URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning
Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore（国家大学学院整合科学与工程计划）；Advanced Robotics Centre, National University of Singapore（国家大学新加坡先进机器人中心）；Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore（设计与工程学院机械工程系）；Singapore MIT Alliance for Research and Technology (SMART) centre（新加坡MIT研究与技术联盟中心）
Fengkang Ying, Hanwen Zhang, Haozhe Wang, Huishi Huang, Marcelo H. Ang
Collision-free motion planning for redundant robot manipulators in complex environments is yet to be explored. Although recent advancements at the intersection of deep reinforcement learning (DRL) and robotics have highlighted its potential to handle versatile robotic tasks, current DRL-based collision-free motion planners for manipulators are highly costly, hindering their deployment and application. This is due to an overreliance on the minimum distance between the manipulator and obstacles, inadequate exploration and decision-making by DRL, and inefficient data acquisition and utilization. In this article, we propose URPlanner, a universal paradigm for collision-free robotic motion planning based on DRL. URPlanner offers several advantages over existing approaches: it is platform-agnostic, cost-effective in both training and deployment, and applicable to arbitrary manipulators without solving inverse kinematics. To achieve this, we first develop a parameterized task space and a universal obstacle avoidance reward that is independent of minimum distance. Second, we introduce an augmented policy exploration and evaluation algorithm that can be applied to various DRL algorithms to enhance their performance. Third, we propose an expert data diffusion strategy for efficient policy learning, which can produce a large-scale trajectory dataset from only a few expert demonstrations. Finally, the superiority of the proposed methods is comprehensively verified through experiments.
Birla Institute of Technology and Science (BITS) Pilani（皮拉尼伯拉理工学院（BITS皮拉尼））；University of Nevada, Reno（内华达大学雷诺分校）
This paper presents the design concept, modeling and motion planning solution for the aerial robotic chain. This design represents a configurable robotic system of systems, consisting of multi-linked micro aerial vehicles that simultaneously presents the ability to cross narrow sections, morph its shape, ferry significant payloads, offer the potential of distributed sensing and processing, and allow system extendability. We contribute an approach to address the motion planning problem of such a connected robotic system of systems, making full use of its reconfigurable nature, to find collision…
This paper presents a point cloud based robotic system for arc welding. Using hand gesture controls, the system scans partial point cloud views of workpiece and reconstructs them into a complete 3D model by a linear iterative closest point algorithm. Then, a bilateral filter is extended to denoise the workpiece model and preserve important geometrical information. To extract the welding seam from the model, a novel intensity-based algorithm is proposed that detects edge points and generates a smooth 6-DOF welding path. The methods are tested on multiple workpieces with different joint types an…
While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration compl…
Robotics Research Center, IIIT Hyderabad（国际信息技术研究所海得拉巴分校机器人研究中心）；TCS Research（塔塔咨询服务公司研究部）
Table-top Rearrangement and Planning is a challenging problem that relies heavily on an excellent perception stack. The perception stack involves observing and registering the 3D scene on the table, detecting what objects are on the table, and how to manipulate them. Consequently, it greatly influences the system's task-planning and motion-planning stacks that follow. We present a comprehensive overview and discuss the different challenges associated with the perception module. This work is a result of our extensive involvement in the ICRA 2022 Open Cloud Robot Table Organization Challenge, in…
In this paper, we explore the task of robot sculpting. We propose a search based planning algorithm to solve the problem of sculpting by material removal with a multi-axis manipulator. We generate collision free trajectories for a manipulator using best-first search in voxel space. We also show significant speedup of our algorithm by using octrees to decompose the voxel space. We demonstrate our algorithm on a multi-axis manipulator in simulation by sculpting Michelangelo's Statue of David, evaluate certain metrics of our algorithm and discuss future goals for the project.
