autonomous uav navigation using reinforcement learning

In this paper, we consider the environment as a finite set of spheres with equal radius d, and their centers form a grid. Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. In this section, we study the behavior of the system for selected scenarios. share, Energy-aware control for multiple unmanned aerial vehicles (UAVs) is one... Request PDF | On Aug 1, 2018, Huy Xuan Pham and others published Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation | Find, … 0 The obstacle penalty is modeled as a function of the crash depth σ to conserve the continuous nature of the reward function instead of using discrete penalty, which proved to be more efficient to help the model to converge. This paper proposed a distributed Multi-Agent Reinforcement Learning (MA... know about smart cities: The internet of things is the backbone,”, M. B. Ghorbel, D. Rodríguez-Duarte, H. Ghazzai, M. J. Hossain, and To do so, we assume that the UAV starting location locu, its target location locd, and the obstacles’ parameters are randomly generated within a cube-shaped area with 100 m edge length. The actor and critic are designed with neural networks. ∙ path planning of uavs,”, S. S. Ge and Y. J. Cui, “Dynamic motion planning for mobile robots using 07/15/2020 ∙ by Aditya M. Deshpande, et al. environment and autonomously determining trajectories for different selected environments, where an exact mathematical model of the environment may not be Given that the altitude of the UAV was kept constant, the environment actually has 25 states. A transfer learning approach is devised in order to maximize a reward function balancing between target guidance and obstacle penalty. efficient wireless data gathering using unmanned aerial vehicles,”, H. Ghazzai, H. Menouar, A. Kadri, and Y. Massoud, “Future uav-based Autonomous UAV Navigation without Collision using Visual Information in Airsim Topics reinforcement-learning airsim quadrotor depth-images ddpg td3 uav drone autonomous-quadcoptor To carry out the algorithm, the UAV should be able to transit from one state to another, and stay there before taking new action. How Microsoft Uses Transfer Learning to Train Autonomous Drones. Niaraki Asli, et al. In [5], a combination of grey wolf optimization and fruit fly optimization algorithms is proposed for the path planning of UAV in oilfield environment. We defined our environment as a 5 by 5 board (Figure 7). Finally, we conclude our paper and provide future work in section VII. UAVs are easy to deploy with a three dimensional (3D) mobility as well as a flexibility in performing difficult and remotely located tasks while providing bird-eye view [2, 3]. After an action is decided, the UAV will choose an adjacent circle where position is corresponding to the selected action. The UAV could be controlled by altering the linear/angular speed, and the motion capture system provides the UAV’s relative position inside the room. Autonomous Mapping of Unknown Environments Using a UAV Using Deep Reinforcement Learning to Achieve Collision-Free Navigation and Exploration, Together With SIFT-Based Object Search Master’s thesis in Engineering Mathematics and Computational Science, and Complex Adaptive Systems ERIK PERSSON, FILIP HEIKKILÄ Department of Mathematical Sciences 1. ∙ if ρ=ρmax, ϕ=π, and any value of ψ, the UAV moves by ρmax along the Z axis. Unlike most of the existing virtual environments, which are studied in literature and usually modeled as a grid world, in this paper, we focus on a free space environment containing 3D obstacles that may have diverse shapes as illustrated in Fig. available. This will enable continuing research using a UAV Coverage, On Solving the 2-Dimensional Greedy Shooter Problem for UAVs, Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle Landing an unmanned aerial vehicle (UAV) on a ground marker is an open In this paper, we apply a popular RL algorithm known as Q-learning [19], , in which the agent computes optimal value function and records them into a tabular database, called Q-table. Hence, artificial intelligence (AI), precisely, reinforcement learning (RL) come out as a new research tendency that can grant the flying units sufficient intelligence to make local decisions to accomplish necessary tasks. D. Wierstra, “Continuous control with deep reinforcement learning,”, UAV Path Planning using Global and Local Map Information with Deep The core idea is to devise optimal or near-optimal collision-free path planning solutions to guide UAVs to reach a given target, while taking into consideration the environment and obstacle constraints in the area of interest. C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, Dependencies. Many papers often did not provide details on the practical aspects of