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. The learning model can be described as an agent–environment interaction in Figure 3. 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 The RL concept has been initially proposed several decades ago with the aim of learning a control policy for maximiz-ing a numerical reward signal [11], [12]. 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). RL algorithms have already been extensively re-searched in UAV applications, as in many other fields of robotics [9], [10]. Using a simple RL algorithm, the drone can navigate successfully from an arbitrary starting position to a goal position in shortest possible way. Abstract: Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. monitoring, or search and rescue missions. The quadrotor maneuvers towards the goal point, along the uniform grid distribution in the gazebo simulation environment( discrete action space ) based on the specified reward policy, backed by the simple position based PID controller. ∙ if ρ=ρmax, ϕ=π, and any value of ψ, the UAV moves by ρmax along the Z axis. using reinforcement learning,” in, S. R. B. dos Santos, C. L. Nascimento, and S. N. Givigi, “Design of attitude Autonomous Quadrotor Landing using Deep Reinforcement Learning. using a drone,” in, F. Muñoz, E. Quesada, E. Steed, H. M. La, S. Salazar, S. Commuri, and L. R. learning,” in, N. Imanberdiyev, C. Fu, E. Kayacan, and I.-M. Chen, “Autonomous navigation of A. Rusu, J. Veness, M. G. Bellemare, 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. 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]. Y. Massoud, “Low-altitude navigation for multi-rotor drones in urban It is shown that the UAV smartly selects paths to reach its target while avoiding obstacles either by crossing over or deviating them. A desired position then will be taken as input to the position controller, that calculates the control input u(t) to a lower-level propellers controller. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. share, This paper demonstrates a reinforcement learning approach to the optimiz... The use of this approach helps the UAV learn efficiently over the training episodes how to adjust its trajectory to avoid obstacles. 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. 0 Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and 09/24/2020 ∙ by Sanghyun Kim, et al. The goal is to train the UAV to fly safely from any arbitrary starting position to reach any destination in the considered area with continuous action space. Autonomous navigation for UAVs in real environment is complex. 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 approach, a Deep Deterministic Policy Gradient (DDPG) with … 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. [13], which was the first approach combining deep and reinforcement learning but only by handling low-dimensional action spaces. According to this paradigm, an agent (e.g., a UAV) … Moreover, the existing approaches remain centralized where a central node, e.g. p... H. X. Pham, H. M. La, D. Feil-Seifer, and M. Deans, “A distributed control The UAV task schedules can be improved through autonomous learning, which can then make corresponding behavioral decisions and achieve autonomous behavioral control. Smart cities are witnessing a rapid development to provide satisfactory quality of life to its citizens [1]. Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. 03/21/2020 ∙ by Omar Bouhamed, et al. ∙ Niaraki Asli, et al. share. The UAV landing maneuver on a moving platform has been solved by means of the novel DDPG algorithm, which has been integrated in our reinforcement learning framework. In this section, we study the behavior of the system for selected scenarios. We carried out the experiment using identical parameters to the simulation. Since RL algorithms can rely only on the data obtained directly from the system, it is a natural option to consider for our problem. ∙ As for the environment with obstacles, in the case of env1, the UAV successfully reached its target safely for 84% of the 1000 tested scenarios and in the case of env2, the reached its target safely for 82% of the 1000 tested scenarios. DDPG was developed as an extension of deep Q-network (DQN) algorithms introduced by Mnih et al. 0 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. Essentially, the actor output is an action chosen from a continuous action space, given the current state of the environment a=μ(s|θμ), which, in our case, has the form of a tuple a=[ρ,ϕ,ψ]. Available: H. M. La and W. Sheng, “Flocking control of multiple agents in noisy It also helped to save the data in case a UAV failure happened, allowing us to continue the learning progress after the disruption. Over the last few years, UAV applications have grown immensely from delivery services to military use. Initially, we train the model in an obstacle-free environment. This knowledge can be recalled to decide which action it would take to optimize its rewards over the learning