CSIRO（澳大利亚联邦科学与工业研究组织）；University of Moratuwa（莫拉图瓦大学）
Autonomous navigation systems based on computer vision sensors often require sophisticated robotics platforms which are very expensive. This poses a barrier for the implementation and testing of complex localization, mapping, and navigation algorithms that are vital in robotics applications. Addressing this issue, in this work, Robot Operating System (ROS) supported mobile robotics platforms are compared and an end-to-end implementation of an autonomous navigation system based on a low-cost educational robotics platform, AlphaBot2 is presented, while integrating the Intel RealSense D435 camera…
Indian Institute of Technology Kharagpur（印度理工学院哈拉格普尔分校）
Sampling based methods are widely used for robotic motion planning. Traditionally, these samples are drawn from probabilistic ( or deterministic ) distributions to cover the state space uniformly. Despite being probabilistically complete, they fail to find a feasible path in a reasonable amount of time in constrained environments where it is essential to go through narrow passages (bottleneck regions). Current state of the art techniques train a learning model (learner) to predict samples selectively on these bottleneck regions. However, these algorithms depend completely on samples generated …
VIP: Variation-based Iterative-learning Planning for Robotic Navigation
School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；Tianmushan Laboratory, Beihang University（北京航空航天大学天目山实验室）
Assessing the Value of Visual Input: A Benchmark of Multimodal Large Language Models for Robotic Path Planning
Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Aichi, Japan（Nagoya大学微纳米机械科学与工程系）；Institutes of Innovation for Future Society, Nagoya University, Nagoya, Aichi, Japan（Nagoya大学未来社会创新研究所）
We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic systems. We present Risk-Averse Nonlinear Steering RRT* (RANS-RRT*) as an RRT* variant that incorporates nonlinear dynamics by solving a nonlinear program (NLP) and accounts for risk by approximating the state distribution and performing a distributionally robust (DR) collision check to promote safe planning. The generated plan is used as a reference for a low-level tracking controller. We demonstrate three controllers: finite horizon linear quadratic regulator (LQR) with linearized dynamics around the…
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）；the University of California San Diego（加州大学圣迭戈分校）
Path planning is a fundamental capability for autonomous navigation of robotic wheelchairs. With the impressive development of deep-learning technologies, imitation learning-based path planning approaches have achieved effective results in recent years. However, the disadvantages of these approaches are twofold: 1) they may need extensive time and labor to record expert demonstrations as training data; and 2) existing approaches could only receive high-level commands, such as turning left/right. These commands could be less sufficient for the navigation of mobile robots (e.g., robotic wheelcha…
Aerial drones have great potential to monitor large areas quickly and efficiently. Aquaculture is an industry that requires continuous water quality data to successfully grow and harvest fish. The Hybrid Aerial Underwater Robotic System (HAUCS) is designed to collect water quality data of aquaculture ponds to reduce labor costs for farmers. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem. A dataset is created to simulate the distribution of ponds on a farm and is used to assess the HAUCS Path Planning Algorithm (HPP). Its perfo…
Open-Source, Cost-Aware Kinematically Feasible Planning for Mobile and Surface Robotics
Open Navigation LLC；Carnegie Mellon University（卡内基梅隆大学）；Locus Robotics；QUT Centre for Robotics and School of Electrical Engineering and Robotics（QUT机器人中心和电气工程与机器人学院）
State Key Laboratory of High Performance Computing (HPCL)（高性能计算国家重点实验室）；School of Computer, National University of Defense Technology（国防科技大学计算机学院）
Recent years have witnessed the prosperity of robots and in order to support consensus and cooperation for multi-robot system, wireless communications and networking among robots and the infrastructure have become indispensable. In this technical note, we first provide an overview of the research contributions on communication-aware motion planning (CAMP) in designing wireless-connected robotic networks (WCRNs), where the degree-of-freedom (DoF) provided by motion and communication capabilities embraced by the robots have not been fully exploited. Therefore, we propose the framework of joint c…
Massachusetts Institute of Technology（麻省理工学院）；Institute for Systems and Robotics, Instituto Superior Técnico（高等技术学院系统与机器人研究所）
As access to space and robotic autonomy capabilities move forward, there is simultaneously a growing interest in deploying large, complex space structures to provide new on-orbit capabilities. New space-borne observatories, large orbital outposts, and even futuristic on-orbit manufacturing will be enabled by robotic assembly of space structures using techniques like on-orbit additive manufacturing which can provide flexibility in constructing and even repairing complex hardware. However, the dynamics underlying the robotic assembler during manipulation may operate under inertial uncertainties.…
Duke University（杜克大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Duke University Medical Center（杜克大学医学中心）
Robotic automation has the potential to assist human surgeons in performing suturing tasks in microsurgery, and in order to do so a robot must be able to guide a needle with sub-millimeter precision through soft tissue. This paper presents a robotic suturing system that uses 3D optical coherence tomography (OCT) system for imaging feedback. Calibration of the robot-OCT and robot-needle transforms, wound detection, keypoint identification, and path planning are all performed automatically. The calibration method handles pose uncertainty when the needle is grasped using a variant of iterative cl…
