181 research outputs found

    Sampling-Based Motion Planning Using Predictive Models

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    Robotic motion planning requires configuration space exploration. In high-dimensional configuration spaces, a complete exploration is computationally intractable. Practical motion planning algorithms for such high-dimensional spaces must expend computational resources in proportion to the local complexity of configuration space regions. We propose a novel motion planning approach that addresses this problem by building an incremental, approximate model of configuration space. The information contained in this model is used to direct computational resources to difficult regions, effectively addressing the narrow passage problem by adapting the sampling density to the complexity of that region. In addition, the expressiveness of the model permits predictive edge validations, which are performed based on the information contained in the model rather then by invoking a collision checker. Experimental results show that the exploitation of the information obtained through sampling and represented in a predictive model results in a significant decrease in the computational cost of motion planning

    Efficient Path Planning in Narrow Passages via Closed-Form Minkowski Operations

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    Path planning has long been one of the major research areas in robotics, with PRM and RRT being two of the most effective classes of path planners. Though generally very efficient, these sampling-based planners can become computationally expensive in the important case of "narrow passages". This paper develops a path planning paradigm specifically formulated for narrow passage problems. The core is based on planning for rigid-body robots encapsulated by unions of ellipsoids. The environmental features are enclosed geometrically using convex differentiable surfaces (e.g., superquadrics). The main benefit of doing this is that configuration-space obstacles can be parameterized explicitly in closed form, thereby allowing prior knowledge to be used to avoid sampling infeasible configurations. Then, by characterizing a tight volume bound for multiple ellipsoids, robot transitions involving rotations are guaranteed to be collision-free without traditional collision detection. Furthermore, combining the stochastic sampling strategy, the proposed planning framework can be extended to solving higher dimensional problems in which the robot has a moving base and articulated appendages. Benchmark results show that, remarkably, the proposed framework outperforms the popular sampling-based planners in terms of computational time and success rate in finding a path through narrow corridors and in higher dimensional configuration spaces

    Sensory Steering for Sampling-Based Motion Planning

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    Sampling-based algorithms offer computationally efficient, practical solutions to the path finding problem in high-dimensional complex configuration spaces by approximately capturing the connectivity of the underlying space through a (dense) collection of sample configurations joined by simple local planners. In this paper, we address a long-standing bottleneck associated with the difficulty of finding paths through narrow passages. Whereas most prior work considers the narrow passage problem as a sampling issue (and the literature abounds with heuristic sampling strategies) very little attention has been paid to the design of new effective local planners. Here, we propose a novel sensory steering algorithm for sampling- based motion planning that can “feel” a configuration space locally and significantly improve the path planning performance near difficult regions such as narrow passages. We provide computational evidence for the effectiveness of the proposed local planner through a variety of simulations which suggest that our proposed sensory steering algorithm outperforms the standard straight-line planner by significantly increasing the connectivity of random motion planning graphs. For more information: Kod*la

    Discrete Search Leading Continuous Exploration for Kinodynamic Motion Planning

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    This paper presents the Discrete Search Leading continuous eXploration (DSLX) planner, a multi-resolution approach to motion planning that is suitable for challenging problems involving robots with kinodynamic constraints. Initially the method decomposes the workspace to build a graph that encodes the physical adjacency of the decomposed regions. This graph is searched to obtain leads, that is, sequences of regions that can be explored with sampling-based tree methods to generate solution trajectories. Instead of treating the discrete search of the adjacency graph and the exploration of the continuous state space as separate components, DSLX passes information from one to the other in innovative ways. Each lead suggests what regions to explore and the exploration feeds back information to the discrete search to improve the quality of future leads. Information is encoded in edge weights, which indicate the importance of including the regions associated with an edge in the next exploration step. Computation of weights, leads, and the actual exploration make the core loop of the algorithm. Extensive experimentation shows that DSLX is very versatile. The discrete search can drastically change the lead to reflect new information allowing DSLX to find solutions even when sampling-based tree planners get stuck. Experimental results on a variety of challenging kinodynamic motion planning problems show computational speedups of two orders of magnitude over other widely used motion planning methods