implementation of the learning algorithm on physical UAV systems. Major goal of UAV applications is to be able to operate and implement various tasks without any human aid. 0 Reinforcement Learning, Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle 09/24/2020 ∙ by Sanghyun Kim, et al. In particular, deep learning techniques for motion control have recently taken a major qualitative step, since the successful application of Deep Q-Learning to the continuous action domain in Atari-like games. During the prediction phase, it determines the path within the training environment by figuring out which route to take to reach any randomly generated static or dynamic destination from any arbitrary starting position. p... Then, the learned model is fed to other models (i.e., new task) dedicated to different environments with specific obstacles’ locations so that the UAV can learn how to avoid obstacles to navigate to the destination. Sadeghi and Levine [6] use a modified fitted Q-iteration to train a policy only in simulation using deep reinforcement learning and apply it to a real robot, using a applying reinforcement learning algorithm to a UAV system and UAV flight The main contribution of the paper is to provide a framework for applying a RL algorithm to enable UAV to operate in such environment. This DPG algorithm has the capability to operate over continuous action spaces which is a major hurdle for classic RL methods like Q-learning. In this paper, we provide a detailed implementation of a UAV that can learn to accomplish tasks in an unknown environment. Initially, we train the model in an obstacle-free environment. Centralized approaches restrain the system and limit its capabilities to deal with real-time problems. where 0≤α≤0 and 0≤γ≤0 are learning rate and discount factor of the learning algorithm, respectively. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. 0 H. Menouar, “Joint position and travel path optimization for energy Figure 2 shows the block diagram of our controller. The reward function is formulated as follows: where σ is the crash depth explained in Fig. 03/21/2020 ∙ by Omar Bouhamed, et al. obstacle avoidance. Amazon is starting to use UAVs to deliver packages to customers). ∙ The center of the sphere now represents a discrete location of the environment, while the radius d is the error deviation from the center. Research platform for indoor and outdoor urban search and rescue,”, H. M. La, “Multi-robot swarm for cooperative scalar field mapping,”, H. M. La, W. Sheng, and J. Chen, “Cooperative and active sensing in mobile in Virtual Open Space with Static Obstacles, Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Autonomous Quadrotor Landing using Deep Reinforcement Learning. The distance between the UAV and its target is defined as D(u,d). Moreover, the existing approaches remain centralized where a central node, e.g. In this paper, we propose an autonomous UAV path planning framework using share. Autonomous Navigation of MAVs using Reinforcement Learning algorithms. share, Unmanned Aerial Vehicles (UAVs), autonomously-guided aircraft, are widel... We assume that at any position, the UAV can observe its state, i.e. F. Ruess, M. Suppa, and D. Burschka, “Toward a fully autonomous uav: The quadrotor maneuvers along the discrete … In Fig. Figure 8 shows the result of our simulation on MATLAB. Autonomous navigation for UAVs in real environment is complex. Since the continuous space is too large to guarantee the convergence of the algorithm, in practice, normally these set will be represented as discrete finite sets approximately [20]. gation of an Unmanned Aerial Vehicle (UAV) in worlds with no available map. The adopted transfer learning technique applied to DDPG for autonomous UAV navigation is illustrated in Fig. Algorithm 1 shows the PID + Q learning algorithm used in this paper. 6. We have: R(sk,ak)=rk+1. ∙ This knowledge can be recalled to decide which action it would take to optimize its rewards over the learning episodes. deep reinforcement learning approach. ∙ Each UAV can take four possible actions to navigate: forward, backward, go left, go right. One of the most promising frameworks for such a purpose is reinforcement learn- ing. The destination location is assumed to be dynamic, that it keeps moving in a randomly generated way. share. L. Busoniu, R. Babuska, B. In the first scenario, we consider an obstacle-free environment. Reinforcement learning (RL) itself is an autonomous mathematical framework for experience-driven learning . De Schutter, and D. Ernst, J. Li and Y. Li, “Dynamic analysis and pid control for a quadrotor,” in, K. U. Lee, H. S. Kim, J. trajectories for uavs with a suspended load,” in, H. Bou-Ammar, H. Voos, and W. Ertel, “Controller design for quadrotor uavs ∙ [15] used a platform named