controlling its exploratory behavior. It yields to solution paths that efficiently follow low cost valleys and the saddle points of the cost space. Simulation results show that the method can be applied to a large set of applications including terrain costmap motions or planning low cost motions for free flying or articulated robots.

Optimal Path Planning for Mobile Robot Navigation

Abstract: Some optimal path planning algorithms for navigating mobile rectangular robot among obstacles and weighted regions are presented. The approach is based on a higher geometry maze routing algorithm. Starting from a top view of a workspace with obstacles, the so-called free workspace is first obtained by virtually expanding the obstacles in the image. After that, an 8-geomerty maze routing algorithm is applied to obtain an optimal collision-free path with linear time and space complexities. The proposed methods cannot only search an optimal path among various terrains but also find an optimal path for the 2-D piano mover's problem with 3 DOF. Furthermore, the algorithm can be easily extended to the dynamic collision avoidance problem among multiple autonomous robots or path planning in the 3-D space.

Multiple UAVs path planning algorithms: a comparative study

Abstract: Unmanned aerial vehicles (UAVs) are used in team for detecting targets and keeping them in its sensor range. There are various algorithms available for searching and monitoring targets. The complexity of the search algorithm increases if the number of nodes is increased. This paper focuses on multi UAVs path planning and Path Finding algorithms. Number of Path Finding and Search algorithms was applied to various environments, and their performance compared. The number of searches and also the computation time increases as the number of nodes increases. The various algorithms studied are Dijkstra's algorithm, Bellman Ford's algorithm, Floyd-Warshall's algorithm and the AStar algorithm. These search algorithms were compared. The results show that the AStar algorithm performed better than the other search algorithms. These path finding algorithms were compared so that a path for communication can be established and monitored.

Vehicle routing and path planning

Abstract: A method of determining a path having an ordered set of waypoints to be visited by a mobile agent to accomplish a mission includes: producing candidate paths using a multi-objective optimization algorithm, subject to a path production heuristic; selecting a path from the candidate paths, subject to a path selection heuristic; instructing the mobile agent to move according to the selected path; modifying a maintained subset of the candidate paths to produce a new candidate path using the algorithm and subject to the path production heuristic; designating either the currently-selected path or the new candidate path as the newly-selected path, subject to the path selection heuristic; and instructing the mobile agent to move according to the newly-selected path The method may further include iterating production of new candidate paths, either randomly or based on modifications of previous candidate paths, to continually update an operation plan for the mobile agent

Path Deformation Roadmaps: Compact Graphs with Useful Cycles for Motion Planning

Abstract: In this paper we describe a new approach to sampling-based motion planning with Probabilistic Roadmap Planner (PRM) methods. Our aim is to compute good quality roadmaps which encode the multiple connectedness of the configuration space inside small but yet representative graphs which capture the different varieties of free paths well. The proposed Path Deformation Roadmaps (PDRs) rely on a notion of path deformability indicating whether or not a given path can be continuously deformed into another existing path. By considering a simpler form of deformation than that allowed between homotopic paths, we propose a method that extends the Visibility-PRM technique to construct compact roadmaps which encode richer and more suitable information than representative paths of the homotopy classes. PDRs contain additional useful cycles between paths in the same homotopy class that can be hardly deformed into each other. Experimental results show that the technique enables small roadmaps to reliably capture the multi...

An efficient hierarchical optical path network design algorithm based on a traffic demand expression in a cartesian product space

Abstract: We propose a hierarchical optical path network design algorithm. In order to efficiently accommodate wavelength paths in each waveband path, we define a source-destination Cartesian product space that allows the 'closeness' among wavelength paths to be assessed. By grouping 'close' wavelength paths, found by searching for clusters in the space, we iteratively create waveband paths that efficiently accommodate the wavelength paths. Numerical experiments demonstrate that the proposed algorithm offers lower total network cost than the conventional algorithms. The results also show that the hierarchical optical path network is effective even when traffic demand is relatively small.

3-D path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms

Abstract: Military missions are turning to more complicated and advanced automation technology for maximum endurance and efficiency as well as the minimum vital risks. The path planners which generate collision-free and optimized paths are needed to give autonomous operation capability to the Unmanned Aerial Vehicles (UAVs). This paper presents an off-line path planner for UAVs. The path planner is based on Evolutionary Algorithms (EA), in order to calculate a curved path line with desired attributes in a 3-D terrain. The flight path is represented by parameterized B-Spline curves by considering four objectives: the shortest path to the destination, the feasible path without terrain collision, the path with the desired minimum and maximum distance to the terrain, and the path which provides UAV to maneuver with an angle greater than the minimum radius of curvature. The generated path is represented with the coordinates of its control points being the genes of the chromosome of the EA. The proposed method was tested in several 3-D terrains, which are generated with various terrain generator methods that differ with respect to levels of smoothness of the terrain.