This report overviews a set of recent contributions in the field of path planning that were developed to enable the realization of the autonomous aerial robotic worker for inspection operations. The specific algorithmic contributions address several fundamental challenges of robotic inspection and exploration, and specifically those of optimal coverage planning given an a priori known model of the structure to be inspected, full coverage, optimized and fast inspection path planning, as well as efficient exploration of completely unknown environments and structures. All of the developed path pl…
In this paper, we consider the problem of generating inspection paths for robots. These paths should allow an attached measurement device to perform high-quality measurements. We formally show that generating robot paths, while maximizing the inspection quality, naturally corresponds to the submodular orienteering problem. Traditional methods that are able to generate solutions with mathematical guarantees do not scale to real-world problems. In this work, we propose a method that is able to generate near-optimal solutions for real-world complex problems. We experimentally test this method in …
University of Eastern Finland（东芬兰大学）；Shenzhen Technology University（深圳技术大学）
We investigate and analyze principles of typical motion planning algorithms. These include traditional planning algorithms, supervised learning, optimal value reinforcement learning, policy gradient reinforcement learning. Traditional planning algorithms we investigated include graph search algorithms, sampling-based algorithms, and interpolating curve algorithms. Supervised learning algorithms include MSVM, LSTM, MCTS and CNN. Optimal value reinforcement learning algorithms include Q learning, DQN, double DQN, dueling DQN. Policy gradient algorithms include policy gradient method, actor-criti…
Osaka University（大阪大学）；National Inst. of AIST（国立先进工业科学技术研究所）
This paper develops a flexible and robust robotic system for autonomous drawing on 3D surfaces. The system takes 2D drawing strokes and a 3D target surface (mesh or point clouds) as input. It maps the 2D strokes onto the 3D surface and generates a robot motion to draw the mapped strokes using visual recognition, grasp pose reasoning, and motion planning. The system is flexible compared to conventional robotic drawing systems as we do not fix drawing tools to the end of a robot arm. Instead, a robot selects drawing tools using a vision system and holds drawing tools for painting using its hand.…
This paper presents Latent Sampling-based Motion Planning (L-SBMP), a methodology towards computing motion plans for complex robotic systems by learning a plannable latent representation. Recent works in control of robotic systems have effectively leveraged local, low-dimensional embeddings of high-dimensional dynamics. In this paper we combine these recent advances with techniques from sampling-based motion planning (SBMP) in order to design a methodology capable of planning for high-dimensional robotic systems beyond the reach of traditional approaches (e.g., humanoids, or even systems where…
We present a motion planner for planning through space-time with dynamic obstacles, velocity constraints, and unknown arrival time. Our algorithm, Space-Time RRT* (ST-RRT*), is a probabilistically complete, bidirectional motion planning algorithm, which is asymptotically optimal with respect to the shortest arrival time. We experimentally evaluate ST-RRT* in both abstract (2D disk, 8D disk in cluttered spaces, and on a narrow passage problem), and simulated robotic path planning problems (sequential planning of 8DoF mobile robots, and 7DoF robotic arms). The proposed planner outperforms RRT-Co…
University of Paderborn（帕德博恩大学）；Leuphana University of Lüneburg（吕讷堡莱法纳大学）
This paper presents a collection of path planning algorithms for real-time movement of multiple robots across a Robotic Mobile Fulfillment System (RMFS). Robots are assigned to move storage units to pickers at working stations instead of requiring pickers to go to the storage area. Path planning algorithms aim to find paths for the robots to fulfill the requests without collisions or deadlocks. The state-of-the-art path planning algorithms, including WHCA*, FAR, BCP, OD&ID and CBS, were adapted to suit path planning in RMFS and integrated within a simulation tool to guide the robots from their…
Sampling-based motion planning techniques have emerged as an efficient algorithmic paradigm for solving complex motion planning problems. These approaches use a set of probing samples to construct an implicit graph representation of the robot's state space, allowing arbitrarily accurate representations as the number of samples increases to infinity. In practice, however, solution trajectories only rely on a few critical states, often defined by structure in the state space (e.g., doorways). In this work we propose a general method to identify these critical states via graph-theoretic technique…
MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments
Department of Electrical and Computer Engineering, University of Kaiserslautern Landau（电气与计算机工程系，凯撒斯劳滕大学）；Department of Mechanical Engineering, Dalhousie University（机械工程系，达尔豪斯大学）
Integrating path-planning and control for robotic unicycles
Department of Mechanical Engineering, University of Michigan（机械工程系，密歇根大学）；HUN-REN–BME Dynamics of Machines Research Group（HUN-REN–BME 机械动力学研究组）；Department of Mechanical Engineering and with the Department of Civil and Environmental Engineering, University of Michigan（机械工程系和土木与环境工程系，密歇根大学）；Department of Applied Mechanics, Budapest University of Technology and Economics（应用力学系，布达佩斯技术与经济大学）
Many real-world robotic operations that involve high-dimensional humanoid robots require fast-reaction to plan disturbances and probabilistic guarantees over collision risks, whereas most probabilistic motion planning approaches developed for car-like robots can not be directly applied to high-dimensional robots. In this paper, we present probabilistic Chekov (p-Chekov), a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises. Leveraging recent advances in machine learning as well as our previous…
