Probabilistic Roadmap Research Articles

K-Order Surrounding Roadmaps Path Planner for Robot Path Planning

Probabilistic roadmaps are commonly used in robot path planning. Most sampling-based path planners often produce poor-quality roadmaps as they focus on improving the speed of constructing roadmaps without paying much attention to the quality. Poor-quality roadmaps can cause problems such as poor-quality paths, time-consuming path searching and failures in the searching. This paper presents a K-order surrounding roadmap (KSR) path planner which constructs a roadmap in an incremental manner. The planner creates a tree while answering a query, selects the part of the tree according to quality measures and adds the part to an existing roadmap which is obtained in the same way when answering the previous queries. The KSR path planner is able to construct high-quality roadmaps in terms of good coverage, high connectivity, provision of alternative paths and small size. Comparison between the KSR path planner and Reconfigurable Random Forest (RRF), an existing incremental path planner, as well as traditional probabilistic roadmap (PRM) path planner shows that the roadmaps constructed using the KSR path planner have higher quality that those that are built by the other planners.

Using probabilistic roadmaps in changing environments

ABSTRACTIn this paper, we examine how a path planning problem can be solved in changing environments using probabilistic roadmap planners. A probabilistic roadmap is built in static environment where all obstacles are known in advance, but we show that a roadmap can be built in such a way that it works well even when new obstacles are added to the workspace. However, our experiments show that the roadmap graph must be built carefully. We compare three different methods that are used to decide which edges are added to the roadmap graph to connect the nodes. One of these is a distance‐based method, which we present in this paper. In the tests, we built a roadmap by using only the static obstacles. Then, we added additional obstacles to the environment and tested how well the roadmap still worked. The tests showed that our distance‐based method worked quickly and that it produced roadmaps, which could be used to find a path amid additional obstacles with a high success rate.Copyright © 2013 John Wiley & Sons, Ltd.

A real-time motion planning algorithm for a hyper-redundant set of mechanisms

SUMMARYWe introduce a novel probabilistic algorithm (CPRM) for real-time motion planning in the configuration space${\EuScript C}$. Our algorithm differs from a probabilistic road map (PRM) algorithm in the motion between a pair of anchoring points (local planner) which takes place on the boundary of the obstacle subspace${\EuScript O}$. We define a varying potential fieldfon ∂${\EuScript O}$as a Morse function and follow$\vec{\nabla} f$. We then exemplify our algorithm on a redundant worm climbing robot withndegrees of freedom and compare our algorithm running results with those of the PRM.

Randomized path planning with preferences in highly complex dynamic environments

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 planning problems in dynamic environments, where obstacles and region desirability can change in real time, we use dynamic and anytime search exploration strategies. The dynamic strategy allows the planner to replan efficiently by exploiting results from previous planning phases. The anytime strategy starts with a quickly computed path with a potentially low desirability degree which is then incrementally improved depending on the available planning time.

Asymptotically Near-Optimal Planning With Probabilistic Roadmap Spanners

Asymptotically optimal motion planners guarantee that solutions approach optimal as more iterations are performed. A recently proposed roadmap-based method, i.e., the <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{\tt PRM}^{*}$ </tex></formula> approach, provides this desirable property and minimizes the computational cost of generating the roadmap. Even for this method, however, the roadmap can be slow to construct and quickly grows too large for storage or fast online query resolution, especially for relatively high-dimensional instances. In graph theory, there are algorithms that produce sparse subgraphs, which are known as graph spanners, that guarantee near-optimal paths. This paper proposes different alternatives for interleaving graph spanners with the asymptotically optimal <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{\tt PRM}^{*}$</tex></formula> algorithm. The first alternative follows a sequential approach, where a graph spanner algorithm is applied to the output roadmap of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$\hbox{\tt PRM}^{*}$</tex></formula> . The second one is an incremental method, where certain edges are not considered during the construction of the roadmap as they are not necessary for a roadmap spanner. The result in both cases is an asymptotically near-optimal motion planning solution. Theoretical analysis and experiments performed on typical, geometric motion planning instances show that large reductions in construction time, roadmap density, and online query resolution time can be achieved with a small sacrifice of path quality through roadmap spanners.

A Low Dispersion Probabilistic Roadmaps (LD-PRM) Algorithm for Fast and Efficient Sampling-Based Motion Planning

In this paper, we propose a new learning strategy for a probabilistic roadmap (PRM) algorithm. The proposed strategy is based on reducing the dispersion of the generated set of samples. We defined a forbidden range around each selected sample and ignored this region in further sampling. The resultant planner, called low dispersion-PRM, is an effective multi-query sampling-based planner that is able to solve motion planning queries with smaller graphs. Simulation results indicated that the proposed planner improved the performance of the original PRM and other low-dispersion variants of PRM. Furthermore, the proposed planner is able to solve difficult motion planning instances, including narrow passages and bug traps, which represent particularly difficult tasks for classic sampling-based algorithms. For measuring the uniformity of the generated samples, a new algorithm was created to measure the dispersion of a set of samples based on a predetermined resolution.