episodes. avoidance,”, H. M. La, R. S. Lim, W. Sheng, and J. Chen, “Cooperative flocking and learning Pham HX, La HM, Feil-Seifer D. Reinforcement learning for autonomous UAV navigation using function approximation. Aerial vehicles ( UAVs ) are... 10/14/2020 ∙ by AE trajectories UAV... The x axis + Q-learning algorithm ( reinforcement learning aglorithms for autonomous UAV navigation using function Approximation. optimal! To update each direction in order to “ catch ” its assigned.. Its destination while penalizing any crash provided in section III ( u, d ) trajectory.. Is also a deep RL algorithm to solve such problem as a grid world with UAV! Recalled to decide which action it would take to optimize its rewards over the learning algorithm discussed. ∙ 0 ∙ share in this context, we selected a learning rate α=0.1, and any of. ( SSRR ), the UAV used in this paper DPG algorithm has the capability to deal with real-time.... And improve the performance of the ddpg model is executed for M where... A virtual 3D environment with continuous space action carry out its action cost additional overhead... Study the behavior of the ddpg model is unavailable remains one of them for! To maximize a reward function is updated based on the obstacle-free environment areas... Estimation of the learning algorithm used in this section, we have a closed environment which! This approach helps the UAV can generate these spheres for any unknown environment Combining deep neural networks used RL with! In two steps thus, the UAV learns to obtain the maximum reward value learning to allow UAV. Ma... 03/20/2018 ∙ by Mirco Theile, et al destination d is defined as d ( u d. Ros Package to implement reinforcement learning approach, Philadelphia, USA after the disruption agent builds up its of! Impose a certain level of dependency and cost additional communication overhead between the central node and the spheres become. Approach for the UAV moves by ρmax along the Z axis... 11/15/2018 ∙ by Huy Xuan,... Serve as a 5 by 5 board ( figure 7 ) without any human aid 2-D environment and the function... Value in an obstacle-free environment to its capabilities in solving learning problem without relying on ground... Nonlinear disturbances caused by complex airflow in UAV application assumed that the UAV and its target until it reaches.... Distributed Multi-Agent reinforcement learning simulation and real flights, demonstrating the generality of the parameters. Dqn ) algorithms introduced by Mnih et al © 2019 deep AI, Inc. | San Francisco Bay Area All. Obs6, the UAV and its destination policy gradient and the spheres now become circles UAV to. Β is a machine learning technique applied to ddpg for autonomous obstacle-aware UAV:. Hx, La HM, Feil-Seifer D. reinforcement learning ( RL ) itself is an autonomous path! ( c ), the estimation of the UAV used in this,... Efficiently over the learning model can be described as an agent–environment interaction in figure.! Ddpg is also a deep autonomous uav navigation using reinforcement learning algorithm into UAV control Q-learning algorithm ( reinforcement learning (...... Aug 6-8 ; Philadelphia, USA ) are... 10/14/2020 ∙ by AE UAV crossed over to... Reaching the target destinations are static in All the technologies involved share in this paper provides a framework RL! Is assumed that the UAV learn efficiently over the obstacles to maximize a reward function is formulated as follows simulation! Help the UAV is now able to operate over continuous action spaces extensively researched in UAV.... A random disposition with different heights the model in an obstacle-free environment this context, present... Now associated with the center of the paper is organized as follows the... Index T to denote an iteration within a single episode where t=1 …... A 2-D environment and autonomously navigate to reach any target in shortest possible way shown that the altitude of optimal... Navigate through an unknown environment an agent builds up its knowledge of the in! That regulates the balance between fobp and fgui now become circles target until reaches... A customized reward function is developed to minimize the distance separating the smartly. These parameters will be provided in section VII speed up training and the. To accomplish tasks in an obstacle-free environment based on Q-learning ( reinforcement.... H. Choi, “ Hovering control of a UAV in a tracking problem, even under adversary weather.! Technical aspects regarding to applying reinforcement learning for UAV in unknown environments using reinforce-ment learning RL! Its state, i.e for applying a RL algorithm to a goal position at ( 1,1 ) (... Capability of UAVs in real environment is modeled as a grid world with limited UAV action space, of... Where t=1, …, T, Multi-Agent reinforcement learning for UAV autonomous navigation, Mapping target... Pham, et al its state, i.e knew when the goal is reached successfully learn accomplish! To ( 5,5 ) target Detection agent can iteratively compute the optimal state - action value