Path planning is an active area of research essential for many applications in robotics. Popular techniques include graph-based searches and sampling-based planners. These approaches are powerful but have limitations. This paper continues work to combine their strengths and mitigate their limitations using a unified planning paradigm. It does this by viewing the path planning problem as the two subproblems of search and approximation and using advanced graph-search techniques on a sampling-based approximation. This perspective leads to Advanced BIT*. ABIT* combines truncated anytime graph-based searches, such as ATD*, with anytime almost-surely asymptotically optimal sampling-based planners, such as RRT*. This allows it to quickly find initial solutions and then converge towards the optimum in an anytime manner. ABIT* outperforms existing single-query, sampling-based planners on the tested problems in $\mathbb{R}^{4}$ and $\mathbb{R}^{8}$, and was demonstrated on real-world problems with NASA/JPL-Caltech.
TU Berlin（柏林工业大学）；BITS Pilani（比尔拉科学技术学院皮拉尼分校）；KTH Royal Institute of Technology（KTH皇家理工学院）
Optimal sampling based motion planning and trajectory optimization are two competing frameworks to generate optimal motion plans. Both frameworks have complementary properties: Sampling based planners are typically slow to converge, but provide optimality guarantees. Trajectory optimizers, however, are typically fast to converge, but do not provide global optimality guarantees in nonconvex problems, e.g. scenarios with obstacles. To achieve the best of both worlds, we introduce a new planner, BITKOMO, which integrates the asymptotically optimal Batch Informed Trees (BIT*) planner with the K-Or…
Path planning through complex obstacle spaces is a fundamental requirement of many mobile robot applications. Recently a rapid convergence path planning algorithm, Batch Informed Trees (BIT*), was introduced. This work serves as a concise write-up and explanation of BIT*. This work includes a description of BIT* and how BIT* operates, a graphical demonstration of BIT*, and simulation results where BIT* is compared to Optimal Rapidly-exploring Random Trees (RRT*).
Carnegie Mellon University（卡内基梅隆大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Toronto（多伦多大学）；University of Waterloo（滑铁卢大学）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）
Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as respectively used by informed graph-based searches and anytime sampling-based planners. Informed graph-based searches, such as A*, traditionally use heuristics to search a priori graphs in order of potential solution quality. This makes their search efficient but leaves their performance dependent on the cho…
Automatic welding of tubular TKY joints is an important and challenging task for the marine and offshore industry. In this paper, a framework for tubular joint detection and motion planning is proposed. The pose of the real tubular joint is detected using RGB-D sensors, which is used to obtain a real-to-virtual mapping for positioning the workpiece in a virtual environment. For motion planning, a Bi-directional Transition based Rapidly exploring Random Tree (BiTRRT) algorithm is used to generate trajectories for reaching the desired goals. The complete framework is verified with experiments, a…
RIT*: Riemannian Informed Trees for Cost-Adaptive Optimal Motion Planning
Center for Autonomous Robotic Systems, Khalifa University（哈利法大学自主机器人系统中心）；Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya（加泰罗尼亚理工大学工业与控制工程研究所）
Sampling-based motion planners such as RRT* and BIT*, when applied to kinodynamic motion planning, rely on steering functions to generate time-optimal solutions connecting sampled states. Implementing exact steering functions requires either analytical solutions to the time-optimal control problem, or nonlinear programming (NLP) solvers to solve the boundary value problem given the system's kinodynamic equations. Unfortunately, analytical solutions are unavailable for many real-world domains, and NLP solvers are prohibitively computationally expensive, hence fast and optimal kinodynamic motion…
Stanford University（斯坦福大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
We propose an algorithmic framework for efficient anytime motion planning on large dense geometric roadmaps, in domains where collision checks and therefore edge evaluations are computationally expensive. A large dense roadmap (graph) can typically ensure the existence of high quality solutions for most motion-planning problems, but the size of the roadmap, particularly in high-dimensional spaces, makes existing search-based planning algorithms computationally expensive. We deal with the challenges of expensive search and collision checking in two ways. First, we frame the problem of anytime m…
Czech Technical University in Prague（布拉格捷克理工大学）；International University of Rabat（拉巴特国际大学）
Multi-rotor Unmanned Aerial Vehicles (UAVs) need to tilt in order to move; this modifies the UAV's antenna orientation. We consider the scenario where a multi-rotor UAV serves as a communication relay between a Base Station (BS) and another UAV. We propose a framework to generate feasible trajectories for the multi-rotor UAV relay while considering its motion dynamics and the motion-induced changes of the antenna orientation. The UAV relay's trajectory is optimized to maximize the end-to-end number of bits transmitted. Numerical simulations in MATLAB and Gazebo show the benefits of accounting …
Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and used to order the search. The practical improvement of this informed search depends on the accuracy of the heuristic. Selecting an appropriate heuristic is difficult. Heuristics applicable to an entire problem domain are often simple to define and inexpensive to evaluate but may not be beneficial for a specific problem instance. Heuristics specific to a problem instance are often difficult to define or expensive to ev…
Lexicographic Minimum-Violation Motion Planning using Signal Temporal Logic
School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算信息与技术学院）；ZF Friedrichshafen AG（弗赖堡汽车集团）；Institute of System Dynamics, HTWG Konstanz（康斯坦茨HTWG系统动力学研究所）
Bosch Center for Artificial Intelligence（博世人工智能中心）；Technical University of Berlin（柏林工业大学）；RWTH Aachen University（亚琛工业大学）
Automated bin-picking is a prerequisite for fully automated manufacturing and warehouses. To successfully pick an item from an unstructured bin the robot needs to first detect possible grasps for the objects, decide on the object to remove and consequently plan and execute a feasible trajectory to retrieve the chosen object. Over the last years significant progress has been made towards solving these problems. However, when multiple robot arms are cooperating the decision and planning problems become exponentially harder. We propose an integrated multi-arm bin-picking pipeline (IMAPIP), and de…
Motion planning is a computationally intensive and well-studied problem in autonomous robots. However, motion planning hardware accelerators (MPA) must be soft-error resilient for deployment in safety-critical applications, and blanket application of traditional mitigation techniques is ill-suited due to cost, power, and performance overheads. We propose Collision Exposure Factor (CEF), a novel metric to assess the failure vulnerability of circuits processing spatial relationships, including motion planning. CEF is based on the insight that the safety violation probability increases with the s…
Communication-Aware Map Compression for Online Path-Planning: A Rate-Distortion Approach
Department of Aerospace Engineering of Georgia Institute of Technology（佐治亚理工学院航空航天工程系）；Department of Electrical and Computer Engineering, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校电气与计算机工程系）
An Incremental Sampling and Segmentation-Based Approach for Motion Planning Infeasibility
Robotics Research Center, IIIT Hyderabad（IIIT海得拉巴机器人研究中心）；Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genoa（热那亚大学信息学院、生物工程、机器人与系统工程系）
Popular navigation stacks implemented on top of open-source frameworks such as ROS(Robot Operating System) and ROS2 represent the robot workspace using a discretized 2D occupancy grid. This method, while requiring less computation, restricts the use of such navigation stacks to wheeled robots navigating on flat surfaces. In this paper, we present a navigation stack that uses a volumetric representation of the robot workspace, and hence can be extended to aerial and legged robots navigating through uneven terrain. Additionally, we present a new sampling-based motion planning algorithm which int…
Flexible Informed Trees (FIT*): Adaptive Batch-Size Approach in Informed Sampling-Based Path Planning
Department of Informatics, Technical University of Munich（信息学院，慕尼黑技术大学）；Department of Technology & Innovation, KION Group Linde Material Handling GmbH（技术与创新学院，KION集团Linde物料处理 GmbH）
Carnegie Mellon University（卡内基梅隆大学）；Canadian Space Agency（加拿大航天局）；Defence Research and Development Canada（加拿大国防研究与发展部）；Massachusetts Institute of Technology（麻省理工学院）；National Aeronautics and Space Administration（美国国家航空航天局）；Northern Centre for Advanced Technology Inc.（北方先进技术中心公司）；Ontario Drive and Gear Ltd.（安大略传动与齿轮有限公司）；Office of Naval Research（美国海军研究办公室）；University of Toronto（多伦多大学）；University of Waterloo（滑铁卢大学）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based techniques, such as A*, with the anytime scalability of sampling-based algorithms, such as Rapidly-exploring Random Trees (RRT). BIT* uses a heuristic to efficiently search a series of increasingly dense implicit RGGs while reusing previous information. It can be viewed as an extension of incremental g…
Institute for Computer Science and Control（计算机科学与控制研究所）；Széchenyi István University（塞切尼伊什特万大学）；Eindhoven University of Technology（埃因霍温理工大学）
The paper proposes two control methods for performing a backflip maneuver with miniature quadcopters. First, an existing feedforward control approach is improved by finding the optimal sequence of motion primitives via Bayesian optimization, using a surrogate Gaussian Process model. To evaluate the cost function, the flip maneuver is performed repeatedly in a simulation environment. The second method is based on closed-loop control and it consists of two main steps: first a novel robust, adaptive controller is designed to provide reliable reference tracking even in case of model uncertainties.…
The problem of path planning in unknown environments remains a challenging problem - as the environment is gradually observed during the navigation, the underlying planner has to update the environment representation and replan, promptly and constantly, to account for the new observations. In this paper, we present a visibility graph-based planning framework capable of dealing with navigation tasks in both known and unknown environments. The planner employs a polygonal representation of the environment and constructs the representation by extracting edge points around obstacles to form enclose…
