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Continual developments in robotic technology have enabled the use of robots in everyday applications in domestic, office and public spaces. Although single robot problems have been the main focus of social robotics research, applications of robots in social environments will not be limited to a single robot due to the increasing demand for robotic assistants and multi-robot operations. Multi-robot systems can achieve performances exceeding the sum of the individual robot contributions by exploiting the full potential of the team through information sharing, coordination, and joint decision-making. Robots operating in human-populated environments either directly interact with people or have to share the space with the humans. It is of utmost importance that people co-existing with robots feel safe and comfortable around them. This makes human-awareness essential for long-term sustainable deployment of robots in such environments. Furthermore, for cooperative robots, the presence of humans and their actions can directly affect the robot and team plans, making human-awareness more essential for ensuring high performance as well as social acceptability. Research in the area of socially-aware navigation has received substantial attention in recent years. However, despite their great potential, human-aware teams of robots considering social factors at both individual navigation and collective coordination and planning levels, are currently largely unexplored. In this thesis, we address the problem of human-aware cooperative navigation and coordination for multi-robot systems in realistic social environments. We focus on a class of multi-robot coordination problems known as multi-robot task allocation using a market-based approach. We explicitly consider the challenges of noisy, dynamic and stochastic human-populated environments by means of accounting for perception and prediction limitations and uncertainties in social cost modeling, bid estimation, coordination, and replanning. We construct an end-to-end framework comprising three main components of (i) human-aware navigation, (ii) human-aware coordination and planning for multi-robot systems, and (iii) human-robot interaction in the presence of multiple cooperative robots. We opt for an incremental approach to this problem starting from single robot human-aware navigation with expectation-based social costmaps. Subsequently, we move to multi-robot cooperative navigation in highly stochastic social environments. We propose human-aware coordination strategies based on social costs and social risks. The concept of risk introduced in this thesis incorporates perception and prediction uncertainties as well as social costs for estimating the stochastic costs of tasks that the robots should bid on in the market. Additionally, we introduce an adaptive risk-based replanning method for dealing with the limitations of local perception and unpredicted human behavior in the social environment. Finally, we demonstrate the interactive potential of the team of robots for social multi-robot task allocation by integrating an interaction that actively requests human collaboration and assistance in socially costly and blocking situations, into our adaptive replanning strategy. Extensive experiments with up to four robots and 12 humans in simulation, and up to two robots and two humans in reality have been carried out for evaluating the performance of the proposed methods in this thesis.

The newly emerged and quickly growing science of nanotechnology has been recognized as one of ``the twenty-first century's great leaps forward in scientific knowledge''. Self-assembly provides a powerful enabling technique for nanotechnology by providing a bottom-up solution as an alternative to the conventional top-down approach in nano-fabrication. Employing self-assembly in nanotechnology seems in fact inevitable. As we try to build ever smaller structures as big as only a few atoms, utilizing tools for putting the molecular building blocks together proves more and more inefficient and impractical. Alternatively, we may let the building blocks put themselves together, let the molecules do what they do best, self-assembling themselves into useful structures. The big question today is thus, can we learn to build things the way nature does? A core element of our work is the experimental robotic system. With the goal of realizing a distributed robotic system in which the resource-constrained robotic modules build pre-defined target structures through programmable stochastic self-assembly, our developments are centered around the 3-cm-sized water-floating Lily robotic module. Furthermore, we implement a controllable setup around the Lily robotic modules where several environmental features such as the fluidic flow in the environment as well as the ambient luminosity perceived by the modules can be controlled in order to influence the self-assembly process towards the target structure. The experiments reported in this dissertation has been carried out with up to 15 Lily robotic modules. Developing models that accurately describe the assembly process dynamics is a key component in studying programmable stochastic self-assembling systems. Such models help in: (1) accurately predicting the performances (assembly rate and yield) of the distributed system, and (2) evaluating and optimizing control strategies, whether distributed (e.g., ruleset controllers programmed on the modules) or centralized (e.g., modulating environmental features such as mixing forces deriving random interactions among modules), based on model predictions. We develop models at three abstraction levels, namely submicroscopic, microscopic, and macroscopic. Programmable self-assembly defines a subclass of self-assembly processes where the building blocks carry information about the final desired target structure. It is through modifying this information that the outcome of the self-assembly process can be programmed. The problem of distributed control for programmable self-assembly is thus one of designing a global-to-local behavioral compiler. The problem of ruleset synthesis for programmable self-assembly of bodiless modules has been studied in the literature by employing graph grammar formalism. We extend the graph grammar formalism and take into account the morphology of the robotic modules. This allows for formulating automatic rule synthesis methods for self-assembly of robotic modules, where the synthesized rules can be directly deployed on the robotic modules, with no further tuning. Moreover, we propose a new rule synthesis algorithm for synthesizing assembly rules which further promote parallelization in the self-assembly process without losing guarantees on the completeness of the achieved target.

