Design of Unmanned System for Fish-finding and Obstacle Avoidance Based on Pixhawk

The local path planning of unmanned ships has high real-time requirements, and the main research methods such as dynamic window method, random tree method, artificial potential field method, ant colony algorithm, particle swarm algorithm and other methods are difficult to be directly applied to path planning. Aiming at the local collision avoidance problem of unmanned ships, this paper proposes a local collision avoidance method for unmanned ships based on particle swarm optimization algorithm. The algorithm introduces fuzzy mathematics theory to judge the collision risk of unmanned boats and obstacles, transforms the local obstacle avoidance problem of unmanned boats into the optimization problem of the speed and heading changes of small boats, and integrates maritime rules and unmanned boat dynamics. The constraint conditions further optimize the value range of the speed and heading, so that the optimal obstacle avoidance strategy can be calculated through the optimization of the particle swarm optimization algorithm. The simulation research simulates the complex environment at sea and selects typical encounter situations in the maritime rules for testing. The simulation results show that the proposed method can well adapt to obstacle avoidance situations such as ship encounters, overtaking, and cross encounters.

Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.

AUTONOMOUS FLIGHT CONTROL AND HARDWARE-IN-THE-LOOP SIMULATOR FOR A SMALL HELICOPTER

Visual obstacle avoidance is widely applied to unmanned aerial vehicles (UAVs) and mobile robot fields. A simple system architecture, low power consumption, optimized processing, and real‐time performance are extremely needed due to the limited payload of some mini UAVs. To address these issues, an obstacle avoidance system harnessing the rate encoding features of a photonic spiking neuron based on a Fabry–Pérot (FP) laser is proposed, which simulates the monocular vision. Here, time to collision is used to describe the distance of obstacles. The experimental results show that the FP laser excites ultra‐fast spike responses in real time for the following cases, facilitating the generation of control commands by motor neurons to realize accurate decision‐making. Four cases of mobile obstacle avoidance scenarios, including “Constant velocity approach”, “Approach and retreat”, “The motion state involving stays”, and “Approach with different velocities”, and obstacle avoidance problems with multiple stationary obstacles appearing simultaneously are experimentally analyzed. The system exhibits a spike response rate of up to 5 GHz. This work proves the feasibility of applying the ultra‐fast photonic obstacle avoidance system to UAVs and other fields in the future and highlights the potential of photonic neuromorphic processor platforms.

When humans return to the moon, Astronauts will be accompanied by robotic helpers. Enabling robots to safely operate near astronauts on the lunar surface has the potential to significantly improve the efficiency of crew surface operations. Safely operating robots in close proximity to astronauts on the lunar surface requires reactive obstacle avoidance capabilities not available on existing planetary robots. In this paper we present work on safe, reactive navigation using a stereo based high-speed terrain analysis and obstacle avoidance system. Advances in the design of the algorithms allow it to run terrain analysis and obstacle avoidance algorithms at full frame rate (30Hz) on off the shelf hardware. The results of this analysis are fed into a fast, reactive path selection module, enforcing the safety of the chosen actions. The key components of the system are discussed and test results are presented.

Mobile robot has been a major role to the application in military, industrial and agricultural purposes. Mobile robot should navigate through desire route and avoid the obstacle within the path. Many researcher come with the solution by using the various type of control and instrumentation system. The complexity of mobile robot system can make the system cost intensive and high risk. Proposed is a mobile robot equipped with GPS navigation system and obstacle avoidance system with low cost mobile structure, GPS module and sonar sensor. The combination GPS and Sonar will determine the position and obstacle avoidance for the mobile robot. Mobile robot should navigate according to waypoint that preset to the GPS module and sonar sensor detects the obstacle during mobile robot navigation by triggering the sonar sensor in sequence by using commanded loop daisy chaining application method. Mobile Robot can navigate through desired waypoint and at the same time apply the obstacle avoidance rules

Obstacle Avoidance for Unmanned Sea Surface Vehicles: A Hierarchical Approach

The aim of the project is to design an obstacle avoidance system by using neural network. The main functionality of the project is to predict the object detected and to find a path of avoidance the detected object. Due to the increase in autonomous vehicle centered around machine learning technology, expensive system which incorporates multiple sensors for the obstacle avoidance and object detection are made. To fill this gap, the project is designed to implement an object detection and obstacle avoidance system using a camera as the main component for detection. Jetson nano board is used as the main computer, NoirV2 camera is used as the main vision sensor, 3d printed structure for the body, two dc motor with a I2c based motor driver. SSD MobileNet v2 is used as the model for object detection, Jetson inference is the training guide which is used for the obstacle avoidance and object detection which is optimized to work with Jetson nano board providing a faster detection and fps. The project includes a web based Graphical user interface to control the robot and to monitor it. The project will solve the issues of expensive system and requirement of multiple sensors for object detection and obstacle avoidance.

Objectives In order to solve the problem that unmanned boats cannot carry out reasonable and effective autonomous obstacle avoidance under dense obstacle sea conditions. Methods An improved dual-window dynamic window approach（DWA）obstacle avoidance algorithm based on fuzzy control strategy is proposed, that is, a sensing window based on the onboard sensor is designed on the basis of the conventional speed window, and the dual-window model composed of the speed window and the sensing window is further optimized The speed space is constrained, and the weight of the evaluation function is dynamically adjusted based on the fuzzy control strategy based on the obstacle distribution state and the distance between the UAV and the obstacle. Results The simulation experiment results show that, compared with the original DWA algorithm, the path planned by the improved algorithm under unknown dense obstacle sea conditions is more reasonable and smoother, which not only solves the problem of unmanned boats detouring outside the dense obstacle group, but also takes into account The safety of obstacle avoidance navigation is improved, the number of iterations and running time can be shortened by more than 20%. Conclusions The research results have certain reference value for the research on autonomous obstacle avoidance technology of unmanned boats.

