Hybrid Balance Artificial Potential Field Navigation System For An Autonomous Surface Vessel In Riverine Environment

The demands of Autonomous Surface Vessels (ASVs) for applications such as river bathymetry survey and environmental monitoring are increasing rapidly. However, it is still relatively challenging for the ASVs platform to navigate autonomously due to factors such as unknown and unstructured waterway, and the presence of static and dynamic obstacles. The ASV platform needs some level of autonomy and intelligence in order to make reasonable decisions and risk analysis for safe autonomous navigation. There are two issues related to ASV autonomous riverine navigation; river environment modelling and autonomous path planning and obstacles avoidance. Thus, the objectives of the research are: to develop a riverbanks identification algorithm for ASV navigation; and to develop a marine traffic rules compliant navigation and obstacles avoidance algorithm for ASV in the unstructured riverine environment. The riverbanks are selected as the visual cues for the river tracking. The issues of recognising the riverbanks include factors such as color variation with the light condition, water reflection and the complex scene of plants on the riverbanks. In order to overcome these issues, a Color Segmentation Constrained Hough Transform Algorithm is proposed. The results show that the proposed method identified all the riverbanks successfully. To evaluate the performance of the proposed method, the average and variance error deviation are calculated. The Euclidean distances of detected lines from ground truth are used to compare the accuracy of the proposed method. The average error deviation of the proposed method, color segmentation method, Hough Transform method are 3.145 pixel, 16.736 pixel and 27.507 pixel, respectively. The variance error deviation of the three methods are 0.099, 5.467 and 19.749, respectively. For the river tracking problem, a balance control scheme is proposed in order to achieve simultaneous river tracking and obstacles avoidance. The proposed Hybrid Balance-Artificial Potential Field (APF) method is a method that does not utilize the GPS information for the river navigation which means that it is suitable for the case without known river map. Static and dynamic obstacles in the river are used to verify the proposed balance-APF method. The simulation results show that the Hybrid Balance-APF method successfully achieved simultaneous river tracking and obstacles avoidance. In addition, convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is integrated into the ASV navigation system, which makes the ASV able to abide by the standard marine traffic rules. From the adaptation with COLREGs requirements, the ASV platform can navigate safely from typical riverine encounter such as static and dynamic obstacles avoidance, head-on and overtaking encounter. In summary, feasible autonomous riverine environment navigation system for ASV has been successfully developed

An overview of robotics and autonomous systems for harsh environments

Across a wide range of industries and applications, robotics and autonomous systems can fulfil the crucial and challenging tasks such as inspection, exploration, monitoring, drilling, sampling and mapping in areas of scientific discovery, disaster prevention, human rescue and infrastructure management, etc. However, in many situations, the associated environment is either too dangerous or inaccessible to humans. Hence, a wide range of robots have been developed and deployed to replace or aid humans in these activities. A look at these harsh environment applications of robotics demonstrate the diversity of technologies developed. This paper reviews some key application areas of robotics that involve interactions with harsh environments (such as search and rescue, space exploration, and deep-sea operations), gives an overview of the developed technologies and provides a discussion of the key trends and future directions common to many of these areas

Modelling of a Braitenberg inspired guidance system for an Autonomous surface vessel (ASV)

Master's thesis in Mechatronics (MAS500

Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images

We introduce a multi-sensor navigation system for autonomous surface vessels (ASV) intended for water-quality monitoring in freshwater lakes. Our mission planner uses satellite imagery as a prior map, formulating offline a mission-level policy for global navigation of the ASV and enabling autonomous online execution via local perception and local planning modules. A significant challenge is posed by the inconsistencies in traversability estimation between satellite images and real lakes, due to environmental effects such as wind, aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we specifically modelled these traversability uncertainties as stochastic edges in a graph and optimized for a mission-level policy that minimizes the expected total travel distance. To execute the policy, we propose a modern local planner architecture that processes sensor inputs and plans paths to execute the high-level policy under uncertain traversability conditions. Our system was tested on three km-scale missions on a Northern Ontario lake, demonstrating that our GPS-, vision-, and sonar-enabled ASV system can effectively execute the mission-level policy and disambiguate the traversability of stochastic edges. Finally, we provide insights gained from practical field experience and offer several future directions to enhance the overall reliability of ASV navigation systems.Comment: 33 pages, 20 figures. Project website https://pcctp.github.io. arXiv admin note: text overlap with arXiv:2209.1186

An OpenEaagles Framework Extension for Hardware-in-the-Loop Swarm Simulation

Unmanned Aerial Vehicle (UAV) swarm applications, algorithms, and control strategies have experienced steady growth and development over the past 15 years. Yet, to this day, most swarm development efforts have gone untested and thus unimplemented. Cost of aircraft systems, government imposed airspace restrictions, and the lack of adequate modeling and simulation tools are some of the major inhibitors to successful swarm implementation. This thesis examines how the OpenEaagles simulation framework can be extended to bridge this gap. This research aims to utilize Hardware-in-the-Loop (HIL) simulation to provide developers a functional capability to develop and test the behaviors of scalable and modular swarms of autonomous UAVs in simulation with high confidence that these behaviors will prop- agate to real/live ight tests. Demonstrations show the framework enhances and simplifies swarm development through encapsulation, possesses high modularity, pro- vides realistic aircraft modeling, and is capable of simultaneously accommodating four hardware-piloted swarming UAVs during HIL simulation or 64 swarming UAVs during pure simulation