Showing papers on "Any-angle path planning published in 2004"
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TL;DR: LPA* is developed, an incremental version of A* that combines ideas from the artificial intelligence and the algorithms literature and repeatedly finds shortest paths from a given start vertex to a given goal vertex while the edge costs of a graph change or vertices are added or deleted.
584 citations
06 Jul 2004
TL;DR: This paper examines some of the implementation issues in rigid body path planning and presents techniques which have been found to be effective experimentally for Rigid Body path planning.
Abstract: Important implementation issues in rigid body path planning are often overlooked. In particular, sampling-based motion planning algorithms typically require a distance metric defined on the configuration space, a sampling function, and a method for interpolating sampled points. The configuration space of a 3D rigid body is identified with the Lie group SE(3). Defining proper metrics, sampling, and interpolation techniques for SE(3) is not obvious, and can become a hidden source of failure for many planning algorithm implementations. This paper examines some of these issues and presents techniques which have been found to be effective experimentally for Rigid Body path planning.
289 citations
06 Jul 2004
TL;DR: Ridid body movements, maze like problems as well as path planning problems for chain-like robotic platforms have been solved successfully using the proposed algorithm.
Abstract: We present a new approach to path planning in high-dimensional static configuration spaces. The concept of cell decomposition is combined with probabilistic sampling to obtain a method called probabilistic cell decomposition (PCD). The use of lazy evaluation techniques and supervised sampling in important areas leads to a very competitive path planning method. It is shown that PCD is probabilistic complete, PCD is easily scalable and applicable to many different kinds of problems. Experimental results show that PCD performs well under various conditions. Rigid body movements, maze like problems as well as path planning problems for chain-like robotic platforms have been solved successfully using the proposed algorithm.
185 citations
19 Jun 2004
TL;DR: This work presents results of their work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space.
Abstract: This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.
177 citations
27 Aug 2004
TL;DR: A novel approach to motion planning for coherent groups of units, which uses a path for a single unit, called the backbone path, which can be generated by any motion planner and is capable of generating the paths in real-time.
Abstract: Virtual environment are often populated with moving units and the paths for these units should be planned. When multiple units need to exhibit coherent behavior in a cluttered environment, current techniques often fail, i.e. the resulting paths for the units in the group lack the coherence required. In this paper, we propose a novel approach to motion planning for coherent groups of units.The method presented uses a path for a single unit, called the backbone path, which can be generated by any motion planner. This backbone path is extended to a corridor using the clearance along the path. The units can move freely inside this corridor. By limiting the width of this corridor, and the extent along the corridor where the units can move to, the approach guarantees coherence of the group.Experiments show that the generated paths exhibit group coherence as required, like passing on the same side of obstacles and waiting for fellow group mates to catch up. Performance measurements show that the approach is capable of generating the paths in real-time. In our implementation, the method requires just a few percent of the processor time for groups consisting of up to 100 units.
141 citations
26 Aug 2004
TL;DR: An advanced PSO algorithm with the mutation operator is put forward, which can not only escape the attraction of the local minimum in the later convergence phase, but also maintain the characteristic of fast speed in the early phase.
Abstract: Path planning is one of the most important technologies in the navigation of the mobile robot, which should meet the optimization and real-time requests This paper presents a novel approach of path planning First the MAKLINK graph is built to describe the working space of the mobile robot; then the Dijkstra algorithm is used to obtain the shortest path from the start point to the goal point in the graph, finally the particle swarm optimization algorithm is adopted to get the optimal path Aiming at the shortcoming of the PSO algorithm, that is, easily plunging into the local minimum, this paper puts forward an advanced PSO algorithm with the mutation operator By adding a mutation operator to the algorithm, it can not only escape the attraction of the local minimum in the later convergence phase, but also maintain the characteristic of fast speed in the early phase The results of the simulation demonstrate the effectiveness of the proposed method, which can meet the real-time requests of the mobile robot's navigation
141 citations
TL;DR: In this article, a vector pursuit approach based on the theory of screws was used to track a nonholonomic ground vehicle along a given path using a set of screws, which was developed by Sir Robert Ball in 1900 to generate a desired vehicle turning radius based on vehicle position and orientation relative to the position of a point ahead on the planned path and the desired orientation along the path at that point.
