Graph search planning algorithms for navigation typically rely heavily on heuristics to efficiently plan paths. As a result, while such approaches require no training phase and can directly plan long horizon paths, they often require careful hand designing of informative heuristic functions. Recent works have started bypassing hand designed heuristics by using machine learning to learn heuristic functions that guide the search algorithm. While these methods can learn complex heuristic functions from raw input, they i) require a significant training phase and ii) do not generalize well to new maps and longer horizon paths. Our contribution is showing that instead of learning a global heuristic estimate, we can define and learn local heuristics which results in a significantly smaller learning problem and improves generalization. We show that using such local heuristics can reduce node expansions by 2-20x while maintaining bounded suboptimality, are easy to train, and generalize to new maps & long horizon plans.
Carnegie Mellon University（卡内基梅隆大学）；Honda Research Institute（本田研究所）
Real-time navigation in dense human environments is a challenging problem in robotics. Most existing path planners fail to account for the dynamics of pedestrians because introducing time as an additional dimension in search space is computationally prohibitive. Alternatively, most local motion planners only address imminent collision avoidance and fail to offer long-term optimality. In this work, we present an approach, called Dynamic Channels, to solve this global to local quandary. Our method combines the high-level topological path planning with low-level motion planning into a complete pi…
This paper presents an autonomous navigation system for ground robots traversing aggressive unstructured terrain through a cohesive arrangement of mapping, deliberative planning and reactive behaviour modules. All systems are aware of terrain slope, visibility and vehicle orientation, enabling robots to recognize, plan and react around unobserved areas and overcome negative obstacles, slopes, steps, overhangs and narrow passageways. This is one of pioneer works to explicitly and simultaneously couple mapping, planning and reactive components in dealing with negative obstacles. The system was d…
Army Research Laboratory（陆军研究实验室）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Sony AI（索尼人工智能研究院）
Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigation systems to move mobile robots from one point to another. Despite their overall success, a recently emerging research thrust is devoted to developing machine learning techniques to address the same problem, based in large part on the success of deep learning. However, to date, there has not been much direct comparison between the classical and emerging paradigms to this problem. In this article, we survey recent wo…
Recent applications of deep learning to navigation have generated end-to-end navigation solutions whereby visual sensor input is mapped to control signals or to motion primitives. The resulting visual navigation strategies work very well at collision avoidance and have performance that matches traditional reactive navigation algorithms while operating in real-time. It is accepted that these solutions cannot provide the same level of performance as a global planner. However, it is less clear how such end-to-end systems should be integrated into a full navigation pipeline. We evaluate the typica…
We study the problem of infrastructure inspection using an Unmanned Aerial Vehicle (UAV) in box girder bridge environments. We consider a scenario where the UAV needs to fully inspect box girder bridges and localize along the bridge surface when standard methods like GPS and optical flow are denied. Our method for overcoming the difficulties of box girder bridges consist of creating local navigation routines, a supervisor, and a planner. The local navigation routines use two 2D Lidars for girder and column flight. For switching between local navigation routines we implement a supervisor which …
How does one obtain an admissible heuristic for a kinodynamic motion planning problem? This paper develops the analytical tools and techniques to answer this question. A sufficient condition for the admissibility of a heuristic is presented which can be checked directly from the problem data. This condition is also used to formulate a concave program to optimize an admissible heuristic. This optimization is then approximated and solved in polynomial time using sum-of-squares programming techniques. A number of examples are provided to demonstrate these concepts.
