In this paper, we address the problem of synthesizing optimal path plans in a 2D subject to spatio-temporal and thermal constraints. Our solution consists of reducing the path planning problem to a Mixed Integer Linear Programming (MILP) problem. The challenge is in encoding the implication constraints in the path planning problem using only conjunctions that are permitted by the MILP formulation. Our experimental analysis using an implementation of the encoding in a Python toolbox demonstrates the feasibility of our approach in generating the optimal plans.
Singapore Centre for 3D Printing（新加坡3D打印中心）；Nanyang Technological University（南洋理工大学）
Building and Construction have recently become an exciting application ground for robotics. In particular, rapid progress in materials formulation and in robotics technology has made robotic 3D Printing of concrete a promising technique for in-situ construction. Yet, scalability remains an important hurdle to widespread adoption: the printing systems (gantry- based or arm-based) are often much larger than the structure to be printed, hence cumbersome. Recently, a mobile printing system - a manipulator mounted on a mobile base - was proposed to alleviate this issue: such a system, by moving its…
Rutgers University（罗格斯大学）；Massachusetts Institute of Technology（麻省理工学院）
microMVP is an affordable, portable, and open source micro-scale mobile robot platform designed for robotics research and education. As a complete and unique multi-vehicle platform enabled by 3D printing and the maker culture, microMVP can be easily reproduced and requires little maintenance: a set of six micro vehicles, each measuring $8\times 5\times 6$ cubic centimeters and weighing under $100$ grams, and the accompanying tracking platform can be fully assembled in under two hours, all from readily available components. In this paper, we describe microMVP's hardware and software architectur…
INF-3DP: Implicit Neural Fields for Collision-Free Multi-Axis 3D Printing
The Chinese University of Hong Kong（香港中文大学）；The Chinese University of Hong Kong, China / The University of Manchester（香港中文大学/曼彻斯特大学）；The University of Manchester（曼彻斯特大学）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）
Terrain-Adaptive Mobile 3D Printing with Hierarchical Control
Department of Electrical and Computer Engineering, University of Washington, USA（电气与计算机工程系，华盛顿大学）；Department of Applied Mathematics, University of Washington, USA（应用数学系，华盛顿大学）
University of Nevada, Reno（内华达大学雷诺分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Path planning over spatiotemporal models can be applied to a variety of applications such as UAVs searching for spreading wildfire in mountains or network of balloons in time-varying atmosphere deployed for inexpensive internet service. A notable aspect in such applications is the dynamically changing environment. However, path planning algorithms often assume static environments and only consider the vehicle's dynamics exploring the environment. We present a spatiotemporal model that uses a cross-correlation operator to consider spatiotemporal dependence. Also, we present an adaptive state es…
fortiss GmbH（福蒂斯有限公司）；Technische Universität München（慕尼黑工业大学）
Motion planning for urban environments with numerous moving agents can be viewed as a combinatorial problem. With passing an obstacle before, after, right or left, there are multiple options an autonomous vehicle could choose to execute. These combinatorial aspects need to be taken into account in the planning framework. We address this problem by proposing a novel planning approach that combines trajectory planning and maneuver reasoning. We define a classification for dynamic obstacles along a reference curve that allows us to extract tactical decision sequences. We separate longitudinal and…
Multi-layer Motion Planning with Kinodynamic and Spatio-Temporal Constraints
The Thørväld Group（索尔瓦尔德集团）；Inria Paris-Rocquencourt（巴黎-罗quent court 国家科学研究中心）；Rajiv Gandhi University Doimukh（拉贾·甘地大学多伊穆克）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒姆研究实验室）；The Kumquat Consortium（昆克特联盟）
Birla Institute of Technology and Science, Pilani（皮拉尼比尔拉理工学院）；Cleveland State University（克利夫兰州立大学）；Dajiang Innovation Technology Co., Ltd（大疆创新科技有限公司）；Carnegie Mellon University（卡内基梅隆大学）
Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free trajectories. A layered motion planning approach with decoupled path and speed planning is widely used for this purpose. This approach is prone to be suboptimal in the presence of dynamic obstacles. Spatial-temporal approaches deal with path planning and speed planning simultaneously; however, the existing methods only support simple-shaped corridors like cuboids, which restrict the search space for optimization in complex scenarios. We propose to use trapezoidal prism-shaped corridors for optimizati…
An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China（工业控制技术国家重点实验室，浙江大学，杭州，310027，中国）；Shanghai STEP Electric Corporation, Shanghai, 201802, China（上海STEP电力有限公司，上海，201802，中国）
Robust Spatiotemporal Motion Planning for Multi-Agent Autonomous Racing via Topological Gap Identification and Accelerated MPC
Department of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Center for Project-Based Learning, D-ITET, ETH Zurich（苏黎世联邦理工学院项目导向学习中心）
