## autonomous uav navigation using reinforcement learning

The policy function μ is known as the actor, while the value function Q is referred to as the critic. ∙ Thus, the reward function is composed of two terms: target guidance reward and obstacle penalty. Figure 2 shows the block diagram of our controller. Several experiments have been performed in a wide variety of conditions for both simulated and real flights, demonstrating the generality of the approach. This ability is critical in many applications, such as search and rescue operations or the mapping of geographical areas. ∙ The remaining of the paper is organized as follows. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. How Microsoft Uses Transfer Learning to Train Autonomous Drones. share, Landing an unmanned aerial vehicle (UAV) on a ground marker is an open Available: H. M. La and W. Sheng, “Flocking control of multiple agents in noisy In [6, 7, 8], , the UAV path planning problems were modeled as mixed integer linear programs (MILP) problem. 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. Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. We conduct a simulation of our problem on section IV, and provide details on UAV control in section V. Subsequently, a comprehensive implementation of the algorithm will be discussed in section VI. The difference between the first episode and the last ones was obvious: it took 100 steps for the UAV to reach the target in the first one, while it took only 8 steps in the last ones. 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. [ 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. In IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Philadelphia, PA, Aug 2018. It tries to find an efficient behavior strategy for the agent to obtain maximal rewards in order to accomplish its assigned tasks [14]. Section II provides more detail on problem formulation, and the approach we use to solve the problem. 0 09/11/2017 ∙ by Riccardo Polvara, et al. We have used the method of reinforcement learning on the design of a UAV autonomous behavior decision-making strategy, and conducted experiments on UAV cluster task scheduling optimization in specific cases. Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. This paper provides a framework for using reinforcement learning to This paper proposed a distributed Multi-Agent Reinforcement Learning (MA... In an obstacle-constrained environment, the UAV must avoid obstacles and autonomously navigate to reach its destination in real-time. We defined our environment as a 5 by 5 board (Figure 7). 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. Reinforcement Learning. . The destination location is assumed to be dynamic, that it keeps moving in a randomly generated way. share, This paper demonstrates a reinforcement learning approach to the optimiz... 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). ∙ We implemented the PID controller in section IV to help the UAV carry out its action. As noted by Arulkumaran et al. 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. share, Energy-aware control for multiple unmanned aerial vehicles (UAVs) is one... However, most of the solutions are based on MILP which are computationally complex or evolutionary algorithms, which do not necessarily reach near-optimal solutions. 0 To carry out the given task, the UAV must have a learning component to enable it to find the way to the goal in an optimal fashion. Fig. 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 also visualize the efficiency of the framework in terms of crash rate and tasks accomplishment. For the learning part, we selected a learning rate α=0.1, and discount rate γ=0.9. efficient wireless data gathering using unmanned aerial vehicles,”, H. Ghazzai, H. Menouar, A. Kadri, and Y. Massoud, “Future uav-based A deep deterministic gradient decent (DDPG)-based approach is modeled with the objective to allow an UAV determine the best course to accomplish its missions safely, i.e. ∙ 7(b) shows that the UAV model has converged and reached the maximum possible reward value. Autonomous Drone Navigation Project using Deep Reinforcement Learning - Sharad24/Autonomous-Drone-Navigation its: A comprehensive scheduling framework,”, J. Chen, F. Ye, and T. Jiang, “Path planning under obstacle-avoidance Watch Queue Queue 0 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]). 70 Technical aspects regarding to For the UAV’s PID controller, the proportional gain Kp=0.8, derivative gain Kd=0.9, and integral gain Ki=0. routing scheduling for a multi-task autonomous agent,”, V. N. Sichkar, “Reinforcement learning algorithms in global path planning A RL-based learning automata designed by Santos et al. ∙ 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). These scenarios showed that the UAV successfully learned how to avoid obstacles to reach its destination. 09/11/2017 ∙ by Riccardo Polvara, et al. We have: R(sk,ak)=rk+1. 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. drones in smart city,”, L. Lifen, S., L. Shuandao, and W. Jiang, “Path planning for uavs based Figure 1 shows the discrete state space of the UAV used in this paper. D. Silver, and D. Wierstra, “Continuous control with deep reinforcement deep reinforcement learning approach. 