Sampling-Based Roadmap of Trees for Parallel Motion Planning

This paper shows how to effectively combine a sampling-based method primarily designed for multiple-query motion planning [probabilistic roadmap method (PRM)] with sampling-based tree methods primarily designed for single-query motion planning (expansive space trees, rapidly exploring random trees, and others) in a novel planning framework that can be efficiently parallelized. Our planner not only achieves a smooth spectrum between multiple-query and single-query planning, but it combines advantages of both. We present experiments which show that our planner is capable of solving problems that cannot be addressed efficiently with PRM or single-query planners. A key advantage of our planner is that it is significantly more decoupled than PRM and sampling-based tree planners. Exploiting this property, we designed and implemented a parallel version of our planner. Our experiments show that our planner distributes well and can easily solve high-dimensional problems that exhaust resources available to single machines and cannot be addressed with existing planners.     

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...     

Biologically inspired node generation algorithm for path planning of hyper-redundant manipulators using probabilistic roadmap

This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.     

An Adaptive Rapidly-Exploring Random Tree

Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms have achieved good results in many planning tasks.However,sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages.Therefore,several algorithms have been proposed to overcome these drawbacks.As one of the improved algorithms,Rapidlyexploring random vines(RRV)can achieve better results,but it may perform worse in cluttered environments and has a certain environmental selectivity.In this paper,we present a new improved planning method based on RRT-Connect and RRV,named adaptive RRT-Connect(ARRT-Connect),which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments.The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.     

Motion planning algorithms for molecular simulations: A survey

Motion planning is a fundamental problem in robotics that has motivated research since more than three decades ago. A large variety of algorithms have been proposed to compute feasible motions of multi-body systems in constrained workspaces. In recent years, some of these algorithms have surpassed the frontiers of robotics, finding applications in other domains such as industrial manufacturing, computer animation and computational structural biology. This paper concerns the latter domain, providing a survey on motion planning algorithms applied to molecular modeling and simulation. Both the algorithmic and application sides are discussed, as well as the different issues to be taken into consideration when extending robot motion planning algorithms to deal with molecules. From an algorithmic perspective, the paper gives a general overview of the different extensions to sampling-based motion planners. From the point of view of applications, the survey deals with problems involving protein folding and conformational transitions, as well as protein–ligand interactions.     

A Learning-based Multi-RRT Approach for Robot Path Planning in Narrow Passages

As an important class of sampling-based path planning methods, the Rapidly-exploring Random Trees (RRT) algorithm has been widely studied and applied in the literature. In RRT, how to select a tree to extend or connect is a critical factor, which will greatly influence the efficiency of path planning. In this paper, a novel learning-based multi-RRTs (LM-RRT) approach is proposed for robot path planning in narrow passages. The LM-RRT approach models the tree selection process as a multi-armed bandit problem and uses a reinforcement learning algorithm that learns action values and selects actions with an improved ε-greedy strategy (ε _t -greedy). Compared with previous RRT algorithms, LM-RRT can not only enhance the local space exploration ability of each tree, but also guarantee the efficiency of global path planning. The probabilistic completeness and combinatory optimality of LM-RRT are proved based on the geometric characteristics of the configuration space. Simulation and experimental results show the effectiveness of the proposed LM-RRT approach in single-query path planning problems with narrow passages.     

Path planning for manipulators based on an improved probabilistic roadmap method

An indispensable feature of an intelligent manipulator is its capability to quickly plan a short and safe path in the presence of obstacles in its workspace. Among the path planning methods, the probabilistic roadmap (PRM) method has been widely applied in path planning for a high-dimensional manipulator to avoid obstacles. However, its efficiency remains disappointing when the free space of manipulators contains narrow passages. To solve this problem, an improved PRM method is proposed in this paper. Based on a virtual force field, a new sampling strategy of PRM is presented to generate configurations more appropriate for practical application in the free space. Correspondingly, in order to interconnect these configurations to form a roadmap, a new connection strategy is designed, which consists of three stages and can gradually improve the connectivity of the roadmap. The contributions of this paper are as follows. The new sampling strategy can increase the sampling density at the narrow passages of the free space and reduce the redundancy of the samples in the wide-open regions of the free space; the three-stage connection strategy for interconnecting samples can ensure a high-connectivity roadmap; through synthesizing the above strategies, the improved PRM method is more suitable for path planning of manipulators to avoid obstacles efficiently in a cluttered environment. Simulations and experiments are carried out to evaluate the validity of the proposed method, and the method is available for manipulator of any degrees of freedom.     