ROBOT PATH PLANNING IN NARROW PASSAGES BASED ON PROBABILISTIC ROADMAPS

Using the probabilistic roadmap method (PRM), it is difficult to generate an efficient path through narrow passages within a robot workspace due to the unreasonable number of points or milestones present in the limited space.

Probabilistic Roadmaps Based Motion Planning of a 4 Degree of Freedom Robot

In this paper, we used a probabilistic roadmaps(PRM) method to plan a motion path for a 4 degrees of freedom(DOF) robot in static workspace. This methods includes two phases: a learning phase and a query phase. In learning phase, a roadmap is constructed and stored as a graph , in which stores all of the random collision-free configurations in free configuration space denoted by and keeps all of the edges corresponding to feasible paths between these configurations. In query phase, the algorithm tries to connect any given initial and goal configuration to the nodes in the graph. And then the Dijkstra's algorithm searches for a shortest path to concatenate these two nodes. The experiment result demonstrates that this method applying to this 4 degrees of freedom robot works well.

Efficient environment representation for mobile robot path planning using CVT-PRM with Halton sampling

A centroidal Voronoi tessellation-based probabilistic roadmap (CVT-PRM) and its construction method for mobile robot path planning are introduced. The CVT-PRM efficiently encoded the entire unoccupied region of the environment by autonomously rearranging the positions of nodes via CVT and Halton sampling. Simulation results verified that the CVT-PRM can encode the entire unoccupied region efficiently, using evenly distributed nodes.

Sampling-based retraction method for improving the quality of mobile robot path planning

This paper presents a method for improving the quality of the initial path produced by the probabilistic roadmap (PRM)-based mobile robot path planner. The sampling-based retraction method modifies the initial path to achieve approximate maximum safety by removing unsafe and redundant sections. The updated directions and distances of the waypoints on the initial path are determined by approximately modeling clearances around the initial paths using random samples. The proposed method can control the update speed to induce smooth convergence. The performance of the proposed method was verified by simulation.

Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments

RGB-D cameras provide both color images and per-pixel depth estimates. The richness of this data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight. By leveraging results from recent state-of-the-art algorithms and hardware, our system enables 3D flight in cluttered environments using only onboard sensor data. All computation and sensing required for local position control are performed onboard the vehicle, reducing the dependence on an unreliable wireless link to a ground station. However, even with accurate 3D sensing and position estimation, some parts of the environment have more perceptual structure than others, leading to state estimates that vary in accuracy across the environment. If the vehicle plans a path without regard to how well it can localize itself along that path, it runs the risk of becoming lost or worse. We show how the belief roadmap algorithm prentice2009belief, a belief space extension of the probabilistic roadmap algorithm, can be used to plan vehicle trajectories that incorporate the sensing model of the RGB-D camera. We evaluate the effectiveness of our system for controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its limitations.

Hybrid hierarchical trajectory planning for a fixed-wing UCAV performing air-to-surface multi-target attack

This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle (UCAV) performing an air-to-surface multi-target attack (A/SMTA) mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem (TSP), called the dynamic-constrained TSP with neighborhoods (DCTSPN). Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable probabilistic roadmap (PRM) is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmetric TSP (ATSP). Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.

Grid roadmap based ANN corridor search for collision free, path planning

The probabilistic roadmap (PRM) is a force for path planning in static environments. Altogether, a small change in obstacle position may lead to the regeneration of a new road map, invalidating colliding nodes and edges, and then causing a search for a new collision free path. These steps take a considerable amount of time for processing. In this article, a new method, based on the grid roadmap (GRM), is proposed to mitigate the desired time of path planning. By the suggested roadmap and implementation of an Artificial Neural Network (ANN) for a corridor search, a fast path planning method is achieved, which operates on static, dynamic and unseen environments.

Roadmap Coverage Improvement Using a Node Rearrangement Method for Mobile Robot Path Planning

This paper proposes a method to efficiently abstract the traversable regions of a bounded two-dimensional environment using the probabilistic roadmap (PRM) to plan the path for a mobile robot. The proposed method uses centroidal Voronoi tessellation to autonomously rearrange the positions of initially randomly generated nodes. The PRM using the rearranged nodes covers most of the traversable regions in the environment and regularly divides them. The rearranged roadmap reduces the search space of a graph search algorithm and helps to promptly answer arbitrary queries in the environment. The mobile robot path planner using the proposed rearranged roadmap was integrated with a local planner that considers the kinematic properties of a mobile robot, and the efficiency and the safety of the paths were verified by simulation.