    A Motion Planner For Robot Manipulators Based on Support Vector Machines

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    ABSTRACT Moving a robot between two configurations without making a collision is of high importance in planning problems. Sampling-based planners have gained popularity due to their acceptable performance in practical situations. This body of work introduces the notion of a risk function that is provided using the Support Vector Machine (SVM) algorithm to find safe configurations in a sampled configuration space. A configuration is called safe if it is placed at maximum dis­tance from surrounding obstacle samples. Compared to previous solutions, this function is less sensitive to a selected sampling method and resolution. The proposed function is first used as a repulsive potential field in a local SVM-based planner. Afterwards, a global planner using the notion of the risk function is suggested to address some of the shortcomings of the suggested local planner. The proposed global planner is able to solve a problem with fewer number of milestones and less number of referrals to the collision detection module in comparison to the classical Probabilistic Roadmap Planner (PRM). The two proposed methods are evaluated in both simulated and experimental environments and the results are reported

    Collaborative Motion Planning

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    Planning motion is an essential component for any autonomous robotic system. An intelligent agent must be able to efficiently plan collision-free paths in order to move through its world. Despite its importance, this problem is PSPACE-Hard which means that even planning motions for simple robots is computationally difficult. State-of-the-art approaches trade completeness (always able to provide a solution if one exists or report none exists) for probabilistic completeness (probabilistically guaranteed to find a solution if one exists but cannot report if none exists) and improved efficiency. These methods use sampling-based techniques to design a sequence of motions for the robot. However, as these methods are random in nature, the probability of their success is directly related to the expansiveness, or openness, of the underlying planning space. In other words, narrow passages, complex systems, and various constraints make planning with these methods difficult. On the other hand, humans can often determine approximate solutions for these difficult solutions quickly. In this research, we explore user-guided planning in which a human operator works together with a sampling-based motion planner. By having a human-in-the-loop, a human can steer a sampling-based planner towards a solution. This strategy can provide benefits to many applications such as computer-aided design and virtual prototyping, to name a few. We begin by classifying and creating simple models of common user-guided and heuristic-guided motion planning methods. Our models encompass three forms of user input: configuration-based, path-based, and region-based input. We compare and contrast these approaches and motivate our choice of a region-based collaborative framework. Through this analysis, we gain insight into user-guided planning and further motivate methods that harness low interface complexity and work entirely in workspace, which is most natural to a human operator. Further, we extend the theory of expansiveness to analyze the various types of user inputs. Our novel region-based collaboration framework takes advantage of human intuition by allowing a user to define regions in the workspace to bias and/or constrain the search space of a sampling-based motion planner. This approach allows a user to bias a high dimensional search with low dimensional input, supports intermittent user hints, and empowers a user to customize motion solutions. Finally, we extend region steering to both non-holonomic robotic systems and a human-inspired approach to motion planning. Our results show that this region-based framework can aid many variants of sampling-based planning, reduce computation time, support solution customization, and can be used to develop advanced heuristic methods for solving motion planning problems. We provide experiments exemplifying our approach in planning motions for complex robotic applications such as mobile manipulators, car-like, and free-flying robots

    Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective

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    Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage. In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient. The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions. Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems

    Improved Connected-Component Expansion Strategies for Sampling-Based Motion Planning

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    Motion planning is the problem of computing valid paths through an environment. Since computing exact solutions is intractable, sampling-based algorithms, such as Probabilistic RoadMaps (PRMs), have gained popularity. PRMs compute an approximate mapping of the planning space by sacrificing completeness in favor of efficiency. However, these algorithms have certain bottlenecks that hinder performance, causing difficulty mapping narrow or crowded regions, with the asymptotic bottleneck of these algorithms being the nearest-neighbor queries required to connect the roadmap. Thus, roadmaps may fail to efficiently capture the connectivity of the planning space. In this thesis, we present a set of connected component (CC) expansion algorithms, each with different biases (random expand, expand to the nearest CC, expand away from the host CC, and expand along the medial-axis) and expansion node selection policies (random, farthest from CC's centroid, and difficult nodes), that create a linked-chain of configurations designed to enable efficient connection of CCs. Given an a priori roadmap quality requirement in terms of roadmap connectivity, we show that when our expansion methods augment PRMs, we reach the required roadmap connectivity in less time
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