A Connectivity-Based Method for Enhancing Sampling in Probabilistic Roadmap Planners

The motion planning is a difficult problem but nevertheless, a crucial part of robotics. The probabilistic roadmap planners have shown to be an efficient way to solve these planning problems. In this paper, we present a new algorithm that is based on the principles of the probabilistic roadmap planners. Our algorithm enhances the sampling by intelligently detecting which areas of the configuration space are easy and which parts are not. The algorithm then biases the sampling only to the difficult areas that may contain narrow passages. Our algorithm works by dividing the configuration space into regions at the beginning and then sampling configurations inside each region. Based on the connectivity of the roadmap inside each region, our algorithm aims to detect whether the region is easy or difficult. We tested our algorithm with three different simulated environments and compared it with two other planners. Our experiments showed that with our method it is possible to achieve significantly better results than with other tested planners. Our algorithm was also able to reduce the size of roadmaps.     

Global motion planning under uncertain motion, sensing, and environment map

Uncertainty in motion planning is often caused by three main sources: motion error, sensing error, and imperfect environment map. Despite the significant effect of all three sources of uncertainty to motion planning problems, most planners take into account only one or at most two of them. We propose a new motion planner, called Guided Cluster Sampling (GCS), that takes into account all three sources of uncertainty for robots with active sensing capabilities. GCS uses the Partially Observable Markov Decision Process (POMDP) framework and the point-based POMDP approach. Although point-based POMDPs have shown impressive progress over the past few years, it performs poorly when the environment map is imperfect. This poor performance is due to the extremely high dimensional state space, which translates to the extremely large belief space?B. We alleviate this problem by constructing a more suitable sampling distribution based on the observations that when the robot has active sensing capability, B can be partitioned into a collection of much smaller sub-spaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. Utilizing these observations, GCS samples B in two-stages, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Simulation results on marine robotics scenarios suggest that GCS can generate reasonable policies for motion planning problems with uncertain motion, sensing, and environment map, that are unsolvable by the best point-based POMDPs today. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, given enough time, GCS converges to the optimal policy. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space.     

Triple RRTs: An Effective Method for Path Planning in Narrow Passages

Although Rapidly-exploring Random Trees (RRTs) have been successfully applied in path planning of robots with many degrees of freedom under non-holonomic and differential constraints, rapidly identifying and passing through narrow passages in a robot's configuration space remains a challenge for RRTs-based planners. This paper presents a novel two-stage approach to address the problem of multi-d.o.f. robot path planning in high-dimensional configuration space with narrow corridors. The first stage introduces an efficient sampling algorithm called Bridge Test to find a global roadmap that identifies the critical region. The second stage presents two varieties of RRTs, called Triple-RRTs, to search for a local connection under the guidance of the global landmark. The two-stage strategy keeps a fine balance between global heuristics and local connection, resulting in high performance over the previous RRTs-based path planning methods. We have implemented the Triple-RRTs planners for both rigid and articulated robots in two- and three-dimensional environments. Experimental results demonstrate the effectiveness of the proposed method.     

Optimisation based path planning for car parking in narrow environments

In the last decades, a large variety of robot motion planners emerged. However, manoeuvring in very narrow environments, e.g., for common parking scenarios, cannot be reliably handled with existing path planners at low computing costs. This is why, this paper presents a fast optimisation based path planner which specialises on narrow environments. In the proposed approach, the kinematic differential equations are discretised. For the resulting discrete path segments, a static optimisation problem is formulated to determine the path independently of the considered scenario. In each iteration step, the path length is also optimised to handle close distances to the obstacles as well as longer driving distances. Due to the local nature heuristic rules for driving direction changes are formulated which intend to imitate the behaviour of a human driver. The drawback of the local nature is the lack of global information to handle scenarios with obstacles blocking the path. To overcome this problem, a tree-based guidance for the local planner is introduced. The landmarks for the tree are implicitly created by the local planner allowing an efficient exploration of the free configuration space. The performance of the algorithm is evaluated utilising Monte-Carlo simulations and compared to state-of-the-art path planning algorithms.     

基于随机采样的运动规划综述

综述了基于随机采样的运动规划方法．首先介绍了基于随机采样的运动规划的发展历程和几种典型方法;然后分析了这一类规划方法的重要性质及目前仍然存在的问题;最后展望了基于随机采样的运动规划的应用前景．     

Efficient asymptotically-optimal path planning on manifolds

This paper presents an efficient approach for asymptotically-optimal path planning on implicitly-defined configuration spaces. Recently, several asymptotically-optimal path planners have been introduced, but they typically exhibit slow convergence rates. Moreover, these planners cannot operate on the configuration spaces that appear in the presence of kinematic or contact constraints, such as when manipulating an object with two arms or with a multifingered hand. In these cases, the configuration space usually becomes an implicit manifold embedded in a higher-dimensional joint ambient space. Existing sampling-based path planners on manifolds focus on finding a feasible solution, but they do not optimize the quality of the path in any sense and, thus, the returned solution is usually not adequate for direct execution. In this paper, we adapt several techniques to accelerate the convergence of the asymptotically-optimal planners and we use higher-dimensional continuation tools to deal with the case of implicitly-defined configuration spaces. The performance of the proposed approach is evaluated through various experiments.     

