autonomous uav navigation using reinforcement learning

We implemented the PID controller in section IV to help the UAV carry out its action. The objective for the UAV was to start from a starting position at (1,1) and navigate successfully to the goal state (5,5) in shortest way. In Fig. For the sake of clarity, the figures concerning the UAV path planning are presented in only 2D dimension area (i.e., plan(x,y)) and we provide beside each dot, the altitude of either the target or the UAV. RL algorithms have already been extensively researched in UAV applications, as in many other fields of robotics [9, 10]. “Collision-free navigation and efficient scheduling for fleet of multi-rotor In many realistic cases, however, building models is not possible because the environment is insufficiently known, or the data of the environment is not available or difficult to obtain. In Fig. The simulation results exhibit the capability of UAVs in learning from the surrounding environment to determine their trajectories in real-time. 70 The UAV was expected to navigate from starting position at (1,1) to goal position at (5,5) in shortest possible way. 7(a) shows that the UAV learns to obtain the maximum reward value in an obstacle-free environment. ∙ One issue is that most current research relies on the accuracy of the model describing the target, or prior knowledge of the environment [6, 7]. ∙ 0 D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,”, T. P. Lillicrap, J. J. Piscataway: IEEE Press; 2018. p. 1-6. 09/17/2020 ∙ by Ran Zhang, et al. 09/24/2020 ∙ by Sanghyun Kim, et al. 6. A diagram summarizing the actor-critic architecture is given in Fig. The rewards that an UAV can get depend whether it has reached the pre-described goal G, recognized by the UAV using a specific landmark, where it will get a big reward. 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. UAV with reinforcement learning (RL) capabilities for indoor autonomous navigation. Similar to the simulation, the UAV will have a big positive reward of +100 if it reaches the goal position, otherwise it will take a negative reward (penalty) of -1. learning,” in, N. Imanberdiyev, C. Fu, E. Kayacan, and I.-M. Chen, “Autonomous navigation of Note that the training phase of the DDPG model is executed for M episodes where each one of them accounts for T steps. scenarios. Also, target networks are exploited to avoid the divergence of the learning algorithm caused by the direct updates of the networks weights with the gradients obtained from the TD error signal. 6(c), having a higher altitude than obs6, the UAV crossed over obs6 to reach its target. 12/11/2019 ∙ by Bruna G. Maciel-Pearson, et al. The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by the rapid innovation in all the technologies involved. Many papers focus on applying RL Major goal of UAV applications is to be able to operate and implement various tasks without any human aid. learning,” in, S. L. Waslander, G. M. Hoffmann, J. S. Jang, and C. J. Tomlin, “Multi-agent 09/26/2019 ∙ by AE. source task) and use it to improve the UAV learning of new tasks where it updates its path based on the obstacle locations while flying toward its target. share, Landing an unmanned aerial vehicle (UAV) on a ground marker is an open ∙ A. Rusu, J. Veness, M. G. Bellemare, This paper provides a framework for using reinforcement learning to allow the UAV to navigate successfully in such environments. The destination d is defined by its 3D location locd=[xd,yd,zd]. As for the critic, its output Q(s,a|θμ) is a signal having form of a Temporal Difference (TD) error to criticize the actions made by the actor knowing the current state of the environment. 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. Therefore, to overcome the physical constraint on UAV’s battery life cycle, we also designed a GUI on MATLAB to help discretize the learning process into episodes (Figure 10). Training in such environment, grants the UAV the capability to reach any target in the covered 3D area with continuous space action. multi-agent systems affected by switching network events,”, T. Tomic, K. Schmid, P. Lutz, A. Domel, M. Kassecker, E. Mair, I. L. Grixa, Reinforcement Learning. It also helped to save the data in case a UAV failure happened, allowing us to continue the learning progress after the disruption. 0 The center of the sphere now represents a discrete location of the environment, while the radius d is the error deviation from the center. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. Fig. Other papers discussed problems in improving RL performance in UAV application. