Unknown Obstacles Research Articles

Online Sliding Mode-Based Integrated Guidance and Control System for a Quadcopter UAV in the Presence of Unknown Obstacles

Online Sliding Mode-Based Integrated Guidance and Control System for a Quadcopter UAV in the Presence of Unknown Obstacles

A Method of Multi-UAV Cooperative Task Assignment Based on Reinforcement Learning

With the increasing complexity of UAV application scenarios, the performance of a single UAV cannot meet the mission requirements. Many complex tasks need the cooperation of multiple UAVs. How to coordinate UAV resources becomes the key to mission completion. In this paper, a task model including multiple UAVs and unknown obstacles is constructed, and the model is transformed into a Markov decision process (MDP). In addition, considering the influence of strategies among UAVs, a multiagent reinforcement learning algorithm based on SAC algorithm and centralized training and decentralized execution framework, MA-SAC (Multi-Agent Soft Actor-Critic), is proposed to solve the MDP. Simulation results show that the algorithm can effectively deal with the task allocation problem of multiple UAVs in this scenario, and its performance is better than other multiagent reinforcement learning algorithms.

Soft formation control for unmanned surface vehicles under environmental disturbance using multi-task reinforcement learning

This paper proposes a distributed soft formation collision avoidance strategy to address the problem of formation obstacle avoidance in complex scenario under the environment interference where unmanned surface vehicles (USVs) are restricted in observation and possess no global reference frame for localisation. A multi-task training framework for formation control is also developed based on the motion characteristics of USVs and the leader–follower method. The soft actor–critic (SAC) reinforcement learning algorithm is adapted to construct agents. With elaborated auxiliary tasks, the soft formation algorithm demonstrates strong resilience to disturbances caused by unknown environmental loads or obstacle avoidance needs with only partial information about the environmental state, thus maintaining the formation shape. The proposed trajectory communication system and policy sharing mechanism allow USVs to approach the destination and avoid collisions whilst maintaining a noncompact formation that can be broken up temporarily to improve obstacle avoidance and can be restored quickly when the obstacle is avoided. Trained agents can be applied to different formations of varying sizes. Simulation results demonstrate the feasibility and effectiveness of the proposed method in different complex sea scenarios and its robustness to unknown environmental disturbances.

A learning method for AUV collision avoidance through deep reinforcement learning

This paper investigates the collision avoidance problem for autonomous underwater vehicle (AUV) with limited detection range via deep reinforcement learning method in an unknown underwater environment. Firstly, to deal with both unknown static and dynamic obstacles, we introduce two different event-triggered mechanism to generate obstacle-related states and reward the safe collision avoidance action of AUV, respectively. Secondly, the complete state space is customized by combining the state of AUV itself and the state related to a target area. In addition, considering the task requirement of reaching a target area with the shortest path, the complete reward function is designed. Then, we propose a novel event-triggered soft actor–critic (ET-SAC) algorithm for AUV collision avoidance, and give its detailed pseudo code and implementation architecture. Finally, training and evaluation are carried out in the simulation platform we designed, including unknown static obstacle environment and unknown dynamic obstacle environment in 2D/3D space, and the results confirm that the proposed algorithm of this paper is feasible and effective for AUV to avoid collision safely.

Mobile robot path planning based on fusion of improved A* algorithm and adaptive DWA algorithm

For mobile robot autonomous navigation, path planning algorithm matters. For the reason that traditional A* algorithm is unable to achieve the global path planning perfectly and avoid unknown obstacles at the same time, the mobile robot algorithm combining improved A* with adaptive DWA was proposed for path planning. Firstly, safe distance judgment is added in A* algorithm to avoid the problem that the path crosses the vertex of obstacle diagonally. Secondly, the dynamic tangential method is used to smooth the path of A* algorithm, which is more consistent with the actual motion curve of mobile robot. Then, the path key points explored by improved A* algorithm are extracted as intermediate target points of the velocity adaptive DWA algorithm. Unknown obstacles avoidance based on the optimal global path planning is realized. Finally, the proposed fusion algorithm’s feasibility is verified through mobile robot ROS simulation.

