Path planning and collision avoidance are challenging in complex and highly variable environments due to the limited horizon of events. In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality. We propose a planning algorithm based on a globally stable passive controller that can plan smooth trajectories using limited computational resources in challenging environmental conditions. The architecture combines the recently proposed fractal impedance controller with elastic bands and regions of finite time invariance. As the method is based on an impedance controller, it can also be used directly as a force/torque controller. We validated our method in simulation to analyse the ability of interactive navigation in challenging concave domains via the issuing of via-points, and its robustness to low bandwidth feedback. A swarm simulation using 11 agents validated the scalability of the proposed method. We have performed hardware experiments on a holonomic wheeled platform validating smoothness and robustness of interaction with dynamic agents (i.e., humans and robots). The computational complexity of the proposed local planner enables deployment with low-power micro-controllers lowering the energy consumption compared to other methods that rely upon numeric optimisation.
We demonstrated multi-mobile robot navigation based on Visible Light Positioning(VLP) localization. From our experiment, the VLP can accurately locate robots' positions in navigation.
Safe robot navigation is a fundamental research field for autonomous robots including ground mobile robots and flying robots. The primary objective of a safe robot navigation algorithm is to guide an autonomous robot from its initial position to a target or along a desired path with obstacle avoidance. With the development of information technology and sensor technology, the implementations combining robotics with sensor network are focused on in the recent researches. One of the relevant implementations is the sensor network based robot navigation. Moreover, another important navigation probl…
Zen Aerologiks；BMS College of Engineering（BMS工程学院）
The navigation system is at the heart of any mobile robot it comprises of SLAM and path planning units, which is utilized by the robot to generate a map of the environment, localize itself within it and determine an optimal a path to the destination. This paper describes the conceptualization, development, simulation and hardware implementation of GenNav a generic indoor navigation system for any mobile aerial or ground robot. The generalization is brought about by modularizing and creating independence between the software computation and hardware actuation units by providing an alternate sou…
Mobile robots have gained increased importance within industrial tasks such as commissioning, delivery or operation in hazardous environments. The ability to autonomously navigate safely especially within dynamic environments, is paramount in industrial mobile robotics. Current navigation methods depend on preexisting static maps and are error-prone in dynamic environments. Furthermore, for safety reasons, they often rely on hand-crafted safety guidelines, which makes the system less flexible and slow. Visual based navigation and high level semantics bear the potential to enhance the safety of…
Reasoning Systems for Semantic Navigation in Mobile Robots
University of Carlos III of Madrid（卡洛斯三世大学）；Technical University of Cartagena（卡塔赫纳理工大学）；Institute of Artificial Intelligent at the University of Bremen（不来梅大学人工智能研究所）
Anhui Polytechnic University（安徽工程大学）；Wuhu HIT Robot Technology Research Institute Co. Ltd（芜湖哈工大机器人技术研究院有限公司）；Cranfield University（克兰菲尔德大学）
In this paper, we developed a new navigation system, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Mophin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The experiments on simulation and a robot for eight scenarios demonstrate that the robot can effectively and efficiently avoid pote…
The basic idea behind evolutionary robotics is to evolve a set of neural controllers for a particular task at hand. It involves use of various input parameters such as infrared sensors, light sensors and vision based methods. This paper aims to explore the evolution of vision based navigation in a mobile robot. It discusses in detail the effect of different field of views for a mobile robot. The individuals have been evolved using different FOV values and the results have been recorded and analyzed.The optimum values for FOV have been proposed after evaluating more than 100 different values. I…
University of Bristol（布里斯托大学）；The Hong Kong University of Science and Technology（香港科技大学）；Shandong University（山东大学）；Jilin University（吉林大学）；Shenzhen Sanhang Industrial Technology Research Institute（深圳三航工业技术研究院）
Mobile robots are becoming increasingly important both for individuals and industries. Mobile robotic technology is not only utilised by experts in this field but is also very popular among amateurs. However, implementing a mobile robot to perform tasks autonomously can be expensive because of the need for various types of sensors and the high price of robot platforms. Hence, in this paper we present a mobile robot localisation and navigation system which uses a LEGO ultrasonic sensor in an indoor map based on the LEGO MINDSTORM NXT. This provides an affordable and ready-to-use option for most…
In the current era of the industrial revolution, mobile robots are playing a pivotal role in helping out mankind in many complex and hazardous environments for performing tasks like search and rescue, obstacle avoidance, mining and security surveillance, etc. A lot of navigation algorithms have been developed in recent years but novel challenges still exist in autonomous path planning of multiple robots to track and follow multiple intruders. This report demonstrates a decentralized strategy of arithmetic mean based navigation algorithm for a group of mobile robots to navigate through an unkno…