Abstract: Autonomous ground vehicle navigation requires the integration of many technologies such as path planning, position and orientation sensing, vehicle control, and obstacle avoidance The work presented here focuses on the control of a nonholonomic ground vehicle as it tracks a given path A new path tracking technique called “vector pursuit” is presented This new technique is based on the theory of screws, which was developed by Sir Robert Ball in 1900 It generates a desired vehicle turning radius based on the vehicle's current position and orientation relative to the position of a point ahead on the planned path and the desired orientation along the path at that point The vector pursuit algorithm is compared to other geometrical approaches, and it is shown to be more robust, resulting in more accurate path tracking © 2004 Wiley Periodicals, Inc
121 citations
06 Jul 2004
TL;DR: RRTLocTrees as mentioned in this paper augments RRT-planners with local trees to solve the narrow passage problem and explore several difficult regions in parallel, which has proved to be very effective for problems where the solution trajectory repeatedly has to pass difficult regions.
Abstract: During the last few years, Rapidly-exploring Random Trees, RRTs, has been recognized as a very useful tool for designing efficient single-shot path planners. Another benefit is that RRTs can easily handle planning problems involving non-holonomic systems. However, it has also been noted that the narrow passage problem can become even more severe compared to other randomized methods. To reduce that problem, we suggest augmenting RRT-planners with local trees. Furthermore, using local trees, the planner is able to explore several difficult regions in parallel, something that has proved to be very effective for problems where the solution trajectory repeatedly has to pass difficult regions. We present powerful heuristics for when to create such trees and how often they should be allowed to grow, such that the RRT-planner will improve its qualities as an efficient single-shot path planner. The resulting algorithm, RRTLocTrees, is implemented in a newly developed object-oriented framework for path planning and tested on four different path planning problems, with excellent results.
91 citations
TL;DR: The performance evaluation of the restricted shortest path algorithms and multi-constrained path algorithms is presented based on complexity analysis and simulation results and may shed some light on the difficult task of selecting the proper algorithm for a QoS-capable network.
Abstract: Constraint-based path selection is an invaluable part of a full-fledged quality of service (QoS) architecture. Internet service providers want to be able to select paths for QoS flows that optimize network utilization and satisfy user requirements and as such increase revenues. Unfortunately, finding a path subject to multiple constraints is known to be an NP-complete problem. Hence, accurate constraint-based path selection algorithms with a fast running time are scarce. Numerous heuristics and a few exact algorithms have been proposed. In this article we compare most of these algorithms. We focus on the restricted shortest path algorithms and multi-constrained path algorithms. The performance evaluation of these two classes of algorithms is presented based on complexity analysis and simulation results and may shed some light on the difficult task of selecting the proper algorithm for a QoS-capable network.
91 citations
TL;DR: A new path planning algorithm is presented where these three methods are integrated for the first time in a single architecture and generally yields shorter paths than the Voronoi and potential field methods, and faster than the visibility graph.
Abstract: Numerous methods have been developed to solve the motion planning problem, among which the Voronoi diagram, visibility graph, and potential fields are well-known techniques. In this paper, a new path planning algorithm is presented where these three methods are integrated for the first time in a single architecture. After constructing the generalized Voronoi diagram of C-space, we introduce a novel procedure for its abstraction, producing a pruned generalized Voronoi diagram. A broad freeway net is then developed through a new a-MID (maximal inscribed discs) concept. A potential function is assigned to the net to form an obstacle-free network of valleys. Afterwards we take advantage of a bidirectional search, where the visibility graph and potential field modules execute alternately from both start and goal configurations. A steepest descent mildest ascent search technique is used for local planning and avoiding local minima. The algorithm provides a parametric tradeoff between safest and shortest paths and generally yields shorter paths than the Voronoi and potential field methods, and faster than the visibility graph. It also performs well in complicated environments. © 2004 Wiley Periodicals, Inc.