We propose a system for detection and avoidance of obstacles based on the use of fuzzy logic. Basically, data coming from a ZED stereo camera are used for the decision making based on fuzzy predicates. Validation and testing of the algorithms are provided under the ROS (Robot Operating System), which runs together with Gazebo 7. The developed tool is an important step towards solving problems related to the techniques and methods or algorithms that can be used in mobile robots for detection and avoidance of obstacles and other objects on their way. The proposed system proved to be very robust in the tests carried out in the simulated environment.

Unmanned aerial vehicles (UAVs), also known as drones, have gained considerable interest among academics in recent years, which significantly increases the demand for autonomous navigation systems for UAVs. In this study, autonomous navigation is addressed as a two-stage problem. The first stage is the obstacle detection phase, during which the UAVs leverage their onboard sensors to perceive the surroundings and detect obstacles. The second stage is the obstacle avoidance phase, where the UAVs make decisions to continue operations without colliding with anything while approaching the target. This study proposes a self-supervised learning-based depth prediction framework as the criterion for obstacle proximity detection. The framework consists of two subnetworks: the depth and pose estimation modules. The former predicts depth values, while the latter evaluates the object’s translation and rotation matrix. The framework leverages the subnetworks’ outputs to reconstruct the projection relationships of pixels between adjacent frames to optimize the training process. To address the challenge of UAVs operating in large-scale environments, a modified loss function that combines photometric loss and edge-aware smoothness is presented to increase the depth prediction module’s accuracy. This study also presents a deep reinforcement learning-based obstacle avoidance system that takes depth maps as inputs to generate evasive maneuvers. The system works independently from priori maps and models while it learns the collision-free trajectory from interacting with the environment. This study deploys value-based algorithms to learn the nonlinear mapping between the continuous inputs and discrete output motion commands. Furthermore, the proposed system is validated on different simulation scenarios. It is capable of completing the obstacle avoidance task with relatively optimal trajectories in varied environments, demonstrating its superior effectiveness and transferability. Keywords: unmanned aerial vehicles, urban airspace, obstacle detection and avoidance, self-supervised learning, deep reinforcement learning.

Under emergency conditions, the safety distance between vehicles is insufficient, and drivers initiate unreasonable obstacle avoidance, which results in accident risks to vehicles. Therefore, an emergency obstacle avoidance system is needed for intervention to avoid traffic accidents. This paper proposes a coordinated steering and braking obstacle avoidance control strategy based on a safety distance model. The strategy takes into account handling stability and ride comfort. In the emergency braking obstacle avoidance module, the safety distance model and the graded warning mechanism based on the braking process are established. In the emergency steering obstacle avoidance module, a steering safety distance mode with multiple constraints is established, the obstacle avoidance trajectory is planned using the fifth-degree polynomial, and the obstacle avoidance trajectory is tracked using the model predictive control algorithm. The co-simulation results show that the emergency obstacle avoidance strategy proposed in this paper can select the appropriate way to complete obstacle avoidance under different working conditions. In braking obstacle avoidance, the braking deceleration essentially matches the expected value. In steering obstacle avoidance, the maximum error of lateral displacement can be reduced to 0.09 m. The research in this paper can provide a theoretical and practical basis for improving the driving safety, handling stability and ride comfort of intelligent vehicles.

The authors describe the implementation of a full-function guidance and control system for automatic obstacle avoidance in helicopter nap-of-the-earth (NOE) flight. The guidance function assumes that the helicopter is sufficiently responsive so that the flight path can be readily adjusted at NOE speeds. The controller, basically an autopilot for following the derived flight path, was implemented with parameter values to control a generic helicopter model used in the simulation. Evaluation of the guidance and control system with a 3-dimensional graphical helicopter simulation suggests that the guidance has the potential for providing good and meaningful flight trajectories. >

Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms (formation control term, velocity matching term, and obstacle avoidance term) is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.

The obstacle avoidance controller is a key autonomous component which involves the control of tractor system dynamics, such as the yaw lateral dynamics, the longitudinal dynamics, and nonlinear constraints including the speed and steering angles limits during the path-tracking process. To achieve the obstacle avoidance ability of control accuracy, an independent path re-planning controller is proposed based on ROS (Robot Operating System) nonlinear model prediction in this paper. In the design process, the obstacle avoidance function and an objective function are introduced. Based on these functions, the obstacle avoidance maneuvering performance is transformed into a nonlinear quadratic optimization problem with vehicle dynamic constraints. Moreover, the tractor dynamics maneuvering performance can be effectively adjusted through the proposed objective function. To validate the proposed algorithm, a ROS based tractor dynamics model and the SLAM (Simultaneous Localization and Mapping) are established for numerical simulations under different speed. The maximum obstacle avoidance deviation in the simulation is 0.242 m at 10 m/s, and 0.416 m at 30 m/s. The front-wheel rotation angle and lateral velocity are within the constraint range during the whole tracking process. The numerical results show that the designed controller can achieve the tractor obstacle avoidance ability with good accuracy under different conditions. Keywords: ROS, obstacle avoidance, nonlinear model prediction, agricultural tractor DOI: 10.25165/j.ijabe.20191206.4907 Citation: Liu Z D, Lv Z Q, Zheng W X, Zhang W Z, Cheng X X. Design of obstacle avoidance controller for agricultural tractor based on ROS. Int J Agric & Biol Eng, 2019; 12(6): 58–65.