The work of this thesis centers around the research subject of distributed robotic search. Within the field of distributed robotic systems, a task of particular interest is attempting to locate one or more targets in a possibly unknown environment. While numerous studies have proposed and analyzed methods to accomplish this, there is currently a lack of a strong foundation of tools and techniques that can be used to facilitate the development and evaluation of different approaches to distributed robotic search. In this work, we aim to provide such a foundation through tools, methods, models, and analysis of experimentation. An often overlooked aspect of the distributed robotic research process is the development and analysis of tools and modules to be used with robotic systems. These may include plug-ins for realistic robotic simulators, software/hardware systems to track multiple mobile robots in real-time, extension boards for robotic platforms, and the robots themselves. Along with other tools developed and used for this work, we focus particularly on the development, characterization, and validation of a fast, accurate on-board system for relative positioning and communication between robots, a capability which is critical for effective distributed robotic search. Designing individual robot controllers to generate a specific group behavior is a difficult and often counter-intuitive process. A possible alternative to hand-crafting distributed search controllers is automatic synthesis using machine-learning techniques. We explore the effectiveness of using a noise-resistant version of the Particle Swarm Optimization algorithm to optimize the weights of an embedded Artificial Neural Network, allowing the robot to learn obstacle avoidance behavior (a common benchmark for robotic learning techniques); we find that this technique appears to offer superior performance as compared to the canonical approach of using Genetic Algorithms for this type of learning. A method for faster learning using distributed evaluation in a robot team is tested and is found to offer comparable performance using only a fraction of the original learning time. This technique can be used for fast, effective learning and adaptation in a distributed robotic system performing search. The process of designing and analyzing algorithms for distributed robotic systems can be greatly facilitated if models are available to describe the dynamics of the algorithm at an abstract level. Inspired by previous examples in the distributed robotic field, we work to design a model of robotic search that captures the system at different levels of abstraction, ranging from accurately recreating the details of individual robots to describing the entire system as an indivisible whole. To capture the entire search process, we model both the exploration phase, where robots cover an environment in an effort to detect traces of targets, and the localization phase, where robots use target emission sensing to navigate towards the target. The utility of our models is demonstrated by using them to develop an effective technique for the declaration phase of search, where robots decide that a target has been accurately localized and announce its position. In distributed robotics research, it is important that techniques developed with abstracted simulations and models are ultimately validated using real robotic platforms in order to verify their correctness. In that spirit, we run systematic sound search experiments using teams of up to ten real robots. These experiments utilize the tools developed throughout the research process, demonstrate the utility of our learning technique for fast search adaptation, and serve to validate our models of distributed robotic localization. In addition, they allow us to analyze and better understand the subtle dynamics of the search process, providing information which should be useful for any future work on distributed robotic search.