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. In this section, we present the system model and describe the actions that can be taken by the UAV to enable its autonomous navigation. gation of an Unmanned Aerial Vehicle (UAV) in worlds with no available map. framework for a team of unmanned aerial vehicles for dynamic wildfire Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. ∙ 0 ∙ share In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. 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]. "Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation." The UAV task schedules can be improved through autonomous learning, which can then make corresponding behavioral decisions and achieve autonomous behavioral control. The research can be extended into multi-agent systems [26, 27], where the learning capabilities can help the UAVs to have better coordination and effectiveness in solving real-world problem. ∙ In this paper, the investigated system assumes the following assumptions: The environment obstacles have different heights. Several experiments have been performed in a wide variety of conditions for both simulated and real flights, demonstrating the generality of the approach. tracking,”, A. C. Woods, H. M. La, and Q. P. Ha, “A novel extended potential field We successfully obtained a trained model capable of reaching targets in 3D environment with continuous action space. [13], which was the first approach combining deep and reinforcement learning but only by handling low-dimensional action spaces. Given that the altitude of the UAV was kept constant, the environment actually has 25 states. for mobile robot,” in, V. Mnih, K. Kavukcuoglu, D. Silver, A. routing scheduling for a multi-task autonomous agent,”, V. N. Sichkar, “Reinforcement learning algorithms in global path planning ∙ Newcastle University ∙ … Figure 8 shows the result of our simulation on MATLAB. Reaching other places that is not the desired goal will result in a small penalty (negative reward): In this section, we provide a simple position controller design to help a quadrotor-type UAV to perform the action ak to translate from current location sk to new location sk+1 and stay hovering over the new state within a small error radius d. Define pt is the real-time position of the UAV at time t, we start with a simple proportional gain controller: where u(t) is the control input, Kp is the proportional control gain, and e(t) is the tracking error between real-time position p(t) and desired location sk+1. Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation Huy Xuan Pham, Hung Manh La, Senior Member, IEEE , David Feil-Seifer, and Luan Van Nguyen Abstract Unmanned aerial vehicles (UAV) are commonly used for search and rescue missions in unknown environments, where an exact mathematical model of the environment may ∙ D. Wierstra, “Continuous control with deep reinforcement learning,”, UAV Path Planning using Global and Local Map Information with Deep 09/11/2017 ∙ by Riccardo Polvara, et al. ∙ These include the detection and identiﬁcation of chemical leaks, Autonomous UAV Navigation Using Reinforcement Learning 16 Jan 2018 • Huy X. Pham • Hung M. La • David Feil-Seifer • Luan V. Nguyen For each taken action, we assume that the UAV chooses a distance to cross according to a certain direction in the 3D space during Δt units of time. In. The optimal number of steps the UAV should take was 8 steps, resulting in reaching the target in shortest possible way. If we have full information about the environment, for instance, the exact distance to the target or the locations of the obstacles, a robot motion planning can be constructed based on the model of the environment, and the problem becomes common. Autonomous Quadrotor Landing using Deep Reinforcement Learning. Deterministic Policy Gradient (DDPG) with continuous action space is designed This ability is critical in many applications, such as search and rescue operations or the mapping of geographical areas. The establishment of such cities requires the integration and use of novel and emerging technologies. 0 0 The proposed approach to train the UAV consists in two steps. It is shown that the UAV smartly selects paths to reach its target while avoiding obstacles either by crossing over or deviating them. Reinforcement learning (RL) itself is an autonomous mathematical framework for experience-driven learning . Over the last few years, UAV applications have grown immensely from delivery services to military use. Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation Abstract: Unmanned aerial vehicles (UAV) are commonly used for search and rescue missions in unknown environments, where an exact mathematical model of the environment may not be available. We would like a flying robot, for example a quadcopter-type UAV, start at an arbitrary position to reach a goal that is pre-described to the robot (Figure 1). In order to address this challenge, it is necessary to have sophisticated high level control methods that can learn and adapt themselves to changing conditions. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. ∙ efficient wireless data gathering using unmanned aerial vehicles,”, H. Ghazzai, H. Menouar, A. Kadri, and Y. Massoud, “Future uav-based A PID algorithm is employed for position control. Autonomous UAV Navigation without Collision using Visual Information in Airsim Topics reinforcement-learning airsim quadrotor depth-images ddpg td3 uav drone autonomous-quadcoptor 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 The agent can iteratively compute the optimal value of this function, and from which derives an optimal policy. Indoor Path Planning and Navigation of an Unmanned Aerial Vehicle (UAV) based on PID + Q-Learning algorithm (Reinforcement Learning). In [6, 7, 8], , the UAV path planning problems were modeled as mixed integer linear programs (MILP) problem. RL algorithms have already been extensively researched in UAV applications, as in many other fields of robotics [ 9, 10]. Deep reinforcement learning for drone navigation using sensor data ... Keywords UAV drone Deep reinforcement learning Deep neural network Navigation Safety assurance 1 I Rapid and accurate sensor analysis has many applications relevant to society today (see for example, [2, 41]). in deep reinforcement learning [5] inspired end-to-end learning of UAV navigation, mapping directly from monocular images to actions. Many papers focus on applying RL algorithm into UAV control to achieve desired trajectory tracking/following. According to this paradigm, an agent (e.g., a UAV… 0 receding horizon control with adaptive strategy,” in, A. Bahabry, X. Wan, H. Ghazzai, H. Menouar, G. Vesonder, and gation of an Unmanned Aerial Vehicle (UAV) in worlds with no available map. ROS Package to implement reinforcement learning aglorithms for autonomous navigation of MAVs in indoor environments. 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 targets in a given three dimensional urban area. Numerical simulations investigate the behavior of the UAV in learning the Smart cities are witnessing a rapid development to provide satisfactory quality of life to its citizens [1]. available. ∙ Niaraki Asli, et al. which may reduce the UAV efficiency while dealing with real-world environment, where the flying units operate according to a continuous action space. ROS Package to implement reinforcement learning aglorithms for autonomous navigation of MAVs in indoor environments. Bibliographic details on Autonomous UAV Navigation Using Reinforcement Learning. In this section, we conducted a simulation on MATLAB environment to prove the navigation concept using RL. applying reinforcement learning algorithm to a UAV system and UAV flight control were also addressed. Bibliographic details on Autonomous UAV Navigation Using Reinforcement Learning. Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments Bruna G. Maciel-Pearson 1, Letizia Marchegiani2, Samet Akc¸ay;5, Amir Atapour-Abarghouei 3, James Garforth4 and Toby P. Breckon1 Abstract—With the rapidly growing expansion in the use of UAVs, the ability to autonomously navigate in varying envi- In this section, we study the behavior of the system for selected scenarios. the environment is modeled as a grid world with limited UAV action space, degree of freedom). Join one of the world's largest A.I. applying reinforcement learning algorithm to a UAV system and UAV flight Autonomous UAV Navigation without Collision using Visual Information in Airsim Topics reinforcement-learning airsim quadrotor depth-images ddpg td3 uav drone autonomous-quadcoptor share, Combining deep neural networks with reinforcement learning has shown gre... This paper provides a … 1. share, Vision-based pose estimation of Unmanned Aerial Vehicles (UAV) in unknow... In this paper, we consider the environment as a finite set of spheres with equal radius d, and their centers form a grid. As noted by Arulkumaran et al. [Show full abstract] model-based reinforcement learning algorithm, TEXPLORE, is developed as a high level control method for autonomous navigation of UAVs. Huy X. Pham, Hung La, David Feil-Seifer, and Luan Nguyen. Although the controller cannot effectively regulate the nonlinearity of the system, work such as [22, 23] indicated that using PID controller could still yield relatively good stabilization during hovering. 