This paper presents a robust computationally efficient real-time collision avoidance algorithm for Unmanned Aerial Vehicle (UAV), namely Memory-based Wall Following-Artificial Potential Field (MWF-APF) method. The new algorithm switches between Wall-Following Method (WFM) and Artificial Potential Field method (APF) with improved situation awareness capability. Historical trajectory is taken into account to avoid repetitive wrong decision. Furthermore, it can be effectively applied to platform with low computing capability. As an example, a quad-rotor equipped with limited number of Time-of-Flight (TOF) rangefinders is adopted to validate the effectiveness and efficiency of this algorithm. Both software simulation and physical flight test have been conducted to demonstrate the capability of the MWF-APF method in complex scenarios.
Department of Cybernetics, Czech Technical University in Prague（捷克技术大学布拉格分校自动化系）；Mechanical and Aerospace Engineering, Old Dominion University（旧 Dominion 大学机械与航空航天工程系）；Mechanical Engineering Faculty, Sahand University of Technology（沙赫德大学机械工程系）；Aksaray University（阿克萨雷大学）
This paper addresses the motion planning problem for a team of aerial agents under high level goals. We propose a hybrid control strategy that guarantees the accomplishment of each agent's local goal specification, which is given as a temporal logic formula, while guaranteeing inter-agent collision avoidance. In particular, by defining 3-D spheres that bound the agents' volume, we extend previous work on decentralized navigation functions and propose control laws that navigate the agents among predefined regions of interest of the workspace while avoiding collision with each other. This allows…
Luleå University of Technology（吕勒奥理工大学）；Jet Propulsion Laboratory California Institute of Technology（加州理工学院喷气推进实验室）
This article proposes a Novel Nonlinear Model Predictive Control (NMPC) for navigation and obstacle avoidance of an Unmanned Aerial Vehicle (UAV). The proposed NMPC formulation allows for a fully parametric obstacle trajectory, while in this article we apply a classification scheme to differentiate between different kinds of trajectories to predict future obstacle positions. The trajectory calculation is done from an initial condition, and fed to the NMPC as an additional input. The solver used is the nonlinear, non-convex solver Proximal Averaged Newton for Optimal Control (PANOC) and its ass…
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…
Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Vanke School of Public Health（万科公共卫生学院）；School of Automation（自动化学院）
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base stations (GBSs) is a challenging task. In this paper, we first reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints as a sequential decision making problem in the discrete time domain. We, then, propose a decentralized deep reinforcement learning approach to solve the problem. More specifically, a value network is d…
University of Luxembourg（卢森堡大学）；Interdisciplinary Centre for Security, Reliability and Trust（跨学科安全、可靠与信任中心）
This paper presents an integrated approach that combines trajectory optimization and Artificial Potential Field (APF) method for real-time optimal Unmanned Aerial Vehicle (UAV) trajectory planning and dynamic collision avoidance. A minimum-time trajectory optimization problem is formulated with initial and final positions as boundary conditions and collision avoidance as constraints. It is transcribed into a nonlinear programming problem using Chebyshev pseudospectral method. The state and control histories are approximated by using Lagrange polynomials and the collocation points are used to s…
Efficient Collision-Avoidance Constraints for Ellipsoidal Obstacles in Optimal Control: Application to Path-Following MPC and UAVs
Institute of Engineering and Computational Mechanics, University of Stuttgart（工程与计算力学研究所，斯图加特大学）；Chair of Systems Modeling and Simulation, Saarland University（系统建模与仿真教授团，萨尔兰大学）；Department of Mechanical Engineering, LUT University（机械工程系，卢霍斯大学）
In this paper, we tackle the problem of Unmanned Aerial (UA V) path planning in complex and uncertain environments by designing a Model Predictive Control (MPC), based on a Long-Short-Term Memory (LSTM) network integrated into the Deep Deterministic Policy Gradient algorithm. In the proposed solution, LSTM-MPC operates as a deterministic policy within the DDPG network, and it leverages a predicting pool to store predicted future states and actions for improved robustness and efficiency. The use of the predicting pool also enables the initialization of the critic network, leading to improved co…
Autonomous Systems Lab. (ASL), ETH Zürich（苏黎世联邦理工学院自主系统实验室）；TU Delft（代尔夫特理工大学）
Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive rep…
Czech Technical University in Prague（布拉格捷克理工大学）；International University of Rabat（拉巴特国际大学）
Multi-rotor Unmanned Aerial Vehicles (UAVs) need to tilt in order to move; this modifies the UAV's antenna orientation. We consider the scenario where a multi-rotor UAV serves as a communication relay between a Base Station (BS) and another UAV. We propose a framework to generate feasible trajectories for the multi-rotor UAV relay while considering its motion dynamics and the motion-induced changes of the antenna orientation. The UAV relay's trajectory is optimized to maximize the end-to-end number of bits transmitted. Numerical simulations in MATLAB and Gazebo show the benefits of accounting …
