An Information Value Approach to Route Planning for UAV Search and Track Missions

This dissertation has three contributions in the area of path planning for Unmanned Aerial Vehicle (UAV) Search And Track (SAT) missions. These contributions are: (a) the study of a novel metric, G, used to quantify the value of the target information gained during a search and track mission, (b) an optimal planning horizon that minimizes time-error of a planning horizon when interrupted by Poisson random events, and (c) a modified Particle Swarm Optimization (PSO) algorithm for search missions that uses the prior target distribution in the generation of paths rather than just in the evaluation of them. UAV route planning is an important topic with many applications. Of these, military applications are the best known. This dissertation focuses on route planning for SAT missions that jointly optimize the conflicting objectives of detecting new targets and monitoring previously detected targets. The information theoretic approach proposed here is different from and is superior to existing approaches. One of the main differences is that G quantifies the value of the target information rather than the information itself. Several examples are provided to highlight G’s desirable properties. Another important component of path planning is the selection of a planning horizon, which specifies the amount of time to include in a plan. Unfortunately, little research is available to aid in the selection of a planning horizon. The proposed planning horizon is derived in the context of plan updates triggered by Poisson random events. To our knowledge, it is the only theoretically derived horizon available making it an important contribution. While the proposed horizon is optimal in minimizing planning time errors, simulation results show that it is also near optimal in minimizing the average time needed to capture an evasive target. The final contribution is the modified PSO. Our modification is based on the idea that PSO should be provided with the target distribution for path generation. This allows the algorithm to create candidate path plans in target rich regions. The modified PSO is studied using a search mission and is used in the study of G

Al-Robotics team: A cooperative multi-unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge

The Al-Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al-Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.Unión Europea H2020 73166

The Value of Information in Multi-Objective Missions

In many multi-objective missions there are situations when actions based on maximum information gain may not be the `best' given the overall mission objectives. In addition to properties such as entropy, information also has value, which is situationally dependent. This thesis examines the concept of information value in a multi-objective mission from an information theory perspective. A derivation of information value is presented that considers both the context of information, via a fused world belief state, and a system mission. The derived information value is used as part of the objective function for control of autonomous platforms within a framework developed for human robot cooperative control. A simulated security operation in a structured environment is implemented to test both the framework, and information value based control. The simulation involves a system of heterogeneous, sensor equipped Unmanned Aerial Vehicles (UAVs), tasked with gathering information regarding ground vehicles. The UAVs support an e ort to protect a number of important buildings in the area of operation. Thus, the purpose of the information is to aid the security operation by ensuring that security forces can deploy e ciently to counter any threat. A number of di erent local controllers using information based control are implemented and compared to a task based control scheme. The relative performance of each is examined with respect to a number of performance metrics with conclusions drawn regarding the performance and exibility of information value based control

Multi-objective Decentralised Coordination for Teams of Robotic Agents

This thesis introduces two novel coordination mechanisms for a team of multiple autonomous decision makers, represented as autonomous robotic agents. Such techniques aim to improve the capabilities of robotic agents, such as unmanned aerial or ground vehicles (UAVs and UGVs), when deployed in real world operations. In particular, the work reported in this thesis focuses on improving the decision making of teams of such robotic agents when deployed in an unknown, and dynamically changing, environment to perform search and rescue operations for lost targets. This problem is well known and studied within both academia and industry and coordination mechanisms for controlling such teams have been studied in both the robotics and the multi-agent systems communities. Within this setting, our first contribution aims at solves a canonical target search problem, in which a team of UAVs is deployed in an environment to search for a lost target. Specifically, we present a novel decentralised coordination approach for teams of UAVs, based on the max-sum algorithm. In more detail, we represent each agent as a UAV, and study the applicability of the max-sum algorithm, a decentralised approximate message passing algorithm, to coordinate a team of multiple UAVs for target search. We benchmark our approach against three state-of-the-art approaches within a simulation environment. The results show that coordination with the max-sum algorithm out-performs a best response algorithm, which represents the state of the art in the coordination of UAVs for search, by up to 26%, an implicitly coordinated approach, where the coordination arises from the agents making decisions based on a common belief, by up to 34% and finally a non-coordinated approach by up to 68%. These results indicate that the max-sum algorithm has the potential to be applied in complex systems operating in dynamic environments. We then move on to tackle coordination in which the team has more than one objective to achieve (e.g. maximise the covered space of the search area, whilst minimising the amount of energy consumed by each UAV). To achieve this shortcoming, we present, as our second contribution, an extension of the max-sum algorithm to compute bounded solutions for problems involving multiple objectives. More precisely, we develop the bounded multi-objective max-sum algorithm (B-MOMS), a novel decentralised coordination algorithm able to solve problems involving multiple objectives while providing guarantees on the solution it recovers. B-MOMS extends the standard max-sum algorithm to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Moreover, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Finally, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds $2$, and is typically less than $1.5$ for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds $30$ minutes, even for maximally constrained graphs with one hundred agents