Learning-Based Multi-Robot Formation Control With Obstacle Avoidance

Multi-robot formation control has been intensively studied in recent years. In practical applications, the multi-robot system’s ability to independently change the formation to avoid collision among the robots or with obstacles is critical. In this study, a multi-robot adaptive formation control framework based on deep reinforcement learning is proposed. The framework consists of two layers, namely the execution layer and the decision-making layer. The execution layer enables the robot to approach its target position and avoid collision with other robots and obstacles through a deep network trained by a reinforcement learning method. The decision-making layer organizes all robots into a formation through a new leader–follower configuration and provides target positions to the leader and followers. The leader’s target position is kept unchanged, while the follower’s target position is changed according to the situation it encounters. In addition, to operate more effectively in environments with different levels of complexity, a hybrid switching control strategy is proposed. The simulation results demonstrate that our proposed formation control framework enables the robots to adjust formation independently to pass through obstacle areas and can be generalized to different scenarios with unknown obstacles and varying number of robots.

Time-Varying Formation Tracking Control for Unmanned Aerial Vehicles with the Leader’s Unknown Input and Obstacle Avoidance: Theories and Applications

This paper investigates the time-varying formation tracking (TVFT) problems for unmanned aerial vehicle (UAV) swarm systems with the leader’s unknown input and collision and obstacle avoidance mechanism. To solve the TVFT control problem which has not been studied extensively, distributed observers are proposed to estimate the leader’s position and a TVFT control protocol is constructed. Stability of control protocol is proved by Lyapunov stability theory. Furthermore, the effectiveness of the TVFT control protocol is verified by numerical simulations and flying experiments, which is more convincing than the theoretical results only.

Unmanned mobile robot in unknown obstacle environments for multi switching control tracking using adaptive nonlinear sliding mode control method

This paper focuses on a wheeled mobile robot that utilizes the Adaptive Nonlinear Sliding mode control technique for trajectory tracking and obstacle avoidance control. A parallel wheeled differential drive Mobile robot’s trajectory tracking control problem is investigated. For diverse initial conditions, the robot must follow a given course to reach it’s destination. To monitor and identify the obstacles in the path, an obstacle micro-controller is fixed to decide quick crash avoidance and follow the obstacle limit at a predetermined distance. It depends on the robot’s vector connections. An Adaptive Nonlinear Sliding Mode (ANSM) control concept is used for continuous trajectory tracking or object monitoring in the path to avoid it. A Lapunov function control gives stability to each controller. The proposed simulation results demonstrate that the mobile robot can be applied to guarantee its protected development in an obscure obstacle environment. In detail, the proposed control gives another, more straightforward methodology with application esteems for tracking critical thinking in an obscure obstacle environment. Finally, based on these above characteristics, the proposed control strategy’s efficiency, simplicity, and accuracy prove. The steady-state error and mean squared error of the proposed ANSM are 6% and 0.07db, respectively.

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

UAV path planning in 3D cluttered and uncertain environments centers on finding an optimal / sub-optimal collision-free path, considering in parallel geometric, physical and temporal constraints, fox example, obstacles, infrastructure, physical or artificial landmarks, etc. This paper introduces a novel node-based algorithm, called Energy Efficient A* (EEA*), which is based on the A* search algorithm, but overcomes some of its key limitations. The EEA* deals with 3D environments, it is robust converging fast to the solution, it is energy efficient and it is real-time implementable and executable. In addition to the EEA*, a local path planner is also derived to cope with unknown dynamic threats within the working environment. The EEA* and the local path planner are first implemented and evaluated via simulated experiments using a fixed-wing UAV operating in mountain-like 3D environments, and in the presence of unknown dynamic obstacles. This is followed by evaluating a set up where three UAVs are commanded to follow their respective paths in a safe way. The energy efficiency of EEA* is also tested and compared with the conventional A* algorithm.

Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest root to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The main drawbacks of classic methods are the incapacity to move the robot in a dynamic and unknown environment, deadlock in a local minimum and complicated environments, and incapacity to foretell the speed vector of obstacles and non-optimality of the route. This article exhibits a recent path planning approach that utilizes the African Vultures Optimization (AVOA) for navigation of the mobile robot in static and dynamic unknown environments with a dynamic target. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, an environment with unknown dynamic obstacles, and an environment with a dynamic target. The proposed approach can solve a local minima problem in the environment with static obstacles. The online optimization method is performed using two phases which are the sensors’ reading phase and the path calculation phase and the results are given based on computer simulation in different unknown environments. A comparative study was conducted between the suggested algorithm and two other algorithms and the results showed that the AVOA algorithm was better in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rates in the path length compared with the Adaptive Particle Swarm Optimization (APSO) and the Hybrid Fuzzy-Wind Driven Optimization (WDO) are 2.21% and 1.02207%, respectively.