91 citations
06 Jul 2004
TL;DR: Guided Expansive Spaces Trees are introduced to search for a low cost and relatively straight path in a space with motion constraints, and Path Gradient Descent, which builds on the idea of Elastic Strips, finds the locally optimal path for an existing path.
Abstract: Motion planning for systems with constraints on controls or the need for relatively straight paths for real-time actions presents challenges for modern planners. This paper presents an approach which addresses these types of systems by building on existing motion planning approaches. Guided Expansive Spaces Trees are introduced to search for a low cost and relatively straight path in a space with motion constraints. Path Gradient Descent, which builds on the idea of Elastic Strips, finds the locally optimal path for an existing path. These techniques are tested on simulations of rendezvous and docking of the space shuttle to the International Space Station and of a 4-foot fan-controlled blimp in a factory setting.
06 Jul 2004
TL;DR: This work presents a new technique that results in higher quality (shorter) paths with much less variation between the executions, based on adding useful cycles to the roadmap graph.
Abstract: Over the last decade, the probabilistic road map method (PRM) has become one of the dominant motion planning techniques. Due to its random nature, the resulting paths tend to be much longer than the optimal path despite the development of numerous smoothing techniques. Also, the path length varies a lot every time the algorithm is executed. We present a new technique that results in higher quality (shorter) paths with much less variation between the executions. The technique is based on adding useful cycles to the roadmap graph.
28 Sep 2004
TL;DR: A fast dynamic visibility graph (DVG) for constructing a reduced roadmap among convex polygonal obstacles is proposed and is extended to deal with multi-target problems that traditionally require a lot of time for reconstructing configuration space (C-space).
Abstract: In this paper, we propose a fast dynamic visibility graph (DVG) for constructing a reduced roadmap among convex polygonal obstacles. DVG is extracted from the global environment with the simple geometric method and rules. Moreover, the data preprocessing is based on the concept of V-circle. Through V-circle, the process is speeded up greatly. Finally, DVG is extended to deal with multi-target problems that traditionally require a lot of time for reconstructing configuration space (C-space).
28 Sep 2004
TL;DR: Workstation importance sampling (WIS) as discussed by the authors uses geometric information from a robot's workspace as "importance" values to guide sampling in the corresponding configuration space, which increases the sampling density in narrow passages and decreases the sample density in wide-open regions.
Abstract: Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but they behave poorly when a robot's configuration space contains narrow passages. This paper presents workspace importance sampling (WIS), a new sampling strategy for PRM planning. Our main idea is to use geometric information from a robot's workspace as "importance" values to guide sampling in the corresponding configuration space. By doing so, WIS increases the sampling density in narrow passages and decreases the sampling density in wide-open regions. We tested the new planner on rigid-body and articulated robots in 2-D and 3-D environments. Experimental results show that WIS improves the planner's performance for path planning problems with narrow passages.
20 Sep 2004
TL;DR: A framework of parallel evolutionary algorithms for UAV path planning, in which several populations evolve simultaneously and compete with each other is presented, providing more exploration capability to planners and significantly reduces the probability that planners are trapped in local optimal solutions.
Abstract: Evolutionary computation (EC) techniques have been successfully applied to compute near-optimal paths for unmanned aerial vehicles (UAVs). Premature convergence prevents evolutionary-based algorithms from reaching global optimal solutions. This often leads to unsatisfactory routes that are suboptimal to optimal path planning problems. To overcome this problem, this paper presents a framework of parallel evolutionary algorithms for UAV path planning, in which several populations evolve simultaneously and compete with each other. The parallel evolution technique provides more exploration capability to planners and significantly reduces the probability that planners are trapped in local optimal solutions.
06 Jul 2004
TL;DR: A number of techniques to improve the quality of paths are described based on a new approach to increase the path clearance, which showed that the heuristics were able to generate paths of a much higher quality than previous approaches.