The degree of intelligence built-in in today's vehicles in constantly on the rise. The vehicles are being equipped with sensors, with the goal to estimate the state of the vehicle and the environment surrounding it. Intelligent algorithms that process the sensory data can give their output at different levels, ranging from simple warnings, over evasive maneuvers (such as emergency braking), to complete autonomy. While it has been demonstrated that autonomous vehicles can rely solely on their on-board sensors, their performance can be optimized through cooperation with other road vehicles. Information coming from infrastructure can be fused in as well. This is where the communication between vehicles, as well as between vehicles and the infrastructure, comes into play. The main benefits of cooperation include larger coverage and extended situational awareness through sharing sensor data and vehicle intentions (trajectories). In this thesis, we address the cooperative perception problem. To solve this problem efficiently, we construct an end-to-end framework in three steps. First, we design an experimental platform that allows for testing our cooperative perception algorithms. In particular, we equip two fully electric Citroën C-ZERO cars with sensors, on-board computers and communication equipment. At the same time, we reproduce our platform in Webots, a high-fidelity simulation tool originally developed for mobile robots and recently upgraded for road vehicles. We develop and calibrate vehicle and sensor models with the goal to reproduce the real-world conditions as closely as possible, and in turn facilitate the deployment of algorithms developed in simulation on real cars. Second, we design two cooperative algorithms for tracking multiple objects (cars and pedestrians) using laser and camera sensors. The key components of the algorithms are our cooperative fusion methods, which allow for fusion of data obtained from a cooperative vehicle with the data obtained from on-board sensors. The algorithms are first evaluated in simulation and tested in specific scenarios. For instance, to showcase the power of our approach in a potential application, we design an overtaking decision algorithm that uses our cooperative perception algorithm as a baseline. The overtaking application proves the added-value of cooperative perception in situations with occluded or insufficient sensory field of view. Third, we deploy a selected algorithm on real vehicles and validate it in real time. A distributed software framework is designed for this purpose, enabling a relatively smooth transition from simulated to real environments. Moreover, the cooperative perception algorithm is subsequently enhanced for operating in more complex scenarios. Furthermore, we develop a cooperative localization method to achieve increased accuracy in cooperative vehicles' relative localization, thus enabling our cooperative perception algorithm to work properly when deployed on moving vehicles. Overall, we develop an end-to-end framework for cooperative perception, which unifies many different sensory technologies. Despite the end goal has always been that of deploying the framework on our test vehicles, we make substantial effort to keep it as general as possible. Our framework represents a stepping stone towards more complex, multi-vehicle automated systems.

Robotic sensor networks, at the junction between distributed robotics and wireless sensor networks, represent a strategic convergence between mobile and networked systems. In this thesis, we have begun to explore this crossover, and where possible, to bring tools, experience, and insight from the field of robotics to bear in the field of sensor networks. We present here a formal and general framework for the classification and construction of distributed intelligent controllers to facilitate implementation, understanding, and analysis, including a complete parameterized system description, and its corresponding generalized performance metrics. The methods shown are capable of uniquely and unambiguously describing any mechanism for distributed control of a robotic sensor network engaged in a monitoring task. A variety of simple distributed intelligent algorithms are illustrated within this framework, which introduce methods for activity control in time, space, and mobility. Appropriate tools, equipment, and controlled testing environments for systematic experimentation have been designed and built, both for a physical system and for corresponding experimentally validated simulations. The general methods presented are intended neither as an exhaustive collection of possible controllers, nor as a replacement for application-specific solutions, but as a flexible, reusable roadmap for system design allowing a user to make educated design choices systematically and rigorously while encoding available information into the provided template, adapting the control model to the constraints of any given specific scenario, accounting for issues of data quality, measurement, communication, mobility, or any combination of the above.

