A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

A survey on autonomous environmental monitoring approaches: towards unifying active sensing and reinforcement learning

The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment's changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area.

A Comprehensive Review of Path Planning for Agricultural Ground Robots

The population of the world is predicted to reach nine billion by 2050, implying that agricultural output must continue to rise. To deal with population expansion, agricultural chores must be mechanized and automated. Over the last decade, ground robots have been developed for a variety of agricultural applications, with autonomous and safe navigation being one of the most difficult hurdles in this development. When a mobile platform moves autonomously, it must perform a variety of tasks, including localization, route planning, motion control, and mapping, which is a critical stage in autonomous operations. This research examines several agricultural applications as well as the path planning approach used. The purpose of this study is to investigate the current literature on path/trajectory planning aspects of ground robots in agriculture using a systematic literature review technique, to contribute to the goal of contributing new information in the field. Coverage route planning appears to be less advanced in agriculture than point-to-point path routing, according to the finding, which is due to the fact that covering activities are usually required for agricultural applications, but precision agriculture necessitates point-to-point navigation. In the recent era, precision agriculture is getting more attention. The conclusion presented here demonstrates that both field coverage and point-to-point navigation have been applied successfully in path planning for agricultural robots.

An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques.

A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors

The monitoring of water resources using Autonomous Surface Vehicles with water-quality sensors has been a recent approach due to the advances in unmanned transportation technology. The Ypacaraí Lake, the biggest water resource in Paraguay, suffers from a major contamination problem because of cyanobacteria blooms. In order to supervise the blooms using these on-board sensor modules, a Non-Homogeneous Patrolling Problem (a NP-hard problem) must be solved in a feasible amount of time. A dimensionality study is addressed to compare the most common methodologies, Evolutionary Algorithm and Deep Reinforcement Learning, in different map scales and fleet sizes with changes in the environmental conditions. The results determined that Deep Q-Learning overcomes the evolutionary method in terms of sample-efficiency by 50-70% in higher resolutions. Furthermore, it reacts better than the Evolutionary Algorithm in high space-state actions. In contrast, the evolutionary approach shows a better efficiency in lower resolutions and needs fewer parameters to synthesize robust solutions. This study reveals that Deep Q-learning approaches exceed in efficiency for the Non-Homogeneous Patrolling Problem but with many hyper-parameters involved in the stability and convergence.

A Bayesian Optimization Approach for Multi-Function Estimation for Environmental Monitoring Using an Autonomous Surface Vehicle: Ypacarai Lake Case Study

Bayesian optimization is a sequential method that can optimize a single and costly objective function based on a surrogate model. In this work, we propose a Bayesian optimization system dedicated to monitoring and estimating multiple water quality parameters simultaneously using a single autonomous surface vehicle. The proposed work combines different strategies and methods for this monitoring task, evaluating two approaches for acquisition function fusion: the coupled and the decoupled techniques. We also consider dynamic parametrization of the maximum measurement distance traveled by the ASV so that the monitoring system balances the total number of measurements and the total distance, which is related to the energy required. To evaluate the proposed approach, the Ypacarai Lake (Paraguay) serves as the test scenario, where multiple maps of water quality parameters, such as pH and dissolved oxygen, need to be obtained efficiently. The proposed system is compared with the predictive entropy search for multi-objective optimization with constraints (PESMOC) algorithm and the genetic algorithm (GA) path planning for the Ypacarai Lake scenario. The obtained results show that the proposed approach is 10.82% better than other optimization methods in terms of R2 score with noiseless measurements and up to 17.23% better when the data are noisy. Additionally, the proposed approach achieves a good average computational time for the whole mission when compared with other methods, 3% better than the GA technique and 46.5% better than the PESMOC approach.

Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection

The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.

Indoor Path-Planning Algorithm for UAV-Based Contact Inspection

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.