Abstract: Many motion planning techniques, like the probabilistic roadmap method (PRM), generate low quality paths. In this paper, we study a number of different quality criteria on paths in particular length and clearance. We describe a number of techniques to improve the quality of paths. These are based on a new approach to increase the path clearance. Experiments showed that the heuristics were able to generate paths of a much higher quality than previous approaches.
26 Oct 2004
TL;DR: A solution algorithm based on the ant colony optimization technique to search the shortest path from a desired origin to a desired destination of the map is proposed and implemented in C++.
Abstract: The paper presents an optimal approach to search the best path of a map considering the traffic loading conditions. The main objective of this work is to minimize the path length to get the best path planning for a given map. This study proposes a solution algorithm based on the ant colony optimization technique to search the shortest path from a desired origin to a desired destination of the map. The proposed algorithm is implemented in C++. Furthermore, the simulation program can randomly generate maps for evaluating its flexibility and performance. Simulation results demonstrate that the proposed algorithm can obtain the shortest path of a map with fast speed.
Journal Article•
TL;DR: Grid-based methods for finding cost optimal robot paths around obstacles are popular because of their flexibility and simple implementation, but their computational complexity becomes unfeasible for real-time path planning if the resolution of the grid is high.
Abstract: Grid-based methods for finding cost optimal robot paths around obstacles are popular because of their flexibility and simple implementation. However, their computational complexity becomes unfeasible for real-time path planning if the resolution of the grid is high. These methods assume complete knowledge about the world, but in dynamic environments where sensing is done on board the robot, less is known about far-away obstacles than about the ones in close proximity. The paper proposes to utilize this observation by employing a grid of variable resolution. The resolution is high next to the robot and becomes lower with increasing distance. This results in huge savings in computational costs while the initial parts of the paths are still planned with high accuracy. The same principle is applied to the time-axis, allowing for planning paths around moving obstacles with only a moderate increase in computational costs.
06 Jul 2004
TL;DR: A heuristic search algorithm for generating optimal plans in a new class of decision problem, characterised by the incorporation of hidden state, is described, which interleaves heuristic expansion of the reduced space with forwards and backwards propagation phases to produce a solution in a fraction of the time required by other techniques.
Abstract: We describe a heuristic search algorithm for generating optimal plans in a new class of decision problem, characterised by the incorporation of hidden state. The approach exploits the nature of the hidden state to reduce the state space by orders of magnitude. It then interleaves heuristic expansion of the reduced space with forwards and backwards propagation phases to produce a solution in a fraction of the time required by other techniques. Results are provided on an outdoor path planning application.
20 Sep 2004
TL;DR: In this paper, a novel path planning approach for rapid construction of feasible, yet agile, trajectories is presented, which relies upon separating the planning task into an on-line and an o-line component.
Abstract: Autonomous path planning capability is a critical requirement for future unmanned vehicle operations. This paper presents a novel path planning approach for rapid construction of feasible, yet agile, trajectories. The approach relies upon separating the planning task into an on-line and an o-line component. The o-line component consists of building a library of motion primitives using nonlinear simulation, the primitives are then sequenced on-line using an A* search. The main benefit of this separation is that it shifts the computationally intensive work to the o-line component and leaves the on-line task relatively light. A major benefit of using A* is that the search algorithm is unaected by the changes to the library of trim primitives. Thus in the event that a UAVs capabilities are degraded, e.g. due to damage, any unavailable motion primitives are simply removed from the library and A* will not included them in the path construction. To speed up the on-line search time, a sub-optimal modification is made to the standard A* algorithm that delivers a feasible sub-optimal path quickly with relatively small increase in total path cost. Examples of waypoint planning and obstacle avoidance are given to illustrate the path planning approach.
06 Jul 2004
TL;DR: A new path planning technique for a flexible wire is presented and a new parametrization designed to represent low-energy configurations is introduced, which is a good approximation of true minimal energy curves.