Northeastern University（东北大学）；Institute for Experiential Robotics（体验式机器人研究所）
This paper introduces a reactive navigation framework for mobile robots in 3-dimensional (3D) space. The proposed approach does not rely on the global map information and achieves fast navigation by employing a tentacle based sampling and their heuristic evaluations on-the-fly. This reactive nature of the approach comes from the prior arrangement of navigation points on tentacles (parametric contours) to sample the navigation space. These tentacles are evaluated at each time-step, based on heuristic features such as closeness to the goal, previous tentacle preferences and nearby obstacles in a…
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…
CausalNav: A Long-term Embodied Navigation System for Autonomous Mobile Robots in Dynamic Outdoor Scenarios
Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University（人工智能与机器人中心，深圳国际研究生院，清华大学）；Peng Cheng Laboratory（鹏城实验室）
Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of Terrestrial Mobile Robots
University of Tsukuba（筑波大学）；Federal University of Rio Grande（里奥格兰德联邦大学）；Federal University of Santa Maria（圣玛丽亚联邦大学）；Technological University of Uruguay（乌拉圭科技大学）
Bench-Push: Benchmarking Pushing-based Navigation and Manipulation Tasks for Mobile Robots
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（机器人与智能机器研究所，佐治亚理工学院）；Department of Electrical and Computer Engineering, University of Waterloo（电气与计算机工程系，滑铁卢大学）；Department of Mechanical Engineering, University of Alberta（机械工程系，阿尔伯塔大学）
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 …
MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigation
Autonomous Navigation and Sensor Fusion Lab (ANSFL)（自主导航与传感器融合实验室）；Hatter Department of Marine Technologies（海洋技术系）；Charney School of Marine Sciences（海洋科学学院）；University of Haifa（海法大学）
University Libre du Bruxelles（布鲁塞尔自由大学）；TU Delft（代尔夫特理工大学）
There is a growing demand for mobile robots to operate in more variable environments, where guaranteeing safe robot navigation is a priority, in addition to time performance. To achieve this, current solutions for local planning use a specific configuration tuned to the characteristics of the application environment. In this paper, we present an approach for developing quality models that can be used by a self-adaptation framework to adapt the local planner configuration at run-time based on the perceived environment. We contribute a definition of a safety model that predicts the safety of a n…
Risk-Aware Navigation for Mobile Robots in Unknown 3D Environments
Université Clermont Auvergne（克莱蒙奥弗涅大学）；Clermont Auvergne INP（克莱蒙奥弗涅国立综合理工学院）；CNRS（法国国家科学研究中心）；Institut Pascal（帕斯卡尔研究所）；University of Toronto Institute for Aerospace Studies (UTIAS)（多伦多大学航空航天研究所）
Bristol Robotics Laboratory（布里斯托尔机器人实验室）；University of the West of England（西英格兰大学）
In this paper, we investigate the utility of head-mounted display (HMD) interfaces for navigation of mobile robots. We focus on the selection of waypoint positions for the robot, whilst maintaining an egocentric view of the robot's environment. Inspired by virtual reality (VR) gaming, we propose a target selection method that uses the 6 degrees-of-freedom tracked controllers of a commercial VR headset. This allows an operator to point to the desired target position, in the vicinity of the robot, which the robot then autonomously navigates towards. A user study (37 participants) was conducted t…
In recent years, the use of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, a theoretical model of a fuzzy based controller for an autonomous mobile robot is developed. The paper begins with the mathematical model of the robot that involves the kinematic model. Then, the fuzzy logic controller is developed and discussed in detail. The proposed method is successfully tested in simulations, and it compares the effe…
In this work, we present two Deep Reinforcement Learning (Deep-RL) approaches to enhance the problem of mapless navigation for a terrestrial mobile robot. Our methodology focus on comparing a Deep-RL technique based on the Deep Q-Network (DQN) algorithm with a second one based on the Double Deep Q-Network (DDQN) algorithm. We use 24 laser measurement samples and the relative position and angle of the agent to the target as information for our agents, which provide the actions as velocities for our robot. By using a low-dimensional sensing structure of learning, we show that it is possible to t…
This paper presents a technique for navigation of mobile robot with Deep Q-Network (DQN) combined with Gated Recurrent Unit (GRU). The DQN integrated with the GRU allows action skipping for improved navigation performance. This technique aims at efficient navigation of mobile robot such as autonomous parking robot. Framework for reinforcement learning can be applied to the DQN combined with the GRU in a real environment, which can be modeled by the Partially Observable Markov Decision Process (POMDP). By allowing action skipping, the ability of the DQN combined with the GRU in learning key-act…
Technological University of Uruguay（乌拉圭技术大学）；Federal University of Rio Grande - FURG（里奥格兰德联邦大学（FURG））；Universidade Federal de Santa Maria - UFSM（圣玛丽亚联邦大学（UFSM））
Deterministic and Stochastic techniques in Deep Reinforcement Learning (Deep-RL) have become a promising solution to improve motion control and the decision-making tasks for a wide variety of robots. Previous works showed that these Deep-RL algorithms can be applied to perform mapless navigation of mobile robots in general. However, they tend to use simple sensing strategies since it has been shown that they perform poorly with a high dimensional state spaces, such as the ones yielded from image-based sensing. This paper presents a comparative analysis of two Deep-RL techniques - Deep Determin…
