Distributed approach for coverage and patrolling missions with a team of heterogeneous aerial robots under communication constraints

Using aerial robots in area coverage applications is an emerging topic. These applications need a coverage path planning algorithm and a coordinated patrolling plan. This paper proposes a distributed approach to coordinate a team of heterogeneous UAVs cooperating efficiently in patrolling missions around irregular areas, with low communication ranges and memory storage requirements. Hence it can be used with small‐scale UAVs with limited and different capabilities. The presented system uses a modular architecture and solves the problem by dividing the area between all the robots according to their capabilities. Each aerial robot performs a decomposition based algorithm to create covering paths and a ’one‐to‐one’ coordination strategy to decide the path segment to patrol. The system is decentralized and fault‐tolerant. It ensures a finite time to share information between all the robots and guarantees convergence to the desired steady state, based on the maximal minimum frequency criteria. A set of simulations with a team of quad‐rotors is used to validate the approach

Semi-Informed Multi-Agent Patrol Strategies

The adversarial multi-agent patrol problem is an active research topic with many real-world applications such as physical robots guarding an area and software agents protecting a computer network. In it, agents patrol a graph looking for so-called critical vertices that are subject to attack by adversaries. The agents are unaware of which vertices are subject to attack by adversaries and when they encounter such a vertex they attempt to protect it from being compromised (an adversary must occupy the vertex it targets a certain amount of time for the attack to succeed). Even though the terms adversary and attack are used, the problem domain extends to patrolling a graph for other interesting noncompetitive contexts such as search and rescue. The problem statement adopted in this work is formulated such that agents obtain knowledge of local graph topology and critical vertices over the course of their travels via an API ; there is no global knowledge of the graph or communication between agents. The challenge is to balance exploration, necessary to discover critical vertices, with exploitation, necessary to protect critical vertices from attack. Four types of adversaries were used for experiments, three from previous research – waiting, random, and statistical - and the fourth, a hybrid of those three. Agent strategies for countering each of these adversaries are designed and evaluated. Benchmark graphs and parameter settings from related research will be employed. The proposed research culminates in the design and evaluation of agents to counter these various types of adversaries under a range of conditions. The results of this work are agent strategies in which each agent becomes solely responsible for protecting those critical vertices it discovers. The agents use emergent behavior to minimize successful attacks and maximize the discovery of new critical vertices. A set of seven edge choosing primitives (ECPs) are defined that are combined in different ways to yield a range of agent strategies using the chain of responsibility OOP design pattern. Every permutation of them were tested and measured in order to identify those strategies that perform well. One strategy performed particularly well against all adversaries, graph topology, and other experimental variables. This particular strategy combines ECPs of: A hard-deadline return to covered vertices to counter the random adversary, efficiently checking vertices to see if they are being attacked by the waiting adversary, and random movement to impede the statistical adversary

Long-term Informative Path Planning with Autonomous Soaring

The ability of UAVs to cover large areas efficiently is valuable for information gathering missions. For long-term information gathering, a UAV may extend its endurance by accessing energy sources present in the atmosphere. Thermals are a favourable source of wind energy and thermal soaring is adopted in this thesis to enable long-term information gathering. This thesis proposes energy-constrained path planning algorithms for a gliding UAV to maximise information gain given a mission time that greatly exceeds the UAV's endurance. This thesis is motivated by the problem of probabilistic target-search performed by an energy-constrained UAV, which is tasked to simultaneously search for a lost ground target and explore for thermals to regain energy. This problem is termed informative soaring (IFS) and combines informative path planning (IPP) with energy constraints. IFS is shown to be NP-hard by showing that it has a similar problem structure to the weight-constrained shortest path problem with replenishments. While an optimal solution may not exist in polynomial time, this thesis proposes path planning algorithms based on informed tree search to find high quality plans with low computational cost. This thesis addresses complex probabilistic belief maps and three primary contributions are presented: • First, IFS is formulated as a graph search problem by observing that any feasible long-term plan must alternate between 1) information gathering between thermals and 2) replenishing energy within thermals. This is a first step to reducing the large search state space. • The second contribution is observing that a complex belief map can be viewed as a collection of information clusters and using a divide and conquer approach, cluster tree search (CTS), to efficiently find high-quality plans in the large search state space. In CTS, near-greedy tree search is used to find locally optimal plans and two global planning versions are proposed to combine local plans into a full plan. Monte Carlo simulation studies show that CTS produces similar plans to variations of exhaustive search, but runs five to 20 times faster. The more computationally efficient version, CTSDP, uses dynamic programming (DP) to optimally combine local plans. CTSDP is executed in real time on board a UAV to demonstrate computational feasibility. • The third contribution is an extension of CTS to unknown drifting thermals. A thermal exploration map is created to detect new thermals that will eventually intercept clusters, and therefore be valuable to the mission. Time windows are computed for known thermals and an optimal cluster visit schedule is formed. A tree search algorithm called CTSDrift combines CTS and thermal exploration. Using 2400 Monte Carlo simulations, CTSDrift is evaluated against a Full Knowledge method that has full knowledge of the thermal field and a Greedy method. On average, CTSDrift outperforms Greedy in one-third of trials, and achieves similar performance to Full Knowledge when environmental conditions are favourable

Practical applications using multi-UAV systems and aerial robotic swarms

[EN] Nowadays, there are a large number of unmanned aircraft on the market that can be commanded with high-level orders to perform complex tasks almost automatically, such as mapping crop fields. We can ask ourselves if it would be possible to coordinate a group of these robots to perform those same tasks more quickly, flexibly and robustly. In this work, we summarize the tasks that have been studied to be solved with systems composed by groups of unmanned aircraft and the algorithms used, as well as the methods and strategies on which they are based. Although the future of these systems is promising, there are certain legislative and technical obstacles that stop their implementation in a generalized way.[ES] A día de hoy, existen en el mercado una gran cantidad de aeronaves sin piloto que pueden ser comandadas con ordenes de alto nivel para realizar tareas complejas de forma casi automatica, como por ejemplo el mapeo de explotaciones agrícolas. De forma natural, nos podemos preguntar si sería posible coordinar a un grupo de estos robots para realizar esas mismas tareas de forma más rápida, flexible y robusta. En este trabajo se repasan las tareas que se han planteado resolver con sistemas compuestos por grupos de aeronaves no tripuladas y los algoritmos empleados, así como los metodos y estrategias en los que están basados. Aunque el futuro de estos sistemas es prometedor, existen ciertos obstaculos legislativos y técnicos que frenan su implantación de forma generalizada.Las investigaciones que han dado como resultado este trabajo han sido financiadas por RoboCity2030-DIH-CM, 426 Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, financiadas por los Programas de Actividades I+D en la Comunidad Madrid, y por el proyecto TASAR (Team of Advanced Search And Rescue Robots), PID2019-105808RB-I00, financiado por el Ministerio de Ciencia e Innovacion (Gobierno de España).García-Aunon, P.; Roldán, J.; De León, J.; Del Cerro, J.; Barrientos, A. (2021). Aplicaciones practicas de los sistemas multi-UAV y enjambres aéreos. Revista Iberoamericana de Automática e Informática industrial. 18(3):230-241. https://doi.org/10.4995/riai.2020.13560OJS230241183Acevedo, J. J., Arrue, B. C., Maza, I., Ollero, A., 2013. Cooperative large area surveillance with a team of aerial mobile robots for long endurance missions. 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