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

Spatial modeling of field traffic intensity

Agricultural soils are being trafficked frequently and intensively with successively rising weights of machinery. This increases the risk of soil compaction. But the real and spatial traffic intensities in the field, which means the number of wheel passes and the extent of wheel loads, are usually only estimated. However, these are essential to estimate the effects of the loads on the soil spatially and quantitativly. The present dissertation focuses on the investigation of the spatial traffic intensities on arable land by agricultural machines. The field traffic model (FiTraM) models the tire tracks and reconstructs the intensities of individual work processes based on the route. On a study field near Adenstedt in Lower Saxony, Germany, the operations of winter wheat, silage maize and sugar beet were recorded and modeled over a complete crop rotation of four years. For a study area near Ascheberg in Schleswig-Holstein, Germany, the harvesting of wheat on varying field geometries with three different combine harvester configurations was investigated. The results of the analyses quantify the intensities of the traffic spatially. In particular, the headland of the fields and the work processes for harvesting and sowing are the most intensively stressed. The number of wheel passes on the headland depends, i.a., on the geometry of the field. The more complex the field geometry and the larger the area in relation to the perimeter and the headland area, the larger the area with a high number of wheel passes. The wheel load and the completely passed area is mainly influenced by the size of the combine harvester. Results show the actual intensities caused by agricultural machinery in the field. These results show that there are strong spatial variations in the loads and that these should be taken into account in models for the analysis of soil compaction risk in order to be able to make specific recommendations for practical implementation

Autonomous Operation of a Reconfigurable Multi-Robot System for Planetary Space Missions

Reconfigurable robots can physically merge and form new types of composite systems. This ability leads to additional degrees of freedom for robot operations especially when dynamically composed robotic systems offer capabilities that none of the individual systems have. Research in the area of reconfigurable multi-robot systems has mainly been focused on swarm-based robots and thereby to systems with a high degree of modularity but a heavily restricted set of capabilities. In contrast, this thesis deals with heterogeneous robot teams comprising individually capable robots which are also modular and reconfigurable. In particular, the autonomous application of such reconfigurable multi-robot systems to enhance robotic space exploration missions is investigated. Exploiting the flexibility of a reconfigurable multi-robot system requires an appropriate system model and reasoner. Hence, this thesis introduces a special organisation model. This model accounts for the key characteristics of reconfigurable robots which are constrained by the availability and compatibility of hardware interfaces. A newly introduced mapping function between resource structures and functional properties permits to characterise dynamically created agent compositions. Since a combinatorial challenge lies in the identification of feasible and functionally suitable agents, this thesis further suggests bounding strategies to reason efficiently with composite robotic systems. This thesis proposes a mission planning algorithm which permits to exploit the flexibility of reconfigurable multi-robot systems. The implemented planner builds upon the developed organisation model so that multi-robot missions can be specified by high-level functionality constraints which are resolved to suitable combinations of robots. Furthermore, the planner synchronises robot activities over time and characterises plans according to three objectives: efficacy, efficiency and safety. The plannera s evaluation demonstrates an optimization of an exemplary space mission. This research is based on the parallel development of theoretical concepts and practical solutions while working with three reconfigurable multi-robot teams. The operation of a reconfigurable robotic team comes with practical constraints. Therefore, this thesis composes and evaluates an operational infrastructure which can serve as reference implementation. The identification and combination of applicable state-of-the-art technologies result in a distributed and dynamically extensible communication infrastructure which can maintain the properties of reconfigurable multi-robot systems. Field tests covering semi-autonomous and autonomous operation have been performed to characterise multi-robot missions and validate the autonomous control approach for reconfigurable multi-robot systems. The practical evaluation identified critical constraints and design elements for a successful application of reconfigurable multi-robot systems. Furthermore, the experiments point to improvements for the organisation model. This thesis is a wholistic approach to automate reconfigurable multi-robot systems. It identifies theoretical as well as practical challenges and it suggests effective solutions which permit an exploitation of an increased level of flexibility in future robotics missions

Fine-grained performance analysis of massive MTC networks with scheduling and data aggregation

Abstract. The Internet of Things (IoT) represents a substantial shift within wireless communication and constitutes a relevant topic of social, economic, and overall technical impact. It refers to resource-constrained devices communicating without or with low human intervention. However, communication among machines imposes several challenges compared to traditional human type communication (HTC). Moreover, as the number of devices increases exponentially, different network management techniques and technologies are needed. Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources. This thesis provides an overview of the most common IoT applications and the network technologies to support them. We describe the most important challenges in machine type communication (MTC). We use a stochastic geometry (SG) tool known as the meta distribution (MD) of the signal-to-interference ratio (SIR), which is the distribution of the conditional SIR distribution given the wireless nodes’ locations, to provide a fine-grained description of the per-link reliability. Specifically, we analyze the performance of two scheduling methods for data aggregation of MTC: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements. Finally, the impact on the fraction of MTDs that communicate with a target reliability when increasing the aggregators density is investigated

