A major goal in robotics is to enable intelligent mobile robots to operate smoothly in shared human-robot environments. One of the most fundamental capabilities in service of this goal is competent navigation in this ``social" context. As a result, there has been a recent surge of research on social navigation; and especially as it relates to the handling of conflicts between agents during social navigation. These developments introduce a variety of models and algorithms, however as this research area is inherently interdisciplinary, many of the relevant papers are not comparable and there is no shared standard vocabulary.
This survey aims to bridge this gap by introducing such a common language, using it to survey existing work, and highlighting open problems. It starts by defining the boundaries of this survey to a limited, yet highly common type of social navigation - conflict avoidance. Within this proposed scope, this survey introduces a detailed taxonomy of the conflict avoidance components. This survey then maps existing work into this taxonomy, while discussing papers using its framing. Finally, this paper proposes some future research directions and open problems that are currently on the frontier of social navigation to aid ongoing and future research.
In this paper, we investigate the obstacle avoidance and navigation problem in the robotic control area. For solving such a problem, we propose revised Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization algorithms with an improved reward shaping technique. We compare the performances between the original DDPG and PPO with the revised version of both on simulations with a real mobile robot and demonstrate that the proposed algorithms achieve better results.
Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a technique to distinguish dynamic obstacles from static ones with only point cloud input. Then, a computationally efficient obstacle avoidance motion planning approach is proposed and it is in line with an improved relative velocity method. The approach is able to avoid both static obstacles and dynamic ones in the same framework. For static and dynamic obstacles, the collision check and motion constraints are different, and …
Sapienza University of Rome（罗马大学）；IT+Robotics S.r.l.（IT+Robotics有限公司）
This work addresses the problem of coupling vision-based navigation systems for Unmanned Aerial Vehicles (UAVs) with robust obstacle avoidance capabilities. The former problem is solved by maximizing the visibility of the points of interest, while the latter is modeled by means of ellipsoidal repulsive areas. The whole problem is transcribed into an Optimal Control Problem (OCP), and solved in a few milliseconds by leveraging state-of-the-art numerical optimization. The resulting trajectories are well suited for reaching the specified goal location while avoiding obstacles with a safety margin…
DPNet: Efficient Dead-End Prediction and Avoidance for Vision-Based UAV Navigation
Huzhou Institute of Zhejiang University（浙江大学湖州研究院）；University of Electronic Science and Technology of China（电子科技大学）；Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院 cyber系统与控制研究所）；Differential Robotics Technology Company（差分机器人技术公司）
Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a more robust technique to distinguish and track dynamic obstacles from static ones with only point cloud input. Then, to achieve dynamic avoidance, we propose the forbidden pyramids method to solve the desired vehicle velocity with an efficient sampling-based method in iteration. The motion primitives are generated by solving a nonlinear optimization problem with the constraint of desired velocity and the waypoint. Furthermor…
This paper presents our method for enabling a UAV quadrotor, equipped with a monocular camera, to autonomously avoid collisions with obstacles in unstructured and unknown indoor environments. When compared to obstacle avoidance in ground vehicular robots, UAV navigation brings in additional challenges because the UAV motion is no more constrained to a well-defined indoor ground or street environment. Horizontal structures in indoor and outdoor environments like decorative items, furnishings, ceiling fans, sign-boards, tree branches etc., also become relevant obstacles unlike those for ground v…
With the rapidly growing interest in autonomous navigation, the body of research on motion planning and collision avoidance techniques has enjoyed an accelerating rate of novel proposals and developments. However, the complexity of new techniques and their safety requirements render the bulk of current benchmarking frameworks inadequate, thus leaving the need for efficient comparison techniques unanswered. This work proposes a novel framework based on deep reinforcement learning for benchmarking the behavior of collision avoidance mechanisms under the worst-case scenario of dealing with an opt…
Prior research has extensively explored Autonomous Vehicle (AV) navigation in the presence of other vehicles, however, navigation among pedestrians, who are the most vulnerable element in urban environments, has been less examined. This paper explores AV navigation in crowded, unsignalized intersections. We compare the performance of different deep reinforcement learning methods trained on our reward function and state representation. The performance of these methods and a standard rule-based approach were evaluated in two ways, first at the unsignalized intersection on which the methods were …
COMSATS Institute of Information Technology（COMSATS信息技术学院）
Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this survey paper, we mainly discussed different algorithms for robot navigation with obstacle avoidance. We also compared all provided algorithms and mentioned their characteristics; advantages and disadvantages, so that we can select final efficient algorithm by fusing discussed algorithms. Comparison table is provided for justifying the area of intere…
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…
Northwestern Polytechnical University（西北工业大学）；Cranfield University（克兰菲尔德大学）；City, University of London（伦敦大学城市学院）
In this paper, a deep reinforcement learning (DRL) method is proposed to address the problem of UAV navigation in an unknown environment. However, DRL algorithms are limited by the data efficiency problem as they typically require a huge amount of data before they reach a reasonable performance. To speed up the DRL training process, we developed a novel learning framework which combines imitation learning and reinforcement learning and building upon Twin Delayed DDPG (TD3) algorithm. We newly introduced both policy and Q-value network are learned using the expert demonstration during the imita…
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…
Mobile robots in unstructured, mapless environments must rely on an obstacle avoidance module to navigate safely. The standard avoidance techniques estimate the locations of obstacles with respect to the robot but are unaware of the obstacles' identities. Consequently, the robot cannot take advantage of semantic information about obstacles when making decisions about how to navigate. We propose an obstacle avoidance module that combines visual instance segmentation with a depth map to classify and localize objects in the scene. The system avoids obstacles differentially, based on the identity …
Hanoi University of Industry（河内工业大学）；University of Economics - Technology for Industries（经济与技术工业大学）
In recent years, the mobile robot has been considerable attention to researchers for its application in various environments. For a mobile robot navigating its way from starting point to a goal point while traversing through deterrents, needs to recognize the obstacles and generate new trajectories to reach the destination. This paper presents an obstacle avoidance method for mobile robots using an open-source in robot operation system (ROS) combining with the dynamic window approach (DWA) algorithm. The experiment is carried out using a mobile robot in which the navigation data is based on da…
Division of Science and Engineering, Penn State Abington（宾州州立大学阿宾顿分校科学与工程学院）
This paper investigates different methods to detect obstacles ahead of a robot using a camera in the robot, an aerial camera, and an ultrasound sensor. We also explored various efficient path finding methods for the robot to navigate to the target source. Single and multi-iteration angle-based navigation algorithms were developed. The theta-based path finding algorithms were compared with the Dijkstra Algorithm and their performance were analyzed.
Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or inefficient navigation approaches. However, the integration of Deep Reinforcement Learning into existing navigation systems is still an open frontier due to the myopic nature of Deep-Reinforcement-Learning-based navigation, which hinders its widespread integration into current navigation systems. In this paper, we propose the concept of an intermediate planner to interconnect novel Deep-Reinforcement-Learning-based obst…
Dorabot Inc.（Dorabot公司）；City University of Hong Kong（香港城市大学）；Fuzhou University（福州大学）
High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary. We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates an agent's navigation strategy as a deep neural network …
In this article we propose a reactive constrained navigation scheme, with embedded obstacles avoidance for an Unmanned Aerial Vehicle (UAV), for enabling navigation in obstacle-dense environments. The proposed navigation architecture is based on Nonlinear Model Predictive Control (NMPC), and utilizes an on-board 2D LiDAR to detect obstacles and translate online the key geometric information of the environment into parametric constraints for the NMPC that constrain the available position-space for the UAV. This article focuses also on the real-world implementation and experimental validation of…
Berlin Institute of Technology（柏林工业大学）；Chair Industry Grade Networks and Clouds（工业级网络与云研究室）；Faculty of Electrical Engineering, and Computer Science（电气工程与计算机科学学院）；Berlin Institute of Technology, Berlin, Germany（柏林工业大学（德国柏林））；Chair Industry Grade Networks and Clouds, Faculty of Electrical Engineering, and Computer Science, Berlin Institute of Technology, Berlin, Germany（柏林工业大学电气工程与计算机科学学院工业级网络与云研究室（德国柏林））
Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace overly conservative approaches and promises more efficient and flexible navigation. However, deep reinforcement learning approaches are not suitable for long-range navigation due to their proneness to local minima and lack of long term memory, which hinders its widespread integration into industrial applications of mobile robotics. In this paper, we propose a navigation system incorporating deep-reinforcement-learni…
University of Science and Technology of China (USTC)（中国科学技术大学）