Narrow passage sampling for probabilistic roadmap planning

Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but sampling narrow passages in a robot's configuration space remains a challenge for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which reduces sample density in many unimportant parts of a configuration space, resulting in increased sample density in narrow passages. The bridge test can be implemented efficiently in high-dimensional configuration spaces using only simple tests of local geometry. The strengths of the bridge test and uniform sampling complement each other naturally. The two sampling strategies are combined to construct the hybrid sampling strategy for our planner. We implemented the planner and tested it on rigid and articulated robots in 2-D and 3-D environments. Experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.     

Constrained motion planning for robot manipulators using local geometric information

This paper proposes a new motion planning algorithm for robot manipulator systems with path constraints. The constraint function of a manipulator determines the subspace of its joint space, and a proposed sampling-based algorithm can find a path that connects valid samples in the subspace. These valid samples can be obtained by projecting the samples onto the subspace defined by the constraint function. However, these iteratively generated samples easily fall into local optima, which degrades the search performance. The proposed algorithm uses the local geometric information and expands the search tree adaptively to avoid the local convergence problem. It increases the greediness of the search tree when it expands toward an unexplored area, which produces the benefit of reducing computational time. In order to demonstrate the performance of the algorithm, it is applied to two example problems: a maze problem using PUMA 560 under predefined constraints and a closed-chain problem using two Selective Compliance Assembly Robot Arms. The results are compared with those obtained with an existing algorithm to show the improvement in performance.     

EKEMAS,an agent-based geo-simulation framework to support continual planning in the real-word

In this paper, we propose an agent-based geo-simulation framework EKEMAS to assist human planners when planning under strong spatial constraints in a real large-scale space. The approach consists in drawing a parallel between the real environment (for example, a forest in fire) and the simulated environment based on GIS data. This virtual environment uses software agents which are aware of the space and equipped with advanced spatial reasoning capabilities. In addition, we suggest some enhancements for the Continual Planning approach. Our aim is to demonstrate how EKEMAS, when coupled with a continual planning approach and agent’s spatial reasoning capabilities, can assist human planners overcoming obstacles related to real world constraints: dynamic, uncertain, and spatially constrained environment. We illustrate this idea on the forest firefighting problem and we use MAGS as a simulation platform and Prometheus as a fire simulator. Finally, and since plans in the studied case (wildfire fighting) are mainly paths, we also propose a new approach based on agent geo-simulation in order to solve particular Pathfinding problems.     

Sampling-based robot motion planning: Towards realistic applications

This paper presents some of the recent improvements in sampling-based robot motion planning. Emphasis is placed on work that brings motion-planning algorithms closer to applicability in real environments. Methods that approach increasingly difficult motion-planning problems including kinodynamic motion planning and dynamic environments are discussed. The ultimate goal for such methods is to generate plans that can be executed with few modifications in a real robotics mobile platform.     

A Fast Approach for Robot Motion Planning

This paper describes a new approach to robot motion planning that combines the end-point motion planning with joint trajectory planning for collision avoidance of the links. Local and global methods are proposed for end-point motion planning. The joint trajectory planning is achieved through a pseudoinverse kinematic formulation of the problem. This approach enables collision avoidance of the links by a fast null-space vector computation. The power of the proposed planner derives from: its speed; the good properties of the potential function for end-point motion planning; and from the simultaneous avoidance of the links collision, kinematic singularities, and local minima of the potential function. The planner is not defined over computationally expensive configuration space and can be applied for real-time applications. The planner shows to be faster than many previous planners and can be applied to robots with many degrees of freedom. The effectiveness of the proposed local and global planning methods as well as the general robot motion planning approach have been experimented using the computer-simulated robots. Some of the simulation results are included in this paper.     

Automatic motion planning for complex welding problems by considering angular redundancy

Automatic motion planning in complex environment is significant in manufacturing. This paper presents an off-line collision-free motion planning algorithm by considering the task redundancy existing in manufacturing. The paper takes a typical welding technique as an example, which mainly aims at solving the complex continuous welding motion planning problems. In the proposed algorithm, the angular redundancy existing in the welding process is fully considered for planning and optimizing the welding torch path by minimizing the torch angular cost. Besides, some strategies have been introduced to improve the efficiency of the proposed algorithm, such as the heuristic region sampling strategy based on Gaussian sampling, which is adopted to guide planning. Midpoint collision checking strategy is employed to improve the efficiency of the collision checking. The proposed algorithm is very effective in solving the complex welding motion planning problems, such as in the welding environment where the weld seam is situated in the narrow passage or the dense obstacles. The experiments are carried out to verify that our proposed algorithm is feasible in the relevant scenarios. All the experimental results show that not only the proposed algorithm could find a feasible collision-free path in the different complex environments if any path exists, but also the torch angle could be optimized with the increase of iteration.