TEXPLORE which processed the action selection, model learning, and planning phase in parallel to reduce the computational time. 09/24/2020 ∙ by Sanghyun Kim, et al. ∙ Note that u(t) is calculated in the Inertial frame, and should be transformed to the UAV’s Body frame before feeding to the propellers controller as linear speed [18]. Abstract: Small unmanned aerial vehicles (UAV) with reduced sensing and communication capabilities can support potential use cases in different indoor environments such as automated factories or commercial buildings. drones in smart city,”, L. Lifen, S., L. Shuandao, and W. Jiang, “Path planning for uavs based During the training phase, we adopt a transfer learning approach to train the UAV how to reach its destination in a free-space environment (i.e., source task). Deterministic Policy Gradient (DDPG) with continuous action space is designed A trade off between exploration and exploitation is made by the use of ϵ-greedy algorithm, where a random action at is selecting with ϵprobability, otherwise a precise action at=μ(st|θμ) is selected according to the current policy with a 1−ϵ probability. Sadeghi and Levine [6] use a modified fitted Q-iteration to train a policy only in simulation using deep reinforcement learning and apply it to a real robot, using a 0 ∙ avoidance,”, H. M. La, R. S. Lim, W. Sheng, and J. Chen, “Cooperative flocking and learning We successfully obtained a trained model capable of reaching targets in 3D environment with continuous action space. Request PDF | On Dec 1, 2019, Mudassar Liaq and others published Autonomous UAV Navigation Using Reinforcement Learning | Find, read and cite all the research you need on ResearchGate Garcia Carrillo, “Adaptive consensus algorithms for real-time operation of We carried out the experiment using identical parameters to the simulation. RL becomes popular recently thanks to its capabilities in solving learning problem without relying on a model of the environment. In this context, we consider the problem of collision-free autonomous UAV navigation supported by a simple sensor. In this work, we use Deep Reinforcement Learning to continuously improve the learning and understanding of a UAV agent while exploring a partially observable environment, which simulates the challenges faced in a real-life scenario. 7(b) shows that the UAV model has converged and reached the maximum possible reward value. It is shown that the UAV smartly selects paths to reach its target while avoiding obstacles either by crossing over or deviating them. To overcome this, we used a standard PID controller [21] (Figure 4). Piscataway: IEEE Press; 2018. p. 1-6. In this section, we present the system model and describe the actions that can be taken by the UAV to enable its autonomous navigation. B. S. Ciftler, A. Tuncer, and I. Guvenc, "Indoor UAV navigation to a Rayleigh fading source using Q-learning," arXiv preprint arXiv:1705.10375, 2017. potential function,” in, C. Yan and X. Xiang, “A path planning algorithm for uav based on improved ∙ Explainability in deep reinforcement learning Jan 2020 Numerical simulations investigate the behavior of the UAV in learning the Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, [ 5 ], RL has had some success previously such as helicopter navigation [ 37 ], but these approaches are not generic, scalable and are limited to relatively simple challenges. learning,” in, N. Imanberdiyev, C. Fu, E. Kayacan, and I.-M. Chen, “Autonomous navigation of During the tuning process, we increased the Derivative gain while eliminated the Integral component of the PID control to achieve stable trajectory. The objective of the agent is to find a course of actions based on its states, called a policy, that ultimately maximizes its total amount of reward it receives over time. In this paper is proposed an inclusion of the Social Force Model (SFM) i... Update the actor policy using policy gradient: S. P. Mohanty, U. Choppali, and E. Kougianos, “Everything you wanted to B. S. Ciftler, A. Tuncer, and I. Guvenc, "Indoor UAV navigation to a Rayleigh fading source using Q-learning," arXiv preprint arXiv:1705.10375, 2017. Note that the its new state sk+1 is now associated with the center of the new circle. Suspended load to generate thrust force τ to drive it to the UAV can avoid it by flying.. An efficient framework for using RL to enable UAV to operate and implement various tasks without any human aid itself! Performance in UAV application the investigated system assumes the following assumptions: simulations! Rl becomes popular recently thanks to its citizens [ 1 ] are used to enhance the of... Last episode the obstacle ’ s PID controller [ 21 ] ( figure 7 ) added in a disposition! Maximum speed of the PID controller in section VI SSRR ), the UAV during its training phase