University of Freiburg（弗莱堡大学）；Robert Bosch GmbH（罗伯特·博世股份有限公司）
Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remain subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a nonlinear model predictive control (NMPC) framework, that is based on a novel convex inner approximation of the collision avoidance constraint. The proposed Convex Inner ApprOximation (CIAO) method finds kinodynamically feasible and continuous time collision free trajectories, in few iterations, typ…
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…
In recent years, unmanned aerial vehicles (UAVs) are used for numerous inspection and video capture tasks. Manually controlling UAVs in the vicinity of obstacles is challenging, however, and poses a high risk of collisions. Even for autonomous flight, global navigation planning might be too slow to react to newly perceived obstacles. Disturbances such as wind might lead to deviations from the planned trajectories. In this work, we present a fast predictive obstacle avoidance method that does not depend on higher-level localization or mapping and maintains the dynamic flight capabilities of UAV…
This paper proposes a perception-shared and swarm trajectory global optimal (STGO) algorithm fused UAVs formation motion planning framework aided by an active sensing system. First, the point cloud received by each UAV is fit by the gaussian mixture model (GMM) and transmitted in the swarm. Resampling from the received GMM contributes to a global map, which is used as the foundation for consensus. Second, to improve flight safety, an active sensing system is designed to plan the observation angle of each UAV considering the unknown field, overlap of the field of view (FOV), velocity direction …
Omnidirectional Solid-State mmWave Radar Perception for UAV Power Line Collision Avoidance
Drone Infrastructure Inspection and Interaction Group, Section for Digital and High-Frequency Electronics, Department of Mechanical and Electrical Engineering, Faculty of Engineering, University of Southern Denmark（丹麦南部大学工程学院机械与电气工程系数字与高频电子学部无人机基础设施检查与交互小组）
Energy efficiency is of critical importance to trajectory planning for UAV swarms in obstacle avoidance. In this paper, we present $E^2Coop$, a new scheme designed to avoid collisions for UAV swarms by tightly coupling Artificial Potential Field (APF) with Particle Swarm Planning (PSO) based trajectory planning. In $E^2Coop$, swarm members perform trajectory planning cooperatively to avoid collisions in an energy-efficient manner. $E^2Coop$ exploits the advantages of the active contour model in image processing for trajectory planning. Each swarm member plans its trajectories on the contours o…
In this article we propose a distributed collision avoidance scheme for multi-agent unmanned aerial vehicles(UAVs) based on nonlinear model predictive control (NMPC),where other agents in the system are considered as dynamic obstacles with respect to the ego agent. Our control scheme operates at a low level and commands roll, pitch and thrust signals at a high frequency, each agent broadcasts its predicted trajectory to the other ones, and we propose an obstacle prioritization scheme based on the shared trajectories to allow up-scaling of the system. The NMPC problem is solved using an ad hoc …
HEPP: Hyper-efficient Perception and Planning for High-speed Obstacle Avoidance of UAVs
Department of Mechanical Engineering, The University of Hong Kong（机械工程系，香港大学）；Northwestern Polytechnical University（西北工业大学）；Harbin Institute of Technology（哈尔滨工业大学）
Robust Policy Learning for Multi-UAV Collision Avoidance with Causal Feature Selection
College of Engineering, Shantou University（汕头大学工程学院）；School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）；University of Electronic Science and Technology of China（电子科技大学）；International Cooperation Base of Evolutionary Intelligence and Robotics（进化智能与机器人国际合作基地）
This paper presents the application of the Rapidly-exploring Random Tree Star (RRT*) algorithm for multicopter collision avoidance in photogrammetry missions. For better applicability, the presented algorithm redirects the drone onto a predefined mission's path. The experiments are conducted in the simulation software gazebo utilizing a ROS interface to the widely known autopilot software PX4. For obstacle detection, a simulated Intel D435 stereo camera is used. The experiments include two different scenarios, each conducted with two different maximum velocities. The results show that the prob…
Vietnam National University（越南国立大学）；University of Technology Sydney（悉尼科技大学）
In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to…
Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
For accomplishing a variety of missions in challenging environments, the capability of navigating with full autonomy while avoiding unexpected obstacles is the most crucial requirement for UAVs in real applications. In this paper, we proposed such a computationally efficient obstacle avoidance trajectory planner that can be used in cluttered unknown environments. Because of the narrow view field of single depth camera on a UAV, the information of obstacles around is quite limited thus the shortest entire path is difficult to achieve. Therefore we focus on the time cost of the trajectory planne…
There is a risk of collision when multiple UAVs land simultaneously without communication on the same platform. This work accomplishes vision-based autonomous landing and uses a deep-learning-based method to realize collision avoidance during the landing process.