Abstract: In this paper we present a new path planning technique for a flexible wire. We first introduce a new parametrization designed to represent low-energy configurations. Based on this parametrization we can find curves that satisfy endpoint constraints. Next, we present three different techniques for minimizing energy within the self-motion manifold of the curve. We introduce a local planner to find smooth minimal energy deformations for these curves that can be used by a general path planning algorithm. Using a simplified model for obstacles, we can find minimal energy curves of fixed length that pass through specified tangents at given control points. Finally, we show that the parametrization introduced in this paper is a good approximation of true minimal energy curves. Our work has applications in surgical suturing and snake-like robots.
TL;DR: In this article, a unified approach to cooperative target tracking and path planning for multiple vehicles is presented, where all vehicles, friendly and adversarial, are assumed to be aircraft and target state information is estimated in order to integrate into a path planning framework.
Abstract: A unified approach to cooperative target tracking and path planning for multiple vehicles is presented. All vehicles, friendly and adversarial, are assumed to be aircraft. Unlike the typical target tracking problem that uses the linear state and nonlinear output dynamics, a set of aircraft nonlinear dynamics is used in this work. Target state information is estimated in order to integrate into a path planning framework. The objective is to fly from a start point to a goal in a highly dynamic, uncertain environment with multiple friendly and adversarial vehicles, without collision. The estimation architecture proposed is consistent with most path planning methods. Here, the path planning approach is based on evolutionary computation technique which is then combined with a nonlinear extended set membership filter in order to demonstrate a unified approach. A cooperative estimation approach among friendly vehicles is shown to improve speed and routing of the path. Copyright © 2004 John Wiley & Sons, Ltd.
01 Jan 2004
TL;DR: A new approach is described, based on a technique from robotics, that computes a roadmap of smooth, collision-free, high-quality paths that can be used to obtain instantly good paths for entities.
Abstract: Path planning plays an important role in many computer games. Currently the motion of entities is often planned using a combination of scripting, grid-search methods, and reactive approaches. In this paper we describe a new approach, based on a technique from robotics, that computes a roadmap of smooth, collision-free, high-quality paths. This roadmap can be used to obtain instantly good paths for entities. We also describe applications of the technique for planning the motion of groups of entities and for creating smooth camera movements through an environment.
28 Sep 2004
TL;DR: Methods for adapting discrete space search algorithms based on continuous-space motion planning techniques such as rapidly exploring random trees (RRTs) and probabilistic roadmaps (PRMs) for discrete use are described.
Abstract: In this paper, we introduce several discrete space search algorithms based on continuous-space motion planning techniques such as rapidly exploring random trees (RRTs) and probabilistic roadmaps (PRMs) We describe methods for adapting these algorithms for discrete use by replacing distance metrics with cost-to-go heuristic estimates and substituting local planners for straight-line connectivity Finally, we explore coverage and optimality properties of these algorithms in discrete spaces
Journal Article•
TL;DR: A novel path planning approach is presented, in which the MAKLINK graph is built to describe the working space of the mobile robot, the Dijkstra algorithm is used to obtain the shortest path from the start point to the goal point in the graph, and the particle swarm optimization algorithm is adopted to get the best path.
Abstract: This paper presents a novel path planning approach , in which the MAKLINK graph is built to describe the working space of the mobile robot, the Dijkstra algorithm is used to obtain the shortest path from the start point to the goal point in the graph, and the particle swarm optimization algorithm is adopted to get the best path. Simulation results show that the proposed method is effective and can meet the real-time demands of mobile robot navigation.
19 Jul 2004
TL;DR: This work uses an embedded network distributed throughout the environment to approximate the path-planning space and use the network to compute the path in a distributed fashion, which essentially works as a distributed variant of the popular wave-front path planning algorithm.