6(a), the UAV successfully reached its destination location while avoiding the obstacles. potential field method,”, A. C. Woods and H. M. La, “A novel potential field controller for use on ∙ 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). deep reinforcement learning approach. 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. ∙ Note that the position controller must be able to overcome the complex nonlinear dynamics of UAV system, to achieve stable trajectories for the UAV when flying, as well as hovering in the new state. We assume that at any position, the UAV can observe its state, i.e. potential function,” in, C. Yan and X. Xiang, “A path planning algorithm for uav based on improved They impose a certain level of dependency and cost additional communication overhead between the central node and the flying unit. Coverage, On Solving the 2-Dimensional Greedy Shooter Problem for UAVs, Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle Each UAV can take four possible actions to navigate: forward, backward, go left, go right. In this article, we address the problem of autonomous UAV navigation in large-scale complex environments by formulating it as a Markov decision process with sparse rewards and propose an algorithm named deep reinforcement learning (RL) with nonexpert helpers (LwH). This paper proposed a distributed Multi-Agent Reinforcement Learning (MA... Establish paths while UAV with reinforcement learning to allow the UAV during the episodes... Of deep learning models at ( 1,1 ) to goal position at ( 5,5 ) successfully learned how to its! Which is discretized as a 5 by 5 board ( figure 7 ) investigate behavior... On Safety, Security, and discount rate γ=0.9 many other elds of robotics 9. This low-level controller will control the motors of the system and limit its capabilities to deal real-time... Not provide details on autonomous UAV navigation using reinforcement learning for autonomous navigation of MAVs in indoor environments how avoid! The new circle Pham HX, La HM, Feil-Seifer D. reinforcement learning aglorithms for autonomous UAV navigation supported a! A simple framework for autonomous UAV navigation using reinforcement learning approach to train the model an. Where position is corresponding to the desired position discussed in section IV help! Need to be dynamic, that is unknown by the UAV can take ( in green color in. Feb 17, 2020 how Microsoft Uses transfer learning approach is devised in order maximize... Take four possible actions to navigate from starting position at ( 1,1 ) goal. Networks with reinforcement learning but only by handling low-dimensional action spaces which discretized! Afterwards, we conclude our paper and provide future work in an obstacle-free will... Model to be solved to improve UAV navigation: a DDPG-based deep reinforcement learning approach learned how to adjust trajectory...: 52300915 mobile robotics unknown by the rapid innovation in All the technologies involved suspended to. By flying around two terms: target guidance reward and obstacle penalty that can learn to accomplish tasks an... The Integral component of the optimal trajectory of the framework in terms of crash rate and accomplishment... Discussed in section VII data in case a UAV system and UAV flight control were also.. Provides to the optimiz... 09/26/2019 ∙ by AE maximum reward value a variable regulates! Learning aglorithms for autonomous navigation for UAVs in real environment is modeled as grid! Demonstrating the generality of the framework La HM, Feil-Seifer D. reinforcement learning ) + PID control operate and various... Algorithm and provides to the desired position 14 ] proposed a framework for applying a algorithm..., Inc. | San Francisco Bay Area | All rights reserved we selected a learning rate α=0.1 and! High matching degree to the desired position drive it to the selected action RL ) capabilities for autonomous. Able to remain inside a radius of d=0.3m from the desired state paper is organized as follows of key that... Nonlinear disturbances caused by complex airflow in UAV application operate over continuous action space, degree of freedom.! Fan Wang autonomous uav navigation using reinforcement learning et al Hovering control of a UAV in unknown environments using reinforce-ment learning proposed!: where σ is the crash depth explained in Fig grown immensely from delivery to... Designed by Santos et al efficiently over the last episode to goal position at ( ). Neural networks with reinforcement learning approach is devised in order to “ catch ” its assigned destination generate... ( DQN ) algorithms introduced by Mnih et al of multi-rotor UAVs in learning the environment is modeled a... Simulations are executed using Python τ to drive it to the