Hybrid ant colony algorithms for path planning in sparse graphs

Abstract: The general problem of path planning can be modeled as a traveling salesman problem which assumes that a graph is fully connected. Such a scenario of full connectivity is however not always realistic. One such motivating example for us is the application of path planning for unmanned reconnaissance aerial vehicles (URAVs). URAVs are widely deployed for photography or imagery gathering missions of sites of interest. These sites can be targets in a combat zone to be investigated or sites inaccessible by ground transportation, such as those hit by forest fires, earthquake or other forms of natural disasters. The navigation environment is one where the overall configuration of the problem is a sparse graph. Unlike graphs that are fully connected, sparse graphs are not always Hamiltonian. In this paper, we describe hybrid ant colony algorithms (HACAs) proposed for path planning in sparse graphs since existing ant colony solvers designed for solving TSP do not apply to the present context directly. HACAs represent ant inspired algorithms incorporated with a local search procedure and some heuristic techniques for uncovering feasible route(s) or path(s) in a sparse graph within tractable time. Empirical results conducted on a set of generated sparse graphs demonstrate the excellent convergence property and robustness of HACAs in uncovering low risk and Hamiltonian visitation paths. Further, the obtained results also indicate that HACAs converge to secondary closed paths in situations where a Hamiltonian cycle does not exist theoretically or is not attainable within the bounded computational time window.

Path and trajectory diversity: Theory and algorithms

Abstract: We present heuristic algorithms for pruning large sets of candidate paths or trajectories down to smaller subsets that maintain desirable characteristics in terms of overall reachability and path length. Consider the example of a set of candidate paths in an environment that is the result of a forward search tree built over a set of actions or behaviors. The tree is precomputed and stored in memory to be used online to compute collision-free paths from the root of the tree to a particular goal node. In general, such a set of paths may be quite large, growing exponentially in the depth of the search tree. In practice, however, many of these paths may be close together and could be pruned without a loss to the overall problem of path-finding. The best such pruning for a given resulting tree size is the one that maximizes path diversity, which is quantified as the probability of the survival of paths, averaged over all possible obstacle environments. We formalize this notion and provide formulas for computing it exactly. We also present experimental results for two approximate algorithms for path set reduction that are efficient and yield desirable properties in terms of overall path diversity. The exact formulas and approximate algorithms generalize to the computation and maximization of spatio-temporal diversity for trajectories.

Planning High-quality Paths and Corridors Amidst Obstacles

Abstract: The motion-planning problem, involving the computation of a collision-free path for a moving entity amidst obstacles, is a central problem in fields such robotics and game design. In this paper we study the problem of planning high-quality paths. A high-quality path should have some desirable properties: it should be short, avoiding long detours, and at the same time it should stay at a safe distance from the obstacles, namely it should have clearance. We suggest a quality measure for paths, which balances between the above criteria of minimizing the path length while maximizing its clearance. We analyze the properties of optimal paths according to our measure, and devise an approximation algorithm to compute near-optimal paths amidst polygonal obstacles in the plane. We also apply our quality measure to corridors. Instead of planning a one-dimensional motion path for a moving entity, it is often more convenient to let the entity move in a corridor, where the exact motion path is determined by a local pla...

System, method, and program for determining the availability of paths to a device

Abstract: Disclosed is a system, method, and program for selecting one of multiple proposed paths to a device. For each proposed path, a determination is made of a number of components the proposed path shares with existing paths to the device. The components comprise points of failure such that if one component fails then the path including the component fails. The determined number of shared components for each proposed path is used to select one proposed path.

Statistical path selection for at-speed test

Abstract: Process variations make at-speed testing significantly more difficult. They cause subtle delay changes that are distributed rather than the localized nature of a traditional fault model. Due to parametric variations, different paths can be critical in different parts of the process space, and the union of such paths must be tested to obtain good process space coverage. This paper proposes a novel branch-and-bound algorithm that elegantly and efficiently solves the hitherto open problem of statistical path tracing. The resulting paths are used for at-speed structural testing. A new Test Quality Metric (TQM) is proposed and paths which maximize this metric are selected. After chip timing has been performed, the path selection procedure is extremely efficient. Path selection for a multi-million gate chip design can be completed in a matter of seconds.