Content Caching and Delivery in Heterogeneous Vehicular Networks

Connected and automated vehicles (CAVs), which enable information exchange and content delivery in real time, are expected to revolutionize current transportation systems for better driving safety, traffic efficiency, and environmental sustainability. However, the emerging CAV applications such as content delivery pose stringent requirements on latency, throughput, reliability, and global connectivity. The current wireless networks face significant challenges to satisfy the requirements due to scarce radio spectrum resources, inflexibility to dynamic traffic demands, and geographic-constrained fixed infrastructure deployment. To empower multifarious CAV content delivery, heterogeneous vehicular networks (HetVNets), which integrate the terrestrial networks with aerial networks formed by unmanned aerial vehicles (UAVs) and space networks constituting of low Earth orbit (LEO) satellites, can guarantee reliable, flexible, cost-effective, and globally seamless service provisioning. In addition, edge caching is a promising solution to facilitate content delivery by caching popular files in the HetVNet access points (APs) to relieve the backhaul traffic with a lower delivery delay. The main technical issues are: 1) to fully reveal the potential of HetVNets for content delivery performance enhancement, content caching scheme design in HetVNets should jointly consider network characteristics, vehicle mobility patterns, content popularity, and APs’ caching capacities; 2) to fully exploit the controllable mobility and agility of UAVs to support dynamic vehicular content demands, the caching scheme and trajectory design for UAVs should be jointly optimized, which has not been well addressed due to their intricate inter-coupling relationships; and 3) for caching-based content delivery in HetVNets, a cooperative content delivery scheme should be designed to enable the cooperation among different network segments with ingenious utilization of heterogeneous network resources. In this thesis, we design the content caching and delivery schemes in the caching-enabled HetVNet to address the three technical issues. First, we study the content caching in HetVNets with fixed terrestrial APs including cellular base stations (CBSs), Wi-Fi roadside units (RSUs), and TV white space (TVWS) stations. To characterize the intermittent network connection caused by limited network coverage and high vehicle mobility, we establish an on-off model with service interruptions to describe the vehicular content delivery process. Content coding then is leveraged to resist the impact of unstable network connections and enhance caching efficiency. By jointly considering file characteristics and network conditions, the content placement is formulated as an integer linear programming (ILP) problem. Adopting the idea of the student admission model, the ILP problem is then transformed into a many-to-one matching problem between content files and HetVNet APs and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with a low complexity. Second, UAV-aided caching is considered to assist vehicular content delivery in aerial-ground vehicular networks (AGVN) and a joint caching and trajectory optimization (JCTO) problem is investigated to jointly optimize content caching, content delivery, and UAV trajectory. To enable real-time decision-making in highly dynamic vehicular networks, we propose a deep supervised learning scheme to solve the JCTO problem. Specifically, we first devise a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content caching policy, we design a time-based graph decomposition method to jointly optimize content delivery and UAV trajectory, with which we then leverage the particle swarm optimization algorithm to optimize the content caching. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make online decisions. With the CNN-based model, a function mapping the input network information to output decisions can be intelligently learnt to make timely inferences. Extensive trace-driven experiments are conducted to demonstrate the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model. Third, we investigate caching-assisted cooperative content delivery in space-air-ground integrated vehicular networks (SAGVNs), where vehicular content requests can be cooperatively served by multiple APs in space, aerial, and terrestrial networks. In specific, a joint optimization problem of vehicle-to-AP association, bandwidth allocation, and content delivery ratio, referred to as the ABC problem, is formulated to minimize the overall content delivery delay while satisfying vehicular quality-of-service (QoS) requirements. To address the tightly-coupled optimization variables, we propose a load- and mobility-aware ABC (LMA-ABC) scheme to solve the joint optimization problem as follows. We first decompose the ABC problem to optimize the content delivery ratio. Then the impact of bandwidth allocation on the achievable delay performance is analyzed, and an effect of diminishing delay performance gain is revealed. Based on the analysis results, the LMA-ABC scheme is designed with the consideration of user fairness, load balancing, and vehicle mobility. Simulation results demonstrate that the proposed LMA-ABC scheme can significantly reduce the cooperative content delivery delay compared to the benchmark schemes. In summary, we have investigated the content caching in terrestrial networks with fixed APs, joint caching and trajectory optimization in the AGVN, and caching-assisted cooperative content delivery in the SAGVN. The proposed schemes and theoretical results should provide useful guidelines for future research in the caching scheme design and efficient utilization of network resources in caching-enabled heterogeneous wireless networks