Northeastern University（东北大学）；Institute for Semantic Computing Foundation（语义计算基金会研究所）
This paper describes and analyzes a reactive navigation framework for mobile robots in unknown environments. The approach does not rely on a global map and only considers the local occupancy in its robot-centered 3D grid structure. The proposed algorithm enables fast navigation by heuristic evaluations of pre-sampled trajectories on-the-fly. At each cycle, these paths are evaluated by a weighted cost function, based on heuristic features such as closeness to the goal, previously selected trajectories, and nearby obstacles. This paper introduces a systematic method to calculate a feasible pose …
Department of Computer Science, George Mason University（计算机科学系，乔治·马歇尔大学）；Department of Engineering Science, University of South Florida（工程科学系，佛罗里达州立大学）
Autonomous Exploration Development Environment is an open-source repository released to facilitate the development of high-level planning algorithms and integration of complete autonomous navigation systems. The repository contains representative simulation environment models, fundamental navigation modules, e.g., local planner, terrain traversability analysis, waypoint following, and visualization tools. Together with two of our high-level planner releases -- TARE planner for exploration and FAR planner for route planning, we detail usage of the three open-source repositories and share experi…
Massachusetts Institute of Technology（麻省理工学院）；DEVCOM Army Research Laboratory（美国陆军作战能力发展司令部陆军研究实验室）
A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D information) are ideal for real-time safe and fast planning. However, the prevalent approach of generating 2D occupancy grids through raytracing makes the generated map unsafe to plan in, due to inaccurate representation of unknown space. Additionally, existing planners such as MPPI do not consider speeds in known free and unknown space separately, leading to slower overall plans. The RAMP pipeline proposed here solves th…
In this paper, we present a framework for real-time autonomous robot navigation based on cloud and on-demand databases to address two major issues of human-like robot interaction and task planning in global dynamic environment, which is not known a priori. Our framework contributes to make human-like brain GPS mapping system for robot using spatial information and performs 3D visual semantic SLAM for independent robot navigation. We accomplish the feat by separating robot's memory system into Long-Term Memory (LTM) and Short-Term Memory (STM). We also form robot's behavior and knowledge system…
Edge Accelerated Robot Navigation With Collaborative Motion Planning
University of Macau（澳门大学）；The University of Hong Kong（香港大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Zhejiang University（浙江大学）；Weizmann Institute of Science（魏茨曼科学研究所）
Generating natural and physically feasible motions for legged robots has been a challenging problem due to its complex dynamics. In this work, we introduce a novel learning-based framework of autoregressive motion planner (ARMP) for quadruped locomotion and navigation. Our method can generate motion plans with an arbitrary length in an autoregressive fashion, unlike most offline trajectory optimization algorithms for a fixed trajectory length. To this end, we first construct the motion library by solving a dense set of trajectory optimization problems for diverse scenarios and parameter settin…
Enhancing Social Robot Navigation with Integrated Motion Prediction and Trajectory Planning in Dynamic Human Environments
School of Information Science, Japan Advanced Institute of Science and Technology（日本先进科学技术学院信息科学学院）；University of Engineering and Technology, Vietnam National University Hanoi（越南国家大学河内工程技术大学）
Fast and accurate path planning is important for ground robots to achieve safe and efficient autonomous navigation in unstructured outdoor environments. However, most existing methods exploiting either 2D or 2.5D maps struggle to balance the efficiency and safety for ground robots navigating in such challenging scenarios. In this paper, we propose a novel hybrid map representation by fusing a 2D grid and a 2.5D digital elevation map. Based on it, a novel path planning method is proposed, which considers the robot poses during traversability estimation. By doing so, our method explicitly takes …
Norwegian University of Life Sciences（挪威生命科学大学）；University of Lincoln（林肯大学）
This paper introduces a new method for robot motion planning and navigation in uneven environments through a surfel representation of underlying point clouds. The proposed method addresses the shortcomings of state-of-the-art navigation methods by incorporating both kinematic and physical constraints of a robot with standard motion planning algorithms (e.g., those from the Open Motion Planning Library), thus enabling efficient sampling-based planners for challenging uneven terrain navigation on raw point cloud maps. Unlike techniques based on Digital Elevation Maps (DEMs), our novel surfel-bas…
RAVEN: Reinforcement-Adaptive Visibility-Graph Planning for Robust Humanoid Navigation with Collision-Free MPC