It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like social force model (SFM) or optimal reciprocal collision avoidance (ORCA). However, their performances commonly need to be further improved for practical applications, where pedestrians follow multiple different collision avoidance strategies. In this paper, we propose a map-based deep reinforcement learning approach for crowd-aware robot navigation with various pedestrians. We use the sensor map to represent the envir…
University of Maryland at College Park（马里兰大学帕克分校）；University of Hong Kong（香港大学）
We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL). Our approach uses local and global information for each robot from motion information maps. We use a three-layer CNN that takes these maps as input to generate a suitable action to drive each robot to its goal position. Our approach is general, learns an optimal policy using a multi-scenario, multi-state training algorithm, and can directly handle raw sensor measurements for local observations. We demonstrate the performance on dense, complex benchmarks wit…
ZONDA: Zero-shot Object Navigation with Dynamic Avoidance in Multi-floor Environments
Southern University of Science and Technology（南方科技大学）；Guangdong Direct Drive Technology Limited（广东直驱技术有限公司）；South China University of Technology（华南理工大学）；Great Bay University（大湾区大学）
Hybrid Feedback Control for Global Navigation with Locally Optimal Obstacle Avoidance in n-Dimensional Spaces
Department of Electrical Engineering, Lakehead University（电气工程系，拉科夫大学）；Department of Computer Science and Engineering, University of Quebec in Outaouais（计算机科学与工程系，魁北克大学瓦省分校）
Luleå University of Technology（吕勒奥理工大学）；WideFind AB（WideFind公司）
Micro Aerial Vehicles (MAVs) navigation in subterranean environments is gaining attention in the field of aerial robotics, however there are still multiple challenges for collision free navigation in such harsh environments. This article proposes a novel baseline solution for collision free navigation with Nonlinear Model Predictive Control (NMPC). In the proposed method, the MAV is considered as a floating object, where the velocities on the $x$, $y$ axes and the position on altitude are the references for the NMPC to navigate along the tunnel, while the NMPC avoids the collision by consideri…
This paper considers the problem of robot motion planning in a workspace with obstacles for systems with uncertain 2nd-order dynamics. In particular, we combine closed form potential-based feedback controllers with adaptive control techniques to guarantee the collision-free robot navigation to a predefined goal while compensating for the dynamic model uncertainties. We base our findings on sphere world-based configuration spaces, but extend our results to arbitrary star-shaped environments by using previous results on configuration space transformations. Moreover, we propose an algorithm for e…
Self-navigation, referred as the capability of automatically reaching the goal while avoiding collisions with obstacles, is a fundamental skill required for mobile robots. Recently, deep reinforcement learning (DRL) has shown great potential in the development of robot navigation algorithms. However, it is still difficult to train the robot to learn goal-reaching and obstacle-avoidance skills simultaneously. On the other hand, although many DRL-based obstacle-avoidance algorithms are proposed, few of them are reused for more complex navigation tasks. In this paper, a novel danger-aware adaptiv…
LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance
Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）；School of Data Science, USTC（中国科学技术大学数据科学学院）；School of Computer Science and Technology, USTC（中国科学技术大学计算机科学与技术学院）
"Why This Avoidance Maneuver?" Contrastive Explanations in Human-Supervised Maritime Autonomous Navigation
Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), Ålesund, Norway（挪威科技大学（NTNU）奥勒松校区信息通信技术与自然科学系）；Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), Ålesund, Norway（挪威科技大学（NTNU）奥勒松校区海洋运营与土木工程系）；Department of Science and Mathematics, Volda University College (HVO), Volda, Norway（沃尔达大学学院（HVO）科学与数学系）；Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway（挪威科技大学（NTNU）特隆赫姆校区工程控制论系）
Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoi…
Online path planning for multiple unmanned aerial vehicle (multi-UAV) systems is considered a challenging task. It needs to ensure collision-free path planning in real-time, especially when the multi-UAV systems can become very crowded on certain occasions. In this paper, we presented a vision-based decentralized collision-avoidance policy for multi-UAV systems, which takes depth images and inertial measurements as sensory inputs and outputs UAV's steering commands. The policy is trained together with the latent representation of depth images using a policy gradient-based reinforcement learnin…
City University of Hong Kong（香港城市大学）；Metoak Technology (Beijing) CO., LTD（北京梅托克科技有限公司）；Fuzhou University（福州大学）
In this paper, we present a decentralized sensor-level collision avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent's steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy gradien…
Imitation learning holds the promise to address challenging robotic tasks such as autonomous navigation. It however requires a human supervisor to oversee the training process and send correct control commands to robots without feedback, which is always prone to error and expensive. To minimize human involvement and avoid manual labeling of data in the robotic autonomous navigation with imitation learning, this paper proposes a novel semi-supervised imitation learning solution based on a multi-sensory design. This solution includes a suboptimal sensor policy based on sensor fusion to automatic…