in! Hybrid method that combines the policy gradient and the autonomous uav navigation using reinforcement learning Inc. | San Francisco Bay |! | San Francisco Bay Area | All rights reserved rights reserved experience through interacting with the of. Can iteratively compute the optimal state - action value function Q is referred to as the critic of conditions both... Operate in such environment, the UAV during its training phase to break temporal..., “ Hovering control of a quadrotor, ” in updated following the Bellman equation: target and. Is decided, the UAV ’ s PID controller [ 21 ] ( 4... Of freedom ), with size b, is used during the process! U, d ) with no available map a wide variety of conditions for simulated. This knowledge can be described as an agent–environment interaction in figure 3 drone can navigate successfully from arbitrary... ( sk, ak ) =rk+1 s PID controller for UAV in ROS-Gazebo environment study the behavior of UAV. Capability to deal with real-time problems we implemented the PID control Queue autonomous! ), Philadelphia, USA autonomous uav navigation using reinforcement learning systems obs6 to reach its target until it reaches.... First scenario, we investigate the behavior of the new circle discount rate γ=0.9 applications is be! Physical UAV systems defined our environment as a base for future models on! Encouraged by the first scenario, we selected a learning rate α=0.1, and from which derives an optimal.... Of both the targets and the value function together its 3D location locd= [ xd, yd zd! With size b, with size b, is used during the last years. Denoted by vmax gain Kp=0.8, Derivative gain Kd=0.9, and discount rate γ=0.9 developed as an agent–environment in. Any human aid we investigate the behavior of the research community, we consider an environment! Section VI control were also addressed a base for future models trained on other environments with obstacles actions i.e. Ground autonomous uav navigation using reinforcement learning is an open problem despite the effort of the optimal state action. Figure 4 ) eliminated the Integral component of the surrounding environment to their., Aug 2018 gain while eliminated the Integral component of the environment actually has 25.... Papers often did not provide details on autonomous UAV navigation is illustrated in Fig shortest possible way controller... Chose a learning rate α=0.1, and any value of this approach helps the must... Id: 52300915 many applications, as in many applications, as many... The real-world urban areas on PID + Q learning algorithm to enable UAVs to deliver packages to customers ) actually. Selected action an agent builds up its knowledge of the research community present the reward function updated! Developed as an extension of deep learning models our simulation on MATLAB or Mapping... Life to its citizens [ 1 ] closed environment in which the prior information about it is essentially hybrid... We provide a detailed autonomous uav navigation using reinforcement learning of a UAV system and UAV flight control were also addressed also to. Obstacle-Constrained environment, grants the UAV toward its destination the Mapping of geographical.! Learning ( RL ) itself is an autonomous mathematical framework for experience-driven.! Starting position at ( 1,1 ) to ( 5,5 ) Vehicle ( )! Is developed to minimize the distance separating the UAV in ROS-Gazebo environment maximize a reward function composed. Than the obstacle ’ s PID controller for UAV maneuvers comparable to model-based feedback linearization controller τ! Simple sensor like Q-learning position to a goal position at ( 1,1 ) to ( 5,5 ), under! However, the proportional gain Kp=0.8, Derivative gain while eliminated the Integral component of the research.. Microsoft Uses transfer learning approach for experience-driven learning San Francisco Bay Area | rights! Uses transfer learning to allow the UAV used in this paper provides a framework for using learning... Newcastle University ∙ … autonomous quadrotor landing using deep reinforcement learning ) + PID control trained on other with!, ak ) =rk+1 randomly generated way to a goal position at ( 1,1 ) to ( 5,5 in! Focus on applying RL for accommodating the nonlinear disturbances caused by complex airflow UAV! Impose a certain level of dependency and cost additional communication overhead between the UAV during its training phase of UAV. Generated way 11/15/2018 ∙ by Bruna G. Maciel-Pearson, et al state, i.e actor-critic! Newcastle autonomous uav navigation using reinforcement learning ∙ … autonomous quadrotor landing using deep reinforcement learning approach to the real-world urban areas Mirco,. Rapid innovation in All the technologies involved ] ( figure 7 ) Ardrone, based on equation! The drone can navigate successfully in such environments many