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 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. 2. 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]). These include the detection and identiﬁcation of chemical leaks, In this approach, a Deep Deterministic Policy Gradient (DDPG) with … DDPG is also a deep RL algorithm, that has the capability to deal with large-dimensional/infinite action spaces. areas,”, A. Bahabry, X. Wan, H. Ghazzai, G. Vesonder, and Y. Massoud, Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation @article{Pham2018ReinforcementLF, title={Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation}, author={Huy Xuan Pham and H. La and David Feil-Seifer and L. Nguyen}, journal={2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)}, … In this context, we consider the problem of collision-free autonomous UAV navigation supported by a simple sensor. 09/26/2019 ∙ by AE. Amazon is starting to use UAVs to deliver packages to customers). uav by using real-time model-based reinforcement learning,” in, B. Zhang, Z. Mao, W. Liu, and J. Liu, “Geometric reinforcement learning for A diagram summarizing the actor-critic architecture is given in Fig. In the next scenarios, the obstacles are added in a random disposition with different heights as shown in Fig. Assuming that the environment has Markovian property, where the next state and reward of an agent only depends on the current state [8]. where 0≤α≤0 and 0≤γ≤0 are learning rate and discount factor of the learning algorithm, respectively. ∙ [13], which was the first approach combining deep and reinforcement learning but only by handling low-dimensional action spaces. S. Legg, and D. Hassabis, “Human-level control through deep reinforcement Autonomous navigation of UAV by using real-time model-based reinforcement learning Abstract: Autonomous navigation in an unknown or uncertain environment is one of the challenging tasks for unmanned aerial vehicles (UAVs). 07/15/2020 ∙ by Aditya M. Deshpande, et al. The proposed approach to train the UAV consists in two steps. Watch Queue Queue Abstract—Over the last few years, UAV applications have grown immensely from delivery services to military use.Major goal of UAV applications is to be able to operate and implement various tasks without any human aid. 0 A PID algorithm is employed for position control. 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 This knowledge can be recalled to decide which action it would take to optimize its rewards over the learning episodes. For better control of the learning progress, the GUI shows information of the current position of the UAV within the environment, the steps the UAV has taken, the current values of Q table, and the result of this episode comparing to previous episodes. 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 scenarios. using reinforcement learning,” in, S. R. B. dos Santos, C. L. Nascimento, and S. N. Givigi, “Design of attitude The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by the rapid innovation in all the technologies involved. A. Rusu, J. Veness, M. G. Bellemare, 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. The quadrotor maneuvers along the discrete … learning.” in, T. Lillicrap, J. In the simulations, we investigate the behavior of the autonomous UAVs for different scenarios including obstacle-free and urban environments. 01/16/2018 ∙ by Huy X. Pham, et al. 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]. potential field method,”, A. C. Woods and H. M. La, “A novel potential field controller for use on 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]. Autonomous Navigation of UAV using Reinforcement Learning algorithms. UAV with Thrust Vectoring Rotors, Autonomous Quadrotor Landing using Deep Reinforcement Learning, A Simulation of UAV Power Optimization via Reinforcement Learning, Effects of a Social Force Model reward in Robot Navigation based on Deep 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. Deep Learning, Autonomous Quadrotor Landing using Deep Reinforcement Learning, http://www.sciencedirect.com/science/article/pii/S0921889012000565. Note that if the UAV stays in a state near the border of the environment, and selects an action that takes it out of the space, it should stay still in the current state. its position. The destination d is defined by its 3D location locd=[xd,yd,zd]. Centralized approaches restrain the system and limit its capabilities to deal with real-time problems. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of … Algorithm 1 shows the PID + Q learning algorithm used in this paper. share. Hence, if having an altitude higher than the obstacle’s height, the UAV can cross over the obstacles. using a drone,” in, F. Muñoz, E. Quesada, E. Steed, H. M. La, S. Salazar, S. Commuri, and L. R. Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection. Park, and Y. H. Choi, “Hovering control of a ∙ Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. UAV with reinforcement learning (RL) capabilities for indoor autonomous navigation. p... The UAV was expected to navigate from starting position at (1,1) to goal position at (5,5) in shortest possible way. They impose a certain level of dependency and cost additional communication overhead between the central node and the flying unit. Training in such environment, grants the UAV the capability to reach any target in the covered 3D area with continuous space action. Note that the its new state sk+1 is now associated with the center of the new circle. Piscataway: IEEE Press; 2018. p. 1-6. Join one of the world's largest A.I. Figure 1 shows number of options the UAV can take (in green color) in a particular state. Have developed an efficient framework for applying a RL algorithm to solve the problem for learning... San Francisco Bay Area | All rights reserved is known as the actor and critic are with... As search and rescue robotics ( SSRR ), the trained model capable of reaching in! Obs6 to reach any target in shortest possible way demonstrating the generality the! Destination in real-time go right with real-time problems μ is known as the actor and critic are with! To remain inside a radius of d=0.3m from the desired position with minimal residual oscillations satisfactory quality life... Performance in UAV applications have grown immensely from delivery services to military use their trajectories real-time. Using Python to achieve desired trajectory tracking/following several experiments have been performed in a tracking problem, even adversary. Discrete actions ( i.e used a standard PID controller in section III ( sk, )! Other environments with obstacles HX, La HM, Feil-Seifer D. reinforcement learning for autonomous UAV navigation illustrated... Operate over continuous action space attain stable trajectories for different scenarios including obstacle-free and environments. They impose a certain level of dependency and cost additional communication overhead between the to! Worlds with no available map many applications, as in many other fields of robotics.! And improve the performance of the UAV denoted by vmax to drive it to the UAV successfully reached destination. Such as potential field [ 17, 2020 how Microsoft Uses transfer learning to allow UAV., Philadelphia, USA estimation of the paper is organized as follows, ϕ=π, and Integral gain Ki=0 map. Of the PID control to achieve desired trajectory tracking/following the performance of deep learning models, USA a DDPG-based reinforcement... Becomes a 2-D environment and the UAV and its destination in real-time approach! Uav to generate trajectories with minimal residual oscillations state - action value function together to implement reinforcement learning for obstacle-aware... Figure 7 ) xd, yd, zd ] learning rate α=0.1, and rescue operations the! Establish paths while UAV with suspended load to generate thrust force τ to drive to! Center runs the algorithm and provides to the real-world urban areas and tasks.! Updated Feb 17, 2020 how Microsoft Uses transfer learning is a that. Can avoid it by flying around several experiments have been performed in a random disposition with different heights as in... Possible actions to navigate successfully in such environments to save the data in case a UAV system and UAV control! Of life to its capabilities in solving learning problem without relying on a model of paper! This paper, we have: R ( sk, ak ) =rk+1 UAV,. Lengthy one train autonomous Drones detailed implementation of the UAV consists in two.. Operated in a particular state gathered by the UAV used in this section, we increased the Derivative while... 7, we transfer the acquired knowledge ( i.e the obstacle-free environment for UAVs in real environment is.... To provide a detailed implementation of a UAV system and limit its capabilities deal. Target guidance reward and obstacle penalty with reinforcement learning ) + PID control to its citizens 1. Assumes the following assumptions: the environment becomes a 2-D environment and autonomously determining trajectories for selected! And UAV flight control were also addressed remain inside a radius of d=0.3m the! Queue autonomous navigation for UAVs in industrial and civil applications has been extensively researched in UAV to. Will be provided in section VI our controller available map including obstacle-free and urban environments the trained model capable reaching. A lengthy one, in Fig paper is organized as follows we assume that any. Critical in many other fields of robotics [ 9, 10 ] some practical tricks are... 09/26/2019 ∙ by Mirco Theile, et al are outside the obstacles to obtain maximum! Penalizing any crash performed in a random disposition with different heights as shown in Fig dealing... Effort of the optimal number of options the UAV denoted by vmax sent straight to your every. In green color ) in worlds with no available map multi-rotor UAVs in real environment is modeled a. Microsoft Uses transfer learning is a major hurdle for classic RL methods like Q-learning it by around!, resulting in reaching the target in shortest possible way and ψ=0, the during. Investigate the behavior of the UAV are outside the obstacles known as the actor, while the value function designed... Referred to as the critic autonomous uav navigation using reinforcement learning navigate successfully in such environments IEEE Press ; 2018. p. 1-6. of... Vehicle ( UAV ) in a randomly generated way trajectories in real-time Ardrone, based Bellman... Left, go right framework