Mobile Robot Path Planning Method Based on Deep Reinforcement Learning Algorithm

Path planning is an important part of the research field of mobile robots, and it is the premise for mobile robots to complete complex tasks. This paper proposes a reflective reward design method based on potential energy function, and combines the ideas of multi-agent and multi-task learning to form a new training framework. The reflective reward represents the quality of the agent’s current decision relative to the past historical decision sequence, using the second-order information of the historical reward sequence. The policy or value function update of the master agent is then assisted by the reflective agent. The method proposed in this paper can easily extend the existing deep reinforcement learning algorithm based on value function and policy gradient, and then form a new learning method, so that the agent has the reflective characteristics in human learning after making full use of the reward information. It is good at distinguishing the optimal action in the corresponding state. Experiments in pathfinding scenarios verify the effectiveness of the algorithm in sparse reward scenarios. Compared with other algorithms, the deep reinforcement learning algorithm has higher exploration success rate and stability. Experiments in survival scenarios verify the improvement effect of the reward feature enhancement method based on the auxiliary task learning mechanism on the original algorithm. Simulation experiments confirm the effectiveness of the proposed algorithm for solving the path planning problem of mobile robots in dynamic environments and the superiority of deep reinforcement learning algorithms. The simulation results show that the algorithm can accurately avoid unknown obstacles and reach the target point, and the planned path is the shortest and the energy consumed by the robot is the least. This demonstrates the effectiveness of deep reinforcement learning algorithms for local path planning and real-time decision making.

Path Planning and Guidance Laws of a Formula Student Driverless Car

Autonomous driving has been a topic of great interest in several areas, of which motor racing is no exception. The aim of this work is the autonomous control of the future Formula Student Lisboa vehicle, by implementing different strategies for control and path planning, with the purpose of minimising race lap times. These strategies are tested in simulation, using a realistic model of the prototype. The approach followed involves the decoupling of the lateral and longitudinal subsystems and obtaining the reference path using artificial potential fields, combined with a two-pass algorithm developed to generate a speed profile. In this way, a sub-optimal solution is reached that adequately portrays the expected behaviour of a human driver while respecting traction conditions. The process of generating the speed reference requires prior knowledge of the track layout. This assumption is then eased for obstacle avoidance, i.e., for a scenario where, in addition to the track limits, unknown static obstacles are present. A decoupled control approach is followed controlling each of the two subsystems individually.

Revisiting the probe and enclosure methods

This paper is concerned with reconstruction issue of inverse obstacle problems governed by partial differential equations and consists of two parts. (i) The first part considers the foundation of the probe and enclosure methods for an impenetrable obstacle embedded in a medium governed by the stationary Schrödinger equation. Under a general framework, some natural estimates for a quantity computed from a pair of the Dirichlet and Neumann data on the outer surface of the body occupied by the medium are given. The estimates enables us to derive almost immediately the necessary asymptotic behaviour of indicator functions for both methods. (ii) The second one considers the realization of the enclosure method for a penetrable obstacle embedded in an absorbing medium governed by the stationary Schrödinger equation. The unknown obstacle considered here is modeled by a perturbation term added to the background complex-valued L ∞-potential. Under a jump condition on the term across the boundary of the obstacle and some kind of regularity for the obstacle surface including Lipschitz one as a special case, the enclosure method using infinitely many pairs of the Dirichlet and Neumann data is established.

AUV path tracking with real-time obstacle avoidance via reinforcement learning under adaptive constraints

In this paper, the methods are proposed for underactuated autonomous underwater vehicle (AUV) to address three-dimensional (3D) path tracking and real-time obstacle avoidance. The errors of path tracking are generated based on the Serret–Frenet frame and line-of-sight (LOS) guidance law, while the errors in obstacle avoidance are obtained based on the carrier coordinate system to filter irrelevant environment information. On this basis, to deal with the complicated target path and unknown obstacles, the controller is designed by deep deterministic policy gradient (DDPG) algorithm and adaptive multi-constraints. The safety constraints are adopted in reward functions to avoid useless explorations and facilitate convergence. The training proceeds for path tracking and obstacle avoidance respectively. Compared to the original DDPG algorithm in the training, the proposed algorithm shows faster convergence. Various simulations are conducted under different initial conditions, and the results demonstrate the effectiveness of the proposed algorithm.