A configuration deactivation algorithm for boosting probabilistic roadmap planning of robots

We present a method to improve the execution time used to build the roadmap in probabilistic roadmap planners. Our method intelligently deactivates some of the configurations during the learning phase and allows the planner to concentrate on those configurations that are most likely going to be useful when building the roadmap. The method can be used with many of the existing sampling algorithms. We ran tests with four simulated robot problems typical in robotics literature. The sampling methods applied were purely random, using Halton numbers, Gaussian distribution, and bridge test technique. In our tests, the deactivation method clearly improved the execution times. Compared with pure random selections, the deactivation method also significantly decreased the size of the roadmap, which is a useful property to simplify roadmap planning tasks.

A fast path planning method for single and dual crane erections

Abstract This research aims to develop a method for planning the erection path automatically and efficiently. The proposed method is comprised of two steps. The first step is to convert the scene of the crane erection into a configuration space, in which the crane's load capacity and the obstacles in the environment have been included. The second step is to find a collision-free path in the configuration space by using the probabilistic road map (PRM) method. Three tests were conducted to validate the action, crane placproposed method in this research. The results show that the proposed method is efficient, and can generate effective erection paths for operating in near real-time scenarios. The method is appropriate for both single and dual crane erection, and can help engineers plan more easily, and verify erection-planning decisions such as crane seleement, and logistcs.

Mobile robot 3D map building and path planning based on multi-sensor data fusion

3D map building and path planning serve as two essential tasks for mobile robot to work within a complex outdoor environment. An elevation map built from 3D laser points is utilised to extract terrain feature while ground surface character is acquired from visual information by matching characteristics vectors. By projecting the units of the elevation map into the image, the fusion of terrain feature and ground surface character is achieved using statistical method. Then the units which fuse multi-sensor information are evaluated and given traversable weights to extract constraints for autonomous path planning in outdoor scene. After clustering the units into several regions, a hierarchical path planning strategy uses A* and Probabilistic Roadmap Method (PRM) to plan region paths and unit paths, respectively. The PRM is improved by choosing the units with the largest traversable weights in the regions to ensure a uniform distribution instead of random distribution. Experiment results show the validity and practicability of the proposed approaches.

Pick-and-Place Motion Planning for Manipulators with Dual-Arms

This paper proposes a method for planning pick-and-place motion by using dual-arm manipulators. In the offline phase of our algorithm, we model the surface of object and environment by using a set of planar regions. The contact between the object and the environment is established by selecting a cluster from both the object and the environment. In the online phase, our planner first generates multiple candidates of object pose stably placed on the environment. We then generate multiple motion strategies including the regrasping motion. The pick-and-place motion is generated by performing the grasp planning selecting one of the object pose and one of the motion strategies. We finally generate collision-free pick-and-place motion by using the PRM (Probabilistic Roadmap Method). The effectiveness of the proposed method is confirmed by simulation and experimental results using the dual-arm robot NX-HIRO.

Local Path Planning for Dual-Arm Mobile Robot Shuttling within Truss

A new local path planning method for dual-arm mobile robot shuttling within the truss is presented. Like the probabilistic roadmaps method, this method proceeds in two stages: preprocessing stage and path planning stage. In preprocessing stage, the workspace is divided into a set of non-overlapping cubical cells. The nodes in the free workspace are stored in a matrix. In path planning stage, three query strategies are adopted to search the path from start point to goal point. Take use of vertex query strategy, the smooth path can be acquired in a fraction of a second. The algorithm is simple, and is applicable to any static environment with convex obstacles.

Adaptive sampling for generalized probabilistic roadmaps

In this paper, an adaptive sampling strategy is presented for the generalized sampling-based motion planner, generalized probabilistic roadmap (GPRM). These planners are designed to account for stochastic map and model uncertainty and provide a feedback solution to the motion planning problem. Intelligently sampling in this framework can result in large speedups when compared to naive uniform sampling. By using the information of transition probabilities, encoded in these generalized planners, the proposed strategy biases sampling to improve the efficiency of sampling, and increase the overall success probability of GPRM. The strategy is used to solve the motion planning problem of a fully actuated point robot and a 3-DOF fixed-base manipulator on several maps of varying difficulty levels, and results show that the strategy helps solve the problem efficiently, while simultaneously increasing the success probability of the solution. Results also indicate that these rewards increase with an increase in map complexity.