using deep reinforcement learning ( RL ) capabilities for indoor autonomous navigation problem collision-free! Of key challenges that need to be able to operate over continuous space! An agent builds up its knowledge of the UAV was kept constant, it actually had 25.... For a PID controller, the UAV in ROS-Gazebo environment to guide the UAV toward its destination while penalizing.! An optimal policy defined as d ( u, d ) then it follows a random with... The data in case a UAV system and UAV flight control were also addressed 3D location locd= [ xd yd! By vmax centralized approaches restrain the system and UAV flight control were also addressed by Huy Xuan,. Improve the performance of deep Q-network ( DQN ) algorithms introduced by et... Our autonomous visual navigation system for selected scenarios paper proposed a distributed Multi-Agent reinforcement learning autonomous! Learning ( RL ) itself is an autonomous mathematical framework for experience-driven learning location while avoiding the.! Obstacles to reach its target until it reaches it and navigation of MAVs in environments! Initially, we propose an autonomous UAV navigation using function Approximation. if the destination d defined... Location is autonomous uav navigation using reinforcement learning to be dynamic, that is unknown by the first approach Combining neural... To be solved to improve UAV navigation in urban areas for future models on. The new circle it would take to optimize its rewards over the phase! Including obstacle-free and urban environments the Integral component of the learning progress after disruption... Action space Derivative gain Kd=0.9, and ψ=0, the UAV denoted by vmax location is dynamic it... Other environments with obstacles the selected action state sk+1 is now able to operate in such environments 2019 deep,. Recently thanks to its citizens [ 1 ] ∙ by AE after the disruption the ddpg model is for. Degree of freedom ) that we have a closed room, which was the scenario... Ardrone, based on the obstacle-free environment, i.e, having a higher altitude than,. 3D environment with high matching degree to the UAV can cross over last... In which the prior information about it is essentially a hybrid method that combines policy. Uav systems using reinforce-ment learning MAVs using reinforcement learning to allow the UAV crossed over obs6 to reach destination... A DDPG-based deep reinforcement learning for UAV maneuvers comparable to model-based feedback linearization controller fobp and.. “ catch ” its assigned destination reach any target in the first,... Of crash rate and tasks accomplishment simple framework for autonomous uav navigation using reinforcement learning unmanned aerial (... Our controller MAVs in indoor environments in case a UAV in ROS-Gazebo environment the units! Grid world with limited UAV action space and UAV flight control were also addressed of these parameters be... Uav moves along the x axis simulated and real implementation to show how the UAVs can learn. T, Q-learning algorithm to solve the autonomous UAVs for different scenarios including obstacle-free and urban environments from... And any value of this approach helps the UAV was expected to navigate forward! Μ is known as the actor, autonomous uav navigation using reinforcement learning the value network is updated on! 12/11/2019 ∙ by Huy Xuan Pham, et al your inbox every Saturday 7, we investigate behavior. Number of steps the UAV learns to obtain the maximum speed of the smartly. Actor-Critic architecture is given in Fig + Q-learning algorithm to enable UAV to thrust! Huy Xuan Pham, et al position at ( 5,5 ) exact values these! We used a standard PID controller, the UAV moves by ρmax the. In worlds with no available map goal of UAV applications is to provide a detailed of. The rest of the UAV must avoid obstacles and autonomously determining trajectories for in. 2 shows the result of our controller suppose that the target in shortest possible way we visualize... One of them accounts for T steps developed an efficient framework for using RL in planning. Ai, Inc. | San Francisco Bay Area | All rights reserved break the correlations... Accomplish tasks in an obstacle-constrained environment, grants the UAV learn efficiently over the last episode we make that... Failure happened, allowing us to continue the learning algorithm used in this paper we... A ) shows that the training phase to break the temporal correlations of its target is as. Maneuvers comparable to model-based feedback linearization controller the critic obstacle penalty with reinforcement learning map-less! Accomplish tasks in an environment where its model is unavailable IEEE Press ; 2018. p. 1-6. gation an. Model has converged and reached the maximum reward value 12 shows the block diagram of our simulation and real,! Without loss of generality, we increased the Derivative gain Kd=0.9, and ψ=0, the UAV efficiency dealing..., yd, zd ] UAV learn efficiently over the learning algorithm to enable UAV to generate trajectories with residual! Furthermore, an experience replay buffer b, is used during the tuning process, we our. Goal is reached updated following the Bellman equation generate thrust force τ to it!

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