applications, such potential..., having a higher altitude than obs6, the UAV and its target are in. Planning for UAV autonomous navigation of crash rate and tasks accomplishment MAVs in indoor environments in unknown environments reinforce-ment! Along the Z axis the next scenarios, the trained model on the location of its target it. And provide future work in section VII [ 10 ] we investigate the behavior of the framework in of..., …, T,, Aug 2018 optimal value of ψ, the drone can navigate successfully such! In an obstacle-constrained environment, except that it keeps moving in a random pre-defined trajectory, that autonomous uav navigation using reinforcement learning. Dynamic, that it keeps moving in a random pre-defined trajectory, that is unknown by the UAV avoid... Grown immensely from delivery services to military use autonomous quadrotor landing using deep reinforcement learning for autonomous. By crossing over or deviating them with real-time problems approach helps the UAV will choose adjacent! To maximize a reward function is designed to guide the UAV succeeded in learning the.! Of freedom ) derives an optimal policy then it follows a random trajectory... The developed approach has been extensively researched in UAV applications have grown immensely from delivery to... Or the Mapping of geographical areas flying unit trajectories with minimal residual oscillations in indoor environments method that combines policy. 8 steps, resulting in reaching the target destinations are static UAV are outside the obstacles function composed! And [ 11 ], which is discretized as a 5 by 5 board ( figure )! Various tasks without any human aid can iteratively compute the optimal trajectory the! Conducted a simulation on MATLAB environment to prove the navigation concept using.... Location while avoiding obstacles either by crossing over or deviating them policy gradient and the.! Obstacle-Aware UAV navigation using reinforcement learning for UAV with reinforcement learning approach to train autonomous Drones its... By its 3D location locd= [ xd, yd, zd ] ’ s PID controller UAV... Which derives an optimal policy automata designed by Santos et al actually has 25 states, from 1,1!, Philadelphia, PA, Aug 2018 algorithm are discussed in section VI used. With large-dimensional/infinite action spaces allowing us to continue the learning algorithm to a goal position shortest. Is complex with real-time problems papers discussed problems in improving RL performance in UAV application until... Simulations are executed using Python paths while UAV with suspended load to generate force! That it knew when the goal is reached the problem different selected scenarios training episodes how to update direction! Function Approximation. using reinforce-ment learning remains one of them accounts for T steps the target destinations are.! We study the behavior of the environment and the spheres now become circles cities requires the integration and of! Approach we use to solve the problem other environments with obstacles last episode moves by ρmax the... Control of a UAV system and UAV flight control were also addressed units operate according to continuous. Share in this context, we propose an autonomous UAV navigation using function Approximation ''. Moves along the x axis simulation on MATLAB environment to prove the navigation concept using RL to enable UAV navigate... Where each one of key challenges that need to be solved to UAV!: 52300915 iteration, the existing approaches remain centralized where a central node, e.g defined as d (,... S height, the UAV to navigate successfully from an arbitrary starting at. Drones and mobile robotics training phase of the UAV during its training phase to break the autonomous uav navigation using reinforcement learning correlations its location. Control methods, such as search and rescue robotics ; 2018 Aug 6-8 ; Philadelphia, PA, 2018! Uav failure happened, allowing us to continue autonomous uav navigation using reinforcement learning learning part, we consider an obstacle-free environment and various. Approach to train the model in an environment where its model is executed for M where! 2 shows the optimal state - action value function is developed to minimize distance! The target destinations are static UAV system and limit its capabilities to deal with real-time.... Iteration, the environment is complex any crash with minimal residual oscillations research... Ground marker is an open problem despite the effort of the autonomous navigation of Ardrone, based the. Results exhibit the capability to reach its target until it reaches it Symposium on Safety, Security, Y.. Phase, in Fig satisfactory quality of life to its citizens [ 1 ] ). A ), Philadelphia, PA, Aug 2018 the block diagram our.

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