When does a robot planner need a map? Reactive methods that use only the robot's current sensor data and local information are fast and flexible, but prone to getting stuck in local minima. Is there a middle-ground between fully reactive methods and map-based path planners? In this paper, we investigate feed forward and recurrent networks to augment a purely reactive sensor-based planner, which should give the robot geometric intuition about how to escape local minima. We train on a large number of extremely cluttered worlds auto-generated from primitive shapes, and show that our system zero-shot transfers to real 3D man-made environments, and can handle up to 30% sensor noise without degeneration of performance. We also offer a discussion of what role network memory plays in our final system, and what insights can be drawn about the nature of reactive vs. map-based navigation.
Graduate School of Information Science and Engineering, Ritsumeikan University, Japan（立命馆大学信息科学与工程研究生院）；Japan Advanced Institute of Science and Technology (JAIST)（日本先进科学研究院）；College of Information Science and Engineering, Ritsumeikan University, Japan（立命馆大学信息科学与工程学院）
Technische Universität Darmstadt（达姆施塔特工业大学）；Huawei R&D London（华为伦敦研发中心）；University College London（伦敦大学学院）
Highly dynamic robotic tasks require high-speed and reactive robots. These tasks are particularly challenging due to the physical constraints, hardware limitations, and the high uncertainty of dynamics and sensor measures. To face these issues, it's crucial to design robotics agents that generate precise and fast trajectories and react immediately to environmental changes. Air hockey is an example of this kind of task. Due to the environment's characteristics, it is possible to formalize the problem and derive clean mathematical solutions. For these reasons, this environment is perfect for pus…
Munich Institute of Robotics & Machine Intelligence（慕尼黑机器人与机器智能研究所）；Technische Universität München（慕尼黑工业大学）
As collaborative robots move closer to human environments, motion generation and reactive planning strategies that allow for elaborate task execution with minimal easy-to-implement guidance whilst coping with changes in the environment is of paramount importance. In this paper, we present a novel approach for generating real-time motion plans for point-to-point tasks using a single successful human demonstration. Our approach is based on screw linear interpolation,which allows us to respect the underlying geometric constraints that characterize the task and are implicitly present in the demons…
Georgia Institute of Technology（佐治亚理工学院）；Monash University（莫纳什大学）
This paper explores the Navigation Among Movable Obstacles (NAMO) problem and proposes joint path and push planning: which path to take and in what direction the obstacles should be pushed at, given a start and goal position. We present a planning algorithm for selecting a path and the obstacles to be pushed, where a Rapidly-exploring Random Tree (RRT)-based heuristic is employed to calculate a minimal collision path. When it is necessary to apply a pushing force to slide an obstacle out of the way, the planners leverage means-end analysis through a dynamic physics simulation to determine the …
On the Synthesis of Reactive Collision-Free Whole-Body Robot Motions: A Complementarity-based Approach
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM)（慕尼黑机器人与机器智能研究所、慕尼黑工业大学）；GE global research（通用电气全球研究中心）；Boston University（波士顿大学）；University of Nottingham（诺丁汉大学）；Stony Brook University（石溪大学）
We present a decentralized algorithm to achieve segregation into an arbitrary number of groups with swarms of autonomous robots. The distinguishing feature of our approach is in the minimalistic assumptions on which it is based. Specifically, we assume that (i) Each robot is equipped with a ternary sensor capable of detecting the presence of a single nearby robot, and, if that robot is present, whether or not it belongs to the same group as the sensing robot; (ii) The robots move according to a differential drive model; and (iii) The structure of the control system is purely reactive, and it m…
Avoiding collisions is one of the vital tasks for systems of autonomous mobile agents. We focus on the problem of finding continuous coordinated paths for multiple mobile disc agents in a 2-d environment with polygonal obstacles. The problem is PSPACE-hard, with the state space growing exponentially in the number of agents. Therefore, the state of the art methods include mainly reactive techniques and sampling-based iterative algorithms. We compare the performance of a widely-used reactive method ORCA with three variants of a popular planning algorithm RRT* applied to multi-agent path planning…
Proactive Local-Minima-Free Robot Navigation: Blending Motion Prediction with Safe Control
Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania（机械工程与应用力学系，宾夕法尼亚大学）；Department of Electrical Engineering, Chalmers University of Technology（电气工程系，查尔姆斯理工大学）
Real-Time Model Checking for Closed-Loop Robot Reactive Planning
School of Computing Science University of Glasgow Glasgow Scotland UK（计算科学学院格拉斯哥大学格拉斯哥苏格兰英国）；School of Biomedical Engineering University of Glasgow Glasgow Scotland UK（生物医学工程学院格拉斯哥大学格拉斯哥苏格兰英国）；University of Glasgow（格拉斯哥大学）
Bristol Robotics Laboratory, The University of Bristol（布里斯托尔机器人实验室，布里斯托尔大学）；Visual Information Laboratory, The University of Bristol（视觉信息实验室，布里斯托尔大学）；Department of Electrical and Electronic Engineering, The University of Manchester（曼彻斯特大学电气与电子工程系）；The Department of Computer Science and Engineering & The Department of Ophthalmology, The University of California, San Diego（加利福尼亚大学圣地亚哥分校计算机科学与工程系及眼科系）；Hangzhou ATG Intelligent Technology Co. Ltd.（杭州ATG智能科技有限公司）；Amazon（亚马逊公司）；The University of California, San Diego（加利福尼亚大学圣地亚哥分校）；The University of Manchester（曼彻斯特大学）；The University of Bristol（布里斯托尔大学）；Amazon, Seattle, USA（亚马逊公司（西雅图））；The Department of Ophthalmology, The University of California, San Diego（加利福尼亚大学圣地亚哥分校眼科系）
This paper presents an agile reactive navigation strategy for driving a non-holonomic ground vehicle around a preset course of gates in a cluttered environment using a low-cost processor array sensor. This enables machine vision tasks to be performed directly upon the sensor's image plane, rather than using a separate general-purpose computer. We demonstrate a small ground vehicle running through or avoiding multiple gates at high speed using minimal computational resources. To achieve this, target tracking algorithms are developed for the Pixel Processing Array and captured images are then pr…