Robotics and Mechanisms Laboratory (RoMeLa), Department of Mechanical and Aerospace Engineering, University of California, Los Angeles（机器人与机构实验室（RoMeLa），机械与航天工程系，加利福尼亚大学洛杉矶分校）
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；The Chinese University of Hong Kong（香港中文大学）；The University of British Columbia（不列颠哥伦比亚大学）
There are many challenges for robot navigation in densely populated dynamic environments. This paper presents a survey of the path planning methods for robot navigation in dense environments. Particularly, the path planning in the navigation framework of mobile robots is composed of global path planning and local path planning, with regard to the planning scope and the executability. Within this framework, the recent progress of the path planning methods is presented in the paper, while examining their strengths and weaknesses. Notably, the recently developed Velocity Obstacle method and its v…
Automated driving in urban scenarios requires efficient planning algorithms able to handle complex situations in real-time. A popular approach is to use graph-based planning methods in order to obtain a rough trajectory which is subsequently optimized. A key aspect is the generation of trajectories implementing comfortable and safe behavior already during graph-search while keeping computation times low. To capture this aspect, on the one hand, a branching strategy is presented in this work that leads to better performance in terms of quality of resulting trajectories and runtime. On the other…
SkipWorld: Latent Skip Planning with Distilled World Models for Efficient Visual Navigation
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）
City University of Hong Kong（香港城市大学）；Hong Kong Center for Logistics Robotics（香港物流机器人中心）；The Chinese University of Hong Kong（香港中文大学）
Recent advancements toward perception and decision-making of flexible endoscopes have shown great potential in computer-aided surgical interventions. However, owing to modeling uncertainty and inter-patient anatomical variation in flexible endoscopy, the challenge remains for efficient and safe navigation in patient-specific scenarios. This paper presents a novel data-driven framework with self-contained visual-shape fusion for autonomous intelligent navigation of flexible endoscopes requiring no priori knowledge of system models and global environments. A learning-based adaptive visual servoi…
Efficient Global Navigational Planning in 3D Structures based on Point Cloud Tomography
Hong Kong University of Science and Technology（香港科技大学）；University College London（伦敦大学学院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Autonomous Systems Lab, ETH Zurich（苏黎世联邦理工学院自主系统实验室）
Visual robot navigation within large-scale, semi-structured environments deals with various challenges such as computation intensive path planning algorithms or insufficient knowledge about traversable spaces. Moreover, many state-of-the-art navigation approaches only operate locally instead of gaining a more conceptual understanding of the planning objective. This limits the complexity of tasks a robot can accomplish and makes it harder to deal with uncertainties that are present in the context of real-time robotics applications. In this work, we present Topomap, a framework which simplifies …
CeRLP: A Cross-embodiment Robot Local Planning Framework for Visual Navigation
School of Computer Science, Shanghai Jiao Tong University, Shanghai, P. R. China（上海交通大学计算机科学学院，上海，中华人民共和国）；College of Information Science and Technology, Eastern Institute of Technology, Ningbo, P. R. China（信息科学与技术学院，东部技术研究所，宁波，中华人民共和国）
Prediction-Driven Motion Planning: Route Integration Strategies in Attention-Based Prediction Models
Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT)（测量与控制系统研究所，卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（信息技术研究所以）
NetApp India（NetApp印度公司）；Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）；SAP India（SAP印度公司）
Motion planning is the core problem to solve for developing any application involving an autonomous mobile robot. The fundamental motion planning problem involves generating a trajectory for a robot for point-to-point navigation while avoiding obstacles. Heuristic-based search algorithms like A* have been shown to be extremely efficient in solving such planning problems. Recently, there has been an increased interest in specifying complex motion plans using temporal logic. In the state-of-the-art algorithm, the temporal logic motion planning problem is reduced to a graph search problem and Dij…
Visual navigation has been widely used for state estimation of micro aerial vehicles (MAVs). For stable visual navigation, MAVs should generate perception-aware paths which guarantee enough visible landmarks. Many previous works on perception-aware path planning focused on sampling-based planners. However, they may suffer from sample inefficiency, which leads to computational burden for finding a global optimal path. To address this issue, we suggest a perception-aware path planner which utilizes topological information of environments. Since the topological class of a path and visible landmar…
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…
