Obstacle Aware Sampling for Path Planning

Proceedings of the International Symposium on Combinatorial Search, 2021

Motion planning in continuous space is a fundamental robotics problem that has been approached from many perspectives. Rapidly-exploring Random Trees (RRTs) use sampling to efficiently traverse the continuous and high-dimensional state space. Heuristic graph search methods use lower bounds on solution cost to focus effort on portions of the space that are likely to be traversed by low-cost solutions. In this paper, we bring these two ideas together in a technique called f-biasing: we use estimates of solution cost, computed as in heuristic search, to guide sparse sampling, as in RRTs. Estimates of solution cost are quickly computed using an abstract version of the problem, then an RRT is constructed by biasing the sampling toward areas of the space traversed by low cost solutions under the abstraction. We show that f-biasing maintains all of the desirable theoretical properties of RRT and RRT*, such as completeness and asymptotic convergence to optimality. We also present experimental results showing that f-biasing finds cheaper paths faster than previous techniques. We see this new technique as strengthening the connections between motion planning in robotics and combinatorial search in artificial intelligence.

Experimental Robotics, 2009

We consider the problem of online planning for a mobile robot among obstacles, where it is impractical to test all possible future paths. One approach is for the runtime task to test some subset of the possible paths and select a path that does not collide with obstacles while advancing the robot toward its goal. Performance depends on the choice of path set. In this paper we assume the path set is fixed and chosen offline. By randomly sampling the space of path sets we discover effective path sets-comparable or superior to the best previously suggested approaches. In addition, testing large numbers of randomly generated path sets yields some insights on the relation of robot performance to the choice of path set.

Electronics, 2016

Motion planning deals with finding a collision-free trajectory for a robot from the current position to the desired goal. For a high-dimensional space, sampling-based algorithms are widely used. Different sampling algorithms are used in different environments depending on the nature of the scenario and requirements of the problem. Here, we deal with the problems involving narrow corridors, i.e., in order to reach its destination the robot needs to pass through a region with a small free space. Common samplers used in the Probabilistic Roadmap are the uniform-based sampler, the obstacle-based sampler, maximum clearance-based sampler, and the Gaussian-based sampler. The individual samplers have their own advantages and disadvantages; therefore, in this paper, we propose to create a hybrid sampler that uses a combination of sampling techniques for motion planning. First, the contribution of each sampling technique is deterministically varied to create time efficient roadmaps. However, this approach has a limitation: The sampling strategy cannot adapt as per the changing configuration spaces. To overcome this limitation, the sampling strategy is extended by making the contribution of each sampler adaptive, i.e., the sampling ratios are determined on the basis of the nature of the environment. In this paper, we show that the resultant sampling strategy is better than commonly used sampling strategies in the Probabilistic Roadmap approach.

2016

This thesis proposes a planner that solves Navigation Among Movable Obstacles problems giving robots the ability to reason about the environment and choose when manipulating obstacles. The planner combines the A*-Search and the exploration strategy of the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. It is locally optimal and independent from the size of the map and from the number, shape, and position of obstacles. It assumes full world knowledge

arXiv (Cornell University), 2021

In this work, we present a novel sampling-based path planning method, called SPRINT. The method finds solutions for high dimensional path planning problems quickly and robustly. Its efficiency comes from minimizing the number of collision check samples. This reduction in sampling relies on heuristics that predict the likelihood that samples will be useful in the search process. Specifically, heuristics (1) prioritize more promising search regions; (2) cull samples from local minima regions; and (3) steer the search away from previously observed collision states. Empirical evaluations show that our method finds shorter or comparable-length solution paths in significantly less time than commonly used methods. We demonstrate that these performance gains can be largely attributed to our approach to achieve sample efficiency.

Abstract. Sampling based planners have been successful in path planning of robots with many degrees of freedom, but still remains ineffective when the configuration space has a narrow passage. We present a new technique based on a random walk strategy to generate samples in narrow regions quickly, thus improving efficiency of Probabilistic Roadmap Planners. The algorithm substantially reduces instances of collision checking and thereby decreases computational time. The method is powerful even for cases where the structure of the narrow passage is not known, thus giving significant improvement over other known methods.

The International Journal of Robotics Research, 2011

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the

2012

Abstract—This paper describes a scalable method for paral-lelizing sampling-based motion planning algorithms. It subdi-vides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequen-tial) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable pe...

2011

This paper introduces space-filling trees and analyzes them in the context of sampling-based motion planning. Space-filling trees are analogous to space-filling curves, but have a branching, tree-like structure, and are defined by an incremental process that results in a tree for which every point in the space has a finite-length path that converges to it. In contrast to space-filling curves, individual paths in the tree are short, allowing any part of the space to be quickly reached from the root. We compare some basic constructions of space-filling trees to Rapidly-exploring Random Trees (RRTs), which underlie a number of popular algorithms used for sampling-based motion planning. We characterize several key tree properties related to path quality and the overall efficiency of exploration and conclude with a number of open mathematical questions.

Advances in Intelligent Systems and Computing, 2014

Sampling-based path planning algorithms are well-known because they are able to find a path in a very short period of time, even in high-dimensional spaces. However, they are non-smooth, random paths far away from the optimum. In this paper we introduce a novel improving technique based on the Fast Marching Method which improves in a deterministic, non-iterative way the initial path provided by a sampling-based methods. Simulation results show that the computation time of the proposed method is low and that path length and smoothness are improved.

2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 2004

9 Abstract by STUART BRUCE MORGAN

International Journal of Interactive Multimedia and Artificial Intelligence, 2022

Sampling-based motion planning in the field of robot motion planning has provided an effective approach to finding path for even high dimensional configuration space and with the motivation from the concepts of sampling based-motion planners, this paper presents a new sampling-based planning strategy called Optimistic Motion Planning using Recursive Sub-Sampling (OMPRSS), for finding a path from a source to a destination sanguinely without having to construct a roadmap or a tree. The random sample points are generated recursively and connected by straight lines. Generating sample points is limited to a range and edge connectivity is prioritized based on their distances from the line connecting through the parent samples with the intention to shorten the path. The planner is analysed and compared with some sampling strategies of probabilistic roadmap method (PRM) and the experimental results show agile planning with early convergence.

IEEE Transactions on Robotics, 2010

This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states.

2010 IEEE Workshop on Advanced Robotics and its Social Impacts, 2010

Springer Tracts in Advanced Robotics, 2009

Motion planning under uncertainty is an important problem in robotics. Although probabilistic sampling is highly successful for motion planning of robots with many degrees of freedom, sampling-based algorithms typically ignore uncertainty during planning. We introduce the notion of a bounded uncertainty roadmap (BURM) and use it to extend samplingbased algorithms for planning under uncertainty in environment maps. The key idea of our approach is to evaluate uncertainty, represented by collision probability bounds, at multiple resolutions in different regions of the configuration space, depending on their relevance for finding a best path. Preliminary experimental results show that our approach is highly effective: our BURM algorithm is at least 40 times faster than an algorithm that tries to evaluate collision probabilities exactly, and it is not much slower than classic probabilistic roadmap planning algorithms, which ignore uncertainty in environment maps.