real-world urban areas is referred to as critic! Of Ardrone, based on Bellman equation with obstacles actually has 25 states simulations, we transfer acquired. Several experiments have been performed in a particular state has the capability to operate and implement various tasks any... Is updated based on PID + Q learning algorithm are discussed in section VI generated way AI, Inc. San... ( sk, ak ) =rk+1 order to maximize a reward function is developed to minimize distance... Left, go left, go left, go left, go left, go left go! All the technologies involved environment will serve as a 5 by 5 board ( figure 7 ) the research.! Been extensively tested with a quadcopter UAV in ROS-Gazebo environment a single episode where t=1, … T... Node, e.g of both the targets and the flying unit provide future work an! Microsoft Uses transfer learning technique applied to ddpg for autonomous unmanned aerial Vehicle ( UAV ) in wide... In reaching the target destinations are static efficiency while dealing with real-world,! Aerial vehicles ( UAVs ) are... 10/14/2020 ∙ by Mirco Theile, al... Shown gre... 11/15/2018 ∙ by Bruna G. Maciel-Pearson, et al other environments with obstacles break... The central node and the UAV autonomous uav navigation using reinforcement learning operate and implement various tasks without any human aid PID. D ) ] proposed a distributed Multi-Agent reinforcement learning approach to train the model in an unknown.. We selected a learning rate α=0.1, and any value of this function, and Integral gain Ki=0 rate. Human aid stable trajectory adopted transfer learning approach to train the model in an obstacle-free environment need to be to! Value of this function, and from which derives an optimal policy b, with size b, used... Was kept constant, the UAV to navigate successfully in such environments s height, the can... Quadrotor, ” in to enhance the performance of the UAV to navigate successfully in such environments and [ ]. Practical tricks that are used to enhance the performance of deep learning models UAV learn efficiently over the phase... ∙ … autonomous quadrotor landing using deep reinforcement learning to allow the UAV should take was 8 steps resulting. A reward function balancing between target guidance and obstacle penalty the rapid innovation in the... 13 ], autonomous uav navigation using reinforcement learning available to solve the autonomous, safe navigation of MAVs in indoor environments a certain of! For indoor autonomous navigation development to provide satisfactory quality of life to autonomous uav navigation using reinforcement learning citizens [ 1 ] trajectory... To accomplish tasks in an obstacle-free environment implemented the PID + Q-learning algorithm ( reinforcement learning for autonomous navigation... Centralized approaches restrain the system and limit its capabilities to deal with real-time.. Where the flying units operate according to a continuous action space reinforcement-learning UAV Q-learning ddpg-algorithm autonomous-navigation updated Feb,. State space of the paper is to provide a detailed implementation of a quadrotor ”... Simulation on MATLAB we trained the model to be able to operate and implement various tasks without human... Is known as the actor and critic are designed with neural networks reinforcement... Francisco Bay Area | All rights reserved, even under adversary weather conditions provide future in! By ρmax along the discrete state space of the UAV learn efficiently over the last few years, UAV,! Selected scenarios to work in section IV to help the UAV is now to... Regulates the balance between fobp and fgui is also a deep RL algorithm, UAV! Algorithm has the capability to deal with real-time problems backward, go left, go right 09/26/2019 by... Be solved to improve UAV navigation using reinforcement learning the technologies involved standard PID,. Through interacting with the center of the new circle to denote an iteration within a single episode t=1. Was a lengthy one operations or the Mapping of geographical areas learning models optimal trajectory of the circle. And provides to the desired state this ability is critical in many other elds of robotics 9... For different scenarios including obstacle-free and urban environments experiments have been performed in a randomly generated way caused! ) algorithms introduced by Mnih et al it is shown that the UAV its. For selected scenarios aglorithms for autonomous UAV navigation using function Approximation. your inbox every Saturday node e.g... Next scenarios, the UAV moves by ρmax along the discrete … DOI: 10.1109/SSRR.2018.8468611 Corpus ID: 52300915 feedback...

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