Path planning in dynamic environment using Fuzzy Cognitive Maps

Abstract: This paper deals with application of fuzzy cognitive maps (FCM) in combination with a graph searching algorithm A for purposes of path planning for a vehicle in a traffic system with dynamic changes. In this paper a simulation of a parking problem is shown. The purpose of this paper is to show possibilities of using FCM in a dynamic environment, too.

Combined coverage path planning for autonomous cleaning robots in unstructured environments

Abstract: Complete coverage path planning is a key problem for autonomous cleaning robots, which concerns not only the cleaning efficiency but also the adaptability to unstructured environments. But the diversity of environments and limited perception ability of the robot make the problem still unsolved. In this paper, a novel strategy of combined coverage path planning is proposed, which combines the random path planning and local complete coverage path planning. The random planning lets the robot run straight until an obstacle is encountered. After turning a random angle, the robot continues the straight run. This mode is easy to implement and can provide the robot with the flexibility to environments. And local complete coverage path planning works out a comb-like path depending on dead reckoning. The comb-like path can cover every part in a relative small area. All these functions are just based on general hardware: ultrasonic sensors, infrared sensors, incremental encoders, DC motors, vacuum, etc. Finally the experiment shows that this strategy can work efficiently and robustly in common family environments.

Adapting the wavefront expansion in presence of strong currents

Abstract: The wavefront expansion is commonly used for path planning tasks and appreciated for its efficiency. However, the existing extensions able to handle currents are subject to incorrectness and incompleteness issues when these currents become strong. That is, they may return physically infeasible paths or no path at all, even if a feasible path exists. This behavior endangers the robot, especially in a dynamic replanning context. That is why we propose a new extension called sliding wavefront expansion. This algorithm, combining an appropriate cost function and continuous optimization techniques, guarantees the existence of a path with an arbitrary precision.

Efficient cell labelling and path non-existence computation using C-obstacle query

Abstract: We present a simple algorithm to check for path non-existence for a low-degree-of-freedom (DOF) robot among static obstacles. Our algorithm is based on approximate cell decomposition of configuration space or C-space. We use C-obstacle cell query to check whether a cell lies entirely inside the C-obstacle region. This reduces the path non-existence problem to checking whether a path exists through the set of all cells that do not lie entirely inside the C-obstacle region. We present a simple and efficient algorithm to perform C-obstacle cell query using generalized penetration depth computation. Our algorithm is simple to implement and we demonstrate its performance on three-DOF and four-DOF robots.

Multiresolution path planning with wavelets: A local replanning approach

Abstract: A path planning algorithm based on multiresolution cell decomposition of the environment using wavelets is proposed. The environment is assumed to be given by an occupancy grid at fine resolution. The algorithm constructs a cell decomposition at several levels of resolution (cell sizes) and constructs an optimal path to the destination from the current location of the agent. At each step the algorithm iteratively refines a coarse approximation to the path through local replanning. The replanning process uses previous information to refine the original cell channel in the immediate area of the path. This is done efficiently using the wavelet coefficients. Numerical tests show a speed-up of an order of magnitude over the baseline algorithm with minimal impact on the overall optimality of the resulting path. A comparative study with the well-known D* algorithm is also provided.

Path management control method, path management control program, path management control device and path management control system

Abstract: The path management controller 1 refers to resource information 131 including reservation statuses of working paths and advance reserved paths that are set in every link, so as to calculate a route for setting a new path and set this path in this calculated route. If the route for setting this new path cannot be obtained (1) because of a shortage of residual bandwidth of a link of interest, the path management controller 1 refers to the resource reservation information 131 of working paths and advance reserved paths and calculates an alternative route for another existing path used in the link of interest, and moves this path to the calculated alternative route. On the other hand, if the route for setting this new path cannot be obtained (2) because there is no continuous bandwidth sufficient for setting this new path in the link of interest, the path management controller 1 refers to the resource reservation information regarding working paths and advance reserved paths in the link of interest in the resource information 131, so as to execute the time slot rearrangement.

A Simple Path Non-existence Algorithm Using C-Obstacle Query

Abstract: We present a simple algorithm to check for path non-existence for a robot among static obstacles. Our algorithm is based on adaptive cell decomposition of configuration space or C-space. We use two basic queries: free cell query, which checks whether a cell in C-space lies entirely inside the free space, and C-obstacle cell query, which checks whether a cell lies entirely inside the C-obstacle region. Our approach reduces the path non-existence problem to checking whether there exists a path through cells that do not belong to the C-obstacle region. We describe simple and efficient algorithms to perform free cell and C-obstacle cell queries using separation distance and generalized penetration depth computations. Our algorithm is simple to implement and we demonstrate its performance on 3 DOF robots.