Coverage Path Planning for Autonomous Sailboats in Inhomogeneous and Time-Varying Oceans: A Spatiotemporal Optimization Approach
Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences（中国科学院深海科学与工程研究所）；Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院海洋工程研究所）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学 Hangzhou 全球科技创新中心）；Department of Engineering, Faculty of Computing, Engineering and the Built Environment, Birmingham City University（伯明翰城市大学工程系）；School of Ocean and Civil Engineering, Shanghai Jiao Tong University（上海交通大学海洋与Civil Engineering学院）
MIT-WHOI Joint Program（麻省理工学院-伍兹霍尔海洋研究所联合项目）；Massachusetts Institute of Technology（麻省理工学院）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）
In the expeditionary sciences, spatiotemporally varying environments -- hydrothermal plumes, algal blooms, lava flows, or animal migrations -- are ubiquitous. Mobile robots are uniquely well-suited to study these dynamic, mesoscale natural environments. We formalize expeditionary science as a sequential decision-making problem, modeled using the language of partially-observable Markov decision processes (POMDPs). Solving the expeditionary science POMDP under real-world constraints requires efficient probabilistic modeling and decision-making in problems with complex dynamics and observational …
The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mi…
Neural network (NN)-based methods have emerged as an attractive approach for robot motion planning due to strong learning capabilities of NN models and their inherently high parallelism. Despite the current development in this direction, the efficient capture and processing of important sequential and spatial information, in a direct and simultaneous way, is still relatively under-explored. To overcome the challenge and unlock the potentials of neural networks for motion planning tasks, in this paper, we propose STP-Net, an end-to-end learning framework that can fully extract and leverage impo…
HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, Singapore（HP-NTU数字制造企业实验室，南洋理工大学，新加坡）；School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore（机械与航空航天工程学院，南洋理工大学，新加坡）；HP Labs, HP Inc.（惠普实验室，惠普公司）
With the rapid rise of 3D-printing as a competitive mass manufacturing method, manual "decaking" - i.e. removing the residual powder that sticks to a 3D-printed part - has become a significant bottleneck. Here, we introduce, for the first time to our knowledge, a robotic system for automated decaking of 3D-printed parts. Combining Deep Learning for 3D perception, smart mechanical design, motion planning, and force control for industrial robots, we developed a system that can automatically decake parts in a fast and efficient way. Through a series of decaking experiments performed on parts prin…
Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability
College of Intelligent Robotics and Advanced Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）；School of Information Science and Technology, Fudan University（信息科学与技术学院，复旦大学）；Department of Information Technology, Old Dominion University（信息技术系，老 Dominion 大学）
WildFab: Multi-Axis 3D Printing from Models in the Wild
The Chinese University of Hong Kong（香港中文大学）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）；Carnegie Mellon University（卡内基梅隆大学）；Technical University of Denmark（丹麦技术大学）
Zhejiang University（浙江大学）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
This paper proposes Elastic Tracker, a flexible trajectory planning framework that can deal with challenging tracking tasks with guaranteed safety and visibility. Firstly, an object detection and intension-free motion prediction method is designed. Then an occlusion-aware path finding method is proposed to provide a proper topology. A smart safe flight corridor generation strategy is designed with the guiding path. An analytical occlusion cost is evaluated. Finally, an effective trajectory optimization approach enables to generate a spatio-temporal optimal trajectory within the resultant fligh…
Rutgers, the State University of New Jersey（罗格斯大学（新泽西州立大学））
In this work, we systematically examine the application of spatio-temporal splitting heuristics to the Multi-Robot Motion Planning (MRMP) problem in a graph-theoretic setting: a problem known to be NP-hard to optimally solve. Following the divide-and-conquer principle, we design multiple spatial and temporal splitting schemes that can be applied to any existing MRMP algorithm, including integer programming solvers and Enhanced Conflict Based Search, in an orthogonal manner. The combination of a good baseline MRMP algorithm with a proper splitting heuristic proves highly effective, allowing the…
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Georgia Institute of Technology（佐治亚理工学院）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
This paper focuses on automatic guided vehicle (AGV) trajectory planning in the presence of moving obstacles with known but complicated trajectories. In order to achieve good solution precision, optimality and unification, the concerned task should be formulated as an optimal control problem, and then discretized into a nonlinear programming (NLP) problem, which is numerically optimized thereafter. Without a near-feasible or near-optimal initial guess, the NLP-solving process is usually slow. With the purpose of accelerating the NLP solution, a search-based rough planning stage is added to gen…