Abstract: We investigate the application of a low-cost, pervasively distributed network to plan paths for mobile robots in environments with dynamic obstacles. We consider a heterogeneous system composed of small, embedded, immobile, possibly sensor-less, communication nodes and larger mobile robots equipped with sensors and manipulators. The embedded network serves as a pervasive communication and computation fabric, while the mobile robots provide sensing and actuation. The network is responsible for planning paths for the mobile robots even though paths are being created and destroyed dynamically. The embedded network provides nearly optimal path planning without the network nodes or the robots having global knowledge or localization capabilities. Path planning is one of the most fundamental and wellstudied problem in mobile robotics. Techniques such as D*[1] plan paths in dynamic environments where paths are created and destroyed. Inspired by these techniques, we have developed a technique for distributed path planning in environments were obstacles appear and disappear. Traditional path planning algorithms for dynamic environments typically require a single mobile robot to build a map, update the map as the environment changes, and then finally plan over the map. Instead, we use an embedded network distributed throughout the environment to approximate the path-planning space and use the network to compute the path in a distributed fashion. The algorithm essentially works as a distributed variant of the popular wave-front path planning algorithm, or a breadth-first search from the goal, propagating paths from the goal location. The embedded nodes make up the vertices of the path planning graph, and the network connections between them are the edges of the graph. Mobile robots can then use reactive navigation to traverse the graph by visiting the vertices (i.e. the embedded nodes) to the goal. In order to respond to changes in the environment this graph has to be maintained as edges are added and removed. For powerefficiency reasons we prefer algorithms that minimize com
TL;DR: A new approach to the real-time collision-free motion planning of dual-arm reconfigurable robots is used, based on configuration space (C-Space), and when the start point and the end point of the dual- arm robot are specified, the algorithm will successfully get the collision- free path real time.
Abstract: Dual-arm reconfigurable robot is a new type of robot. It can adapt to different tasks by changing its different end-effector modules which have standard connectors. Especially, in fast and flexible assembly, it is very important to research the collision-free planning of dual-arm reconfigurable robots. It is to find a continuous, collision-free path in an environment containing obstacles. A new approach to the real-time collision-free motion planning of dual-arm reconfigurable robots is used in the paper. This method is based on configuration space (C-Space). The method of configuration space and the concepts reachable manifold and contact manifold are successfully applied to the collision-free motion planning of dual-arm robot. The complexity of dual-arm robots’ collision-free planning will reduce to a search in a dispersed C-Space. With this algorithm, a real-time optimum path is found. And when the start point and the end point of the dual-arm robot are specified, the algorithm will successfully get the collision-free path real time. A verification of this algorithm is made in the dual-arm horizontal articulated robot SCARATES, and the simulation and experiment ascertain that the algorithm is feasible and effective.
TL;DR: The off-line path planner module of a smart wheelchair aided navigation system is described and a set of optimal subpaths are previously stored in some nodes of the H-graph (path costs are partially materialized).
Abstract: In this paper, the off-line path planner module of a smart wheelchair aided navigation system is described. Environmental information is structured into a hierarchical graph (H-graph) and used either by the user interface or the path planner module. This information structure facilitates efficient path search and easier information access and retrieval. Special path planning issues like planning between floors of a building (vertical path planning) are also viewed. The H-graph proposed is modelled by a tree. The hierarchy of abstractions contained in the tree has several levels of detail. Each abstraction level is a graph whose nodes can represent other graphs in a deeper level of the hierarchy. Path planning is performed using a path skeleton which is built from the deepest abstraction levels of the hierarchy to the most upper levels and completed in the last step of the algorithm. In order not to lose accuracy in the path skeleton generation and speed up the search, a set of optimal subpaths are previously stored in some nodes of the H-graph (path costs are partially materialized). Finally, some experimental results are showed and compared to traditional heuristic search algorithms used in robot path planning.
TL;DR: The coordinated path planning problem for multiple unmanned air vehicles is studied with the proposal of a novel coevolving and cooperating path planner that can take into account different kinds of mission constraints and generate solutions in real time.
01 Mar 2004
TL;DR: A heuristic-based propagation algorithm for solving restricted Markov decision processes (MDPs) that focuses computation towards promising areas of the state space and is able to significantly reduce the amount of processing required in producing a solution.
Abstract: : We present a heuristic-based propagation algorithm for solving restricted Markov decision processes (MDPs). Our approach, which combines ideas from deterministic search and recent dynamic programming methods, focuses computation towards promising areas of the state space. It is thus able to significantly reduce the amount of processing required in producing a solution. We present a number of results comparing our approach to existing algorithms on a robotic path planning domain.