Transfer Graph Approach for Multimodal Transport Problems

Abstract: Route guidance solutions used to be applied to single transportation mode. The new trend today is to find route guidance approaches able to propose routes which may involve multi transportation modes. Such route guidance solutions are said to be multi modal. This document presents our contribution to multimodal route guidance problem. Following our strategy, we introduce a new graph structure to abstract multimodal networks. The graph structure is called transfer graph. A transfer graph is described by a set of (sub) graphs called components. They are connected via transfer points. By transfer point we mean any node common to two distinct components of a transfer graph. So a transfer graph is distinct from a partitioned graph. An example of transfer graph is a multimodal network in which all participating unimodal networks are not merged, but are kept separated instead. Since a multimodal network is reducible to a transfer graph, transfer graph based approach can be used for multimodal route guidance. Finally, to give meaning to our work, we try to insert our approach with the shortest path service in Carlink project. This step is seen as the implimentation of our algorithm, so we can get an idea on its performance.

Fuzzy Cognitive Maps in Path Planning

Abstract: This paper deals with application of Fuzzy Cognitive Maps (FCM) in combination with a graph search algorithrn A* for purposes of path planning for a vehicle in a traffic system with dynamic changes. In this paper a simulation of a parking problem is shown. The term parking problem is reserved for searching a suitable parking place and path planning. The purpose of this paper is to show possibilities of using FCM in a dynamic environment, too.

Efficient algorithms for path problems in weighted graphs

Abstract: Problems related to computing optimal paths have been abundant in computer science since its emergence as a field. Yet for a large number of such problems we still do not know whether the state-of-the-art algorithms are the best possible. A notable example of this phenomenon is the all pairs shortest paths problem in a directed graph with real edge weights. The best algorithm (modulo small polylogarithmic improvements) for this problem runs in cubic time, a running time known since the 1960s (by Floyd and Warshall). Our grasp of many such fundamental algorithmic questions is far from optimal, and the major goal of this thesis is to bring some new insights into efficiently solving path problems in graphs. We focus on several path problems optimizing different measures: shortest paths, maximum bottleneck paths, minimum nondecreasing paths, and various extensions. For the all-pairs versions of these path problems we use an algebraic approach. We obtain improved algorithms using reductions to fast matrix multiplication. For maximum bottleneck paths and minimum nondecreasing paths we are the first to break the cubic barrier, obtaining truly subcubic strongly polynomial algorithms. We also consider a nonalgebraic, combinatorial approach, which is considered more efficient in practice compared to methods based on fast matrix multiplication. We present a combinatorial data structure that maintains a matrix so that products with given sparse vectors can be computed efficiently. This allows us to obtain good running times for path problems in unweighted sparse graphs. This thesis also gives algorithms for some single source path problems. We obtain the first linear time algorithm for the single source minimum nondecreasing paths problem. We give some extensions to this, including an algorithm to find cheapest minimum nondecreasing paths. Besides finding optimal paths, we consider the related problem of finding optimal cycles. In particular, we focus on the problem of finding in a weighted graph a triangle of maximum weight sum. We obtain the first truly subcubic algorithm for finding a maximum weight triangle in a node-weighted graph. We also present algorithms for the edge-weighted case. These algorithms immediately imply good algorithms for finding maximum weight k-cliques, or arbitrary maximum weight pattern subgraphs of fixed size.

Hierarchical path computation approach for large graphs

Abstract: Time is a critical factor in several path planning problems such as flood emergency rescue operations, escape planning from fires and chemical warfare agents dispersed in large buildings, evacuation from urban areas during natural disasters such as earthquakes, and military personnel movement. We propose a hierarchical path planning algorithm (HIPLA) for real time path planning problems where the computational time is of critical significance. The main idea of HIPLA is to significantly reduce the search space for path computation by searching in a high-level abstraction graph, whose nodes are associated with precomputed risk estimates. The cumulative risk associated with all nodes along a path determines the quality of a path. We present a detailed experimental analysis of HIPLA by comparing it with two well-known approaches viz., shortest path algorithm (SPAH) [1] and Dijkstra's algorithm with pruning [2] for large node-weighted graphs.

Least visible path analysis in raster terrain

Abstract: Least visible path analysis is a basic function in terrain visibility analysis. However, current least visible path planning is constrained to least-cost path computing on a cost surface obtained from visibility information of all the terrain points on digital elevation models. This kind of method ignores the visibility correlation caused by the overlapped part of the adjacent points' reverse viewsheds. With regard to such a correlation, this paper proposes a new method using the amalgamation of reverse viewsheds to find an optimal path with the minimal visibility dominance. Both methods are implemented using C++ programming, and the experimental results show that the new method produces more accurate least visible paths than the traditional one does.