As the demands of autonomous mobile robots are increasing in recent years, the requirement of the path planning/navigation algorithm should not be content with the ability to reach the target without any collisions, but also should try to achieve possible optimal or suboptimal path from the initial position to the target according to the robot's constrains in practice. This report investigates path planning and control strategies for mobile robots with machine learning techniques, including ground mobile robots and flying UAVs. In this report, the hybrid reactive collision-free navigation prob…
Saint Petersburg State University（圣彼得堡国立大学）；University of New South Wales（新南威尔士大学）
A single nonholonomic under-actuated mobile robot with a bounded control range travels in a 3D workspace. This workspace also hosts a compact unknown domain with a smooth boundary. The robot has access to the horizontal distance to this domain and to a certain direction (typically, vertical) in the space, along with its coordinate (typically, altitude) in this direction. We present a new navigation law that drives the robot to the desired distance to the domain and ensures subsequent full scan of its surface within a desired range of "altitudes". The proposed strategy is computationally inexpe…
Ground Technology Research Institute, Agency for Defense Development（国防发展局地面技术研究所）；DGIST（大邱庆北科学技术院）
Currently, mobile robots are developing rapidly and are finding numerous applications in the industry. However, several problems remain related to their practical use, such as the need for expensive hardware and high power consumption levels. In this study, we build a low-cost indoor mobile robot platform that does not include a LiDAR or a GPU. Then, we design an autonomous navigation architecture that guarantees real-time performance on our platform with an RGB-D camera and a low-end off-the-shelf single board computer. The overall system includes SLAM, global path planning, ground segmentati…
The demand for mobile robots has rapidly increased in recent years due to the flexibility and high variety of application fields comparing to static robots. To deal with complex tasks such as navigation, they work with high amounts of different sensor data making it difficult to operate with for non-experts. To enhance user understanding and human robot interaction, we propose an approach to visualize the navigation stack within a cutting edge 3D Augmented Reality device -- the Microsoft Hololens. Therefore, relevant navigation stack data including laser scan, environment map and path planing …
City University of Hong Kong（香港城市大学）；HKUST（香港科技大学）；ETH Zurich（苏黎世联邦理工学院）
We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be tr…
Navigation and guidance of autonomous vehicles is a fundamental problem in robotics, which has attracted intensive research in recent decades. This report is mainly concerned with provable collision avoidance of multiple autonomous vehicles operating in unknown cluttered environments, using reactive decentralized navigation laws, where obstacle information is supplied by some sensor system. Recently, robust and decentralized variants of model predictive control based navigation systems have been applied to vehicle navigation problems. Properties such as provable collision avoidance under distu…
Visually impaired people usually find it hard to travel independently in many public places such as airports and shopping malls due to the problems of obstacle avoidance and guidance to the desired location. Therefore, in the highly dynamic indoor environment, how to improve indoor navigation robot localization and navigation accuracy so that they guide the visually impaired well becomes a problem. One way is to use visual SLAM. However, typical visual SLAM either assumes a static environment, which may lead to less accurate results in dynamic environments or assumes that the targets are all d…
This paper extends the gap-based navigation technique in Potential Gap by guaranteeing safety for nonholonomic robots for all tiers of the local planner hierarchy, so called Safer Gap. The first tier generates a Bezier-based collision-free path through gaps. A subset of navigable free-space from the robot through a gap, called the keyhole, is defined to be the union of the largest collision-free disc centered on the robot and a trapezoidal region directed through the gap. It is encoded by a shallow neural network zeroing barrier function (ZBF). Nonlinear model predictive control (NMPC), with K…
Real-Time Spatial Reasoning by Mobile Robots for Reconstruction and Navigation in Dynamic LiDAR Scenes
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computer Science, University of Guelph（圭尔夫大学计算机科学学院）；School of Computing Science, Simon Fraser University（西蒙弗雷泽大学计算机科学学院）
People are proficient at communicating their intentions in order to avoid conflicts when navigating in narrow, crowded environments. In many situations mobile robots lack both the ability to interpret human intentions and the ability to clearly communicate their own intentions to people sharing their space. This work addresses the second of these points, leveraging insights about how people implicitly communicate with each other through observations of behaviors such as gaze to provide mobile robots with better social navigation skills. In a preliminary human study, the importance of gaze as a…
From Data to Safe Mobile Robot Navigation: An Efficient and Modular Robust MPC Design Pipeline
department of Cognitive Robotics, Delft University of Technology（认知机器人学系，代尔夫特理工大学）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（信息学、机器人学与自动化研究所，布拉格技术大学）；Institute for Dynamic Systems and Control, ETH Zürich（动态系统与控制研究所，苏黎世联邦理工学院）