A hybrid exploration approach for the prediction of geological changes ahead of mechanized tunnel excavation

The application of effective exploration techniques in mechanized tunneling is crucial in order to obtain a knowledge of the subsoil prior to drilling. We present a three-staged method, which seeks to image the excavation environment in detail, also reducing the computational demand of common exploration techniques. The algorithm combines two approaches: supervised machine learning and full waveform inversion. Firstly, the machine learning algorithm is applied on data sets of measured pore water pressures and ground settlements during tunnel propagation, making a primary prediction of geological changes ahead of the boring machine. Secondly, seismic measurements are acquired for full waveform inversion based on parameter identification. This method incorporates the primary predictions from the supervised machine learning in the form of a parametrization of the position, shape and material properties of the disturbance. Thirdly, the subsurface model gained out of the second stage is utilized as a starting model for a second full waveform inversion using the adjoint method, providing an even more detailed image of the subsurface. The exploration algorithm is tested on a synthetic shallow tunnel environment with an unknown obstacle ahead of the tunnel boring machine. It is shown that the algorithm finds the unknown obstacle with improved accuracy.

Mobile Robot Navigation Functions Tuned by Sensor Readings in Partially Known Environments

Mobile robot navigation functions provide geometric path planning that can be used to control the physical mobile robot system. This letter extends the theory of mobile robot navigation functions to planar environments populated by unknown obstacles. The robot has knowledge of the environment outer boundary but must use an obstacle sensor with sector-shape footprint during navigation. The letter introduces on-line tuning of navigation functions based on sensor readings of obstacles and the robot state upon detection of new obstacles in the environment. The letter describes a feedback control law that changes its structure according to the current navigation function, with trajectory damping that ensures smooth monotonic approach to the target. The correctness of the navigation controller in terms of obstacle avoidance and arrival to the target is established. In addition to the analytic guarantees, simulation studies describe the robot system state parameters during navigation in an office floor environment populated by unknown obstacles.

Path planning for assisting blind people in purposeful navigation

Blind <span>people present dificulties for reaching objects of interest in the daily life. In this sense, the integration of a path planning module for assisting blind people in purposeful navigation is noteworthy. In this work, we present an algorithm that leverages the high capability of an embedded computer with graphics processing unit (GPU) NVIDIA Jetson TX2 for computing optimal paths to objects of interest. The algorithm computes the optimal path to the objective, considering changes in the environment and changes in the position of the user. The algorithm is efficient for computing new paths when the environment changes by reusing parts of previous computations. In order to compare the performance, the algorithms were implemented and evaluated in MATLAB, C++ and CUDA, for different size of the grid and percentage of unknown obstacles. We found that the implementation on GPU has a speed up of 20 times W.R.T the implementation in C++ and more than 400 times W.R.T the implementation in MATLAB. These results boost us to integrate this module to our main system based on a stereo camera and a haptic belt and so provide to the user assistance in purposeful navigation at real time.</span>

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment.

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.

An Improved VM Obstacle Identification Method for Reflection Road

An obstacle detection method based on VM (VIDAR and machine learning joint detection model) is proposed to improve the monocular vision system's identification accuracy. When VIDAR (Vision-IMU-based detection and range method) detects unknown obstacles in a reflective environment, the reflections of the obstacles are identified as obstacles, reducing the accuracy of obstacle identification. We proposed an obstacle detection method called improved VM to avoid this situation. The experimental results demonstrated that the improved VM could identify and eliminate unknown obstacles. Compared with more advanced detection methods, the improved VM obstacle detection method is more accurate. It can detect unknown obstacles in reflection, reflective road environments.

Trajectory Planning for Hybrid Unmanned Aerial Underwater Vehicles with Smooth Media Transition

In the last decade, a great effort has been employed in the study of Hybrid Unmanned Aerial Underwater Vehicles, robots that can easily fly and dive into the water with different levels of mechanical adaptation. However, most of this literature is concentrated on physical design, practical issues of construction, and, more recently, low-level control strategies. Little has been done in the context of high-level intelligence, such as motion planning and interactions with the real world. Therefore, we proposed in this paper a trajectory planning approach that allows collision avoidance against unknown obstacles and smooth transitions between aerial and aquatic media. Our method is based on a variant of the classic Rapidly-exploring Random Tree, whose main advantages are the capability to deal with obstacles, complex nonlinear dynamics, model uncertainties, and external disturbances. The approach uses the dynamic model of the HyDrone, a hybrid vehicle proposed with high underwater performance, but we believe it can be easily generalized to other types of aerial/aquatic platforms. In the experimental section, we present simulated results in environments filled with obstacles, where the robot is commanded to perform different media movements, demonstrating the applicability of our strategy.