This paper studies the collision avoidance problem for autonomous multiple fixedwing UAVs in the complex integrated airspace. By studying and combining the online path planning method, the distributed model predictive control algorithm, and the geometric reactive control approach, a three-layered collision avoidance system integrating conflict detection and resolution procedures is developed for multiple fixed-wing UAVs modeled by unicycle kinematics subject to input constraints. The effectiveness of the proposed methodology is evaluated and validated via test results of comparative simulation…
University of Lincoln（林肯大学）；Lingnan Normal University（岭南师范学院）；Guangzhou University（广州大学）
Building a reliable and efficient collision avoidance system for unmanned aerial vehicles (UAVs) is still a challenging problem. This research takes inspiration from locusts, which can fly in dense swarms for hundreds of miles without collision. In the locust's brain, a visual pathway of LGMD-DCMD (lobula giant movement detector and descending contra-lateral motion detector) has been identified as collision perception system guiding fast collision avoidance for locusts, which is ideal for designing artificial vision systems. However, there is very few works investigating its potential in real-…
Multi-UAV Formation Cooperative Obstacle Avoidance and Adaptive Shape Deformation Control in Complex Environments Based on BI-APF-RRT and Affine Transformation
College of Marine Information Engineering（海洋信息工程学院）
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…
Collision Avoidance for Multiple UAVs in Unknown Scenarios with Causal Representation Disentanglement
College of Engineering, Shantou University（汕头大学工程学院）；School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）；University of Electronic Science and Technology of China（电子科技大学）；International Cooperation Base of Evolutionary Intelligence and Robotics（进化智能与机器人国际合作基地）
Traffic collision avoidance systems (TCAS) are used in order to avoid incidences of mid-air collisions between aircraft. We present a game-theoretic approach of a TCAS designed for autonomous unmanned aerial vehicles (UAVs). A variant of the canonical example of game-theoretic learning, fictitious play, is used as a coordination mechanism between the UAVs, that should choose between the alternative altitudes to fly and avoid collision. We present the implementation results of the proposed coordination mechanism in two quad-copters flying in opposite directions.
Czech Technical University in Prague（捷克理工大学）；University of Twente（特温特大学）
A perception-aware Nonlinear Model Predictive Control (NMPC) strategy aimed at performing vision-based target tracking and collision avoidance with a multi-rotor aerial vehicle is presented in this paper. The proposed control strategy considers both realistic actuation limits at the torque level and visual perception constraints to enforce the visibility coverage of a target while complying with the mission objectives. Furthermore, the approach allows to safely navigate in a workspace area populated by dynamic obstacles with a ballistic motion. The formulation is meant to be generic and set up…
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…
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）
EURECOM（欧洲通信学院）；Technical University of Munich（慕尼黑工业大学）
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods. We propose a new end-to-end reinforcement learning (RL) approach to UAV-enabled data collection from Internet of Things (IoT) devices in an urban environment. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. While previous approaches, learning and non-learning based, must perform expensive recomputations or relearn a behavior when important scenario paramet…