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）
In dense and dynamic scenarios, planning a safe and comfortable trajectory is full of challenges when traffic participants are driving at high speed. The classic graph search and sampling methods first perform path planning and then configure the corresponding speed, which lacks a strategy to deal with the high-speed obstacles. Decoupling optimization methods perform motion planning in the S-L and S-T domains respectively. These methods require a large free configuration space to plan the lane change trajectory. In dense dynamic scenes, it is easy to cause the failure of trajectory planning an…
V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；State Key Laboratory of CNS/ATM, Beijing Institute of Technology（北京理工大学国家空管委/民航委重点实验室）；Beijing Key Laboratory of Lightweight Intelligent System, Beijing Institute of Technology（北京理工大学轻量化智能系统重点实验室）；School of Automation, Southeast University（东南大学自动化学院）；Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology（深圳先进技术大学计算机科学与控制工程学院）
Zhejiang University（浙江大学）；University of Leeds（利兹大学）
We present a novel traffic trajectory editing method which uses spatio-temporal keyframes to control vehicles during the simulation to generate desired traffic trajectories. By taking self-motivation, path following and collision avoidance into account, the proposed force-based traffic simulation framework updates vehicle's motions in both the Frenet coordinates and the Cartesian coordinates. With the way-points from users, lane-level navigation can be generated by reference path planning. With a given keyframe, the coarse-to-fine optimization is proposed to efficiently generate the plausible …
There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility to print large, directionally-dependent structures. However, existing extrusion planning algorithms require a substantial amount of human input, do not scale to large instances, and lack theoretical guarantees. In this work, we present a rigorous formalization of robotic spatial extrusion planning and provide several efficient and probabilistically complete planning algorithms.…
The Chinese University of Hong Kong（香港中文大学）；University of Cambridge（剑桥大学）
Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects. In the presence of dynamic obstacles, traditional solutions usually employ re-planning strategies, which re-call a planning algorithm to search for an alternative path whenever the robot encounters a conflict. However, such re-planning strategies often cause unnecessary detours. To address this issue, we propose a learning-based technique that exploits env…
Zhejiang University（浙江大学）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）
In constrained solution spaces with a huge number of homotopy classes, stand-alone sampling-based kinodynamic planners suffer low efficiency in convergence. Local optimization is integrated to alleviate this problem. In this paper, we propose to thrive the trajectory tree growing by optimizing the tree in the forms of deformation units, and each unit contains one tree node and all the edges connecting it. The deformation proceeds both spatially and temporally by optimizing the node state and edge time durations efficiently. The unit only changes the tree locally yet improves the overall qualit…
University of Technology Sydney（悉尼科技大学）；Dartmouth College（达特茅斯学院）；Tulane University（杜兰大学）
The most widely used methods for toolpath planning in fused deposition 3D printing slice the input model into successive 2D layers in order to construct the toolpath. Unfortunately slicing-based methods can incur a substantial amount of wasted motion (i.e., the extruder is moving while not printing), particularly when features of the model are spatially separated. In recent years we have introduced a new paradigm that characterizes the space of feasible toolpaths using a dependency graph on the input model, along with several algorithms to search this space for toolpaths that optimize objectiv…
IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning
The Hong Kong University of Science and Technology（香港科技大学）；HKUST (GZ)（香港科技大学（广州））；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）；Unity Drive Innovation Technology Co. Ltd.（元戎启行创新科技有限公司）
Beckman Institute for Advanced Science and Technology（贝克曼先进科学技术研究所）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Wolfram Research（沃尔夫勒姆研究公司）
3D printing enables the fabrication of complex architectures by automating long sequences of additive steps. The increasing sophistication of printers, materials, and generative design promises to make geometric complexity a non-issue in manufacturing; however, this complexity can only be realized if a design can be translated into a physically executable sequence of printing operations. We investigate this planning problem for freeform direct-write assembly, in which filaments of material are deposited through a nozzle translating along a 3D path to create sparse, frame-like structures. We en…
Multi-robot Learning-based Informative Path Planning Using Spatio-Temporal Gaussian Process Kalman Filter
Carnegie Mellon University（卡内基梅隆大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Daegu Gyeongbuk Institute of Science and Technology（大邱庆北科学技术院）