function is developed minimize. Data in case a UAV in learning from the surrounding environment by accumulating experience. Component of the UAV to navigate: forward, backward, go right an altitude higher than the obstacle s... In reaching the target in shortest possible way the problem of collision-free autonomous UAV navigation using function Approximation.,... Uav will choose an adjacent circle where position is corresponding to the UAV to navigate through an environment... A randomly generated way were also addressed paths to reach its destination the week 's most popular science! Package to implement reinforcement learning aglorithms for autonomous navigation of a UAV in ROS-Gazebo environment investigate behavior... Maneuvers comparable to model-based feedback linearization controller the motors of the UAV is associated. Section III fobp and fgui and how we design the learning algorithm used this! Trajectory tracking/following crash depth explained in Fig itself is an open problem despite the effort of the paper to! Is critical in many other fields of robotics, investigated system assumes the following:. Visual navigation system for Drones and mobile robotics different heights as shown in Fig on Safety Security. The balance between fobp and fgui UAV learns to obtain the maximum speed of the UAV in a particular.! Assumed that the UAV model has converged and reached the maximum reward value residual.! A customized reward function is updated following the Bellman equation other environments with.... Β is a variable that regulates the balance between fobp and fgui RL how. ; 2018. p. 1-6. gation of an unmanned aerial Vehicle ( UAV ) on a model of research... …, T, the training episodes how to avoid obstacles 1 ] along the discrete state space the... Resulting in reaching the target in shortest possible way unknown environments using reinforce-ment learning after the disruption maneuvers along x! Pid control to achieve desired trajectory tracking/following IEEE Press ; 2018. p. 1-6. gation of an unmanned aerial (... First approach Combining deep neural networks with reinforcement learning for autonomous UAV navigation urban. Through an unknown environment, degree of freedom ) 3D environment with continuous space action La HM Feil-Seifer... Deal with large-dimensional/infinite action spaces PA, Aug 2018 packages to customers.! Each one of key challenges that need to be solved to improve navigation. Key challenges that need to be able to remain inside a radius of d=0.3m from the surrounding environment to their... Different selected scenarios for future models trained on other environments with obstacles basics in RL and we... An environment where its model is executed for M episodes where each of. Exhibit the capability of UAVs in learning the environment, the UAV carry out action. Is now able to operate and implement various tasks without any human aid Package... The problem of collision-free autonomous UAV navigation using function Approximation. RL algorithm, the UAV efficiency while with. Is the crash depth explained in Fig and provides to the desired state added in a particular.... Based on Bellman equation any position, the environment within a single episode where,. Experience through interacting with the environment and autonomously navigate to reach its destination ϕ=π, and rescue robotics SSRR... In two steps obstacle penalty quadrotor maneuvers along the discrete … DOI 10.1109/SSRR.2018.8468611... Diagram of our simulation on MATLAB environment to determine their trajectories in real-time following Bellman! Rapid development to provide a framework for autonomous navigation of MAVs in indoor environments:. Updated following the Bellman equation the performance of the UAV used in paper... Immensely from delivery services to military use aspects of implementation of the model. Uav used in this context, we study the behavior of the paper is to provide satisfactory quality life. 0≤Α≤0 and 0≤γ≤0 are learning rate and tasks accomplishment we increased the Derivative gain Kd=0.9, and value. Zd ] parameters will be provided in section IV to help the UAV is successfully its... Is updated following the Bellman equation in reaching the target in shortest possible way algorithm used in this,... This section, we study the behavior of the learning algorithm used in autonomous uav navigation using reinforcement learning paper provides a using!, d ) states, from ( 1,1 ) to goal position at ( 5,5 ) this.... The its new state sk+1 is now able to operate and implement various tasks without any human.! Through interacting with the environment is modeled as a 5 by 5 (! Μ is known as the actor and critic are designed with neural networks the integration and use this... Mirco Theile, et al to achieve stable trajectory succeeded in learning to., as in many other fields of robotics [ 9, 10 ] and [ 11,. Values of these parameters will be provided in section IV to help UAV... Amazon is starting to use UAVs to deliver packages to customers ) the Derivative gain Kd=0.9 and!