Bringing advantages of parallelism and robustness, distributed robotic systems have become an active subject of research since many years. Yet, the progress in that direction with Autonomous Underwater Vehicles (AUVs) has been limited. This project aims at developing a cooperative multi-AUV system for limnological surveying. Current underwater sensing techniques rely on manually deploying sensor probes, a tedious process that provides data at limited resolutions. In contrast, multiple robots equipped with measurement probes and operating in parallel can quickly obtain high-resolution 3D environmental snapshots – essential to capture small-scale, fast-changing phenomena. Several methods exist in literature for such cooperative sensing strategies with aerial and ground robots. However, underwater environments pose additional challenges, primarily with regard to navigation and communication. Considering the complexity and cost of existing solutions for large marine AUVs, a cooperative system with miniature AUVs calls for developing novel techniques. We aim to address the aforementioned challenges within this project. Subsequently, we intend to study how we can exploit in-water cooperation among several AUVs for improving navigation as well as quality and efficiency of the data gathering process.

The field of Unmanned Aerial Vehicles (UAVs), also known as drones, is rapidly growing, both in terms of size and of number of applications. Civil applications range from mapping, inspection, search and rescue, taking aerial footage, to art show, entertainment and more. Currently, most applications have a human pilot supervising or controlling the vehicles, but UAVs are expected to gain more autonomy with time. To fly in general airspace, used by both general and commercial aviation, a high level of autonomy is required from UAVs. A core functionality required to fly in general airspace is the UAVs' ability to detect and avoid collisions with other aircraft or objects. This functionality is handled by a so called Sense And Avoid (SAA) system. From among several sensors investigated to be used for a SAA system, a vision-based sensor is seen as a good candidate for a SAA system due to its ability to detect and identify a large variety of objects, as well as being close to the human's main mean to detect aircraft and other objects. To be as general as possible, this work focuses on non-cooperative algorithms that do not take assumptions on the motion of other aircraft. This thesis presents algorithms for a vision-based SAA system. It focuses on the relationship between sensing and avoidance, and how the limitations of one constrain the second. In particular, this thesis studies the consequences of the limited Field Of View (FOV) of a camera sensor on the collision avoidance algorithms. Given the assumptions above, the sensing and tracking of other UAVs is performed using cameras with fish-eye lenses that have a large enough FOV for the collision avoidance algorithms to guarantee to be collision-free. The detection of other UAVs is performed using two methods: a marker-based or a marker-less computer vision algorithms. Using the measurements from the computer vision algorithm, the positions and velocities of neighboring UAVs are tracked using a Gaussian mixture probability hypothesis density filter. This tracking algorithm is able to track multiple UAVs while requiring little computational resources, therefore representing a suitable candidate for on-board deployment. In this work, it is mathematically proven that the motion of an UAV has to be constrained according to the FOV of its sensor. Following that result, several collision avoidance algorithms are adapted to ensure collision-free navigation when used with a sensor with a limited FOV. Sensory limitations such as noise, lag, limited range and FOV, and their effects on the performance of collision avoidance algorithms are studied. Experimental work using high-fidelity simulation and real robots shows that algorithms that only use position information from the sensors are overall more reliable, although less efficient (in terms of distance traveled or trajectory smoothness) than algorithms that also use velocity estimates from the sensing system.

This thesis studies the automatic design and optimization of high-performing robust controllers for mobile robots using exclusively on-board resources. Due to the often large parameter space and noisy performance metrics, this constitutes an expensive optimization problem. Population-based learning techniques have been proven to be effective in dealing with noise and are thus promising tools to approach this problem. We focus this research on the Particle Swarm Optimization (PSO) algorithm, which, in addition to dealing with noise, allows a distributed implementation, speeding up the optimization process and adding robustness to failure of individual agents. In this thesis, we systematically analyze the different variables that affect the learning process for a multi-robot obstacle avoidance benchmark. These variables include algorithmic parameters, controller architecture, and learning and testing environments. The analysis is performed on experimental setups of increasing evaluation time and complexity: numerical benchmark functions, high-fidelity simulations, and experiments with real robots. Based on this analysis, we apply the distributed PSO framework to learn a more complex, collaborative task: flocking. This attempt to learn a collaborative task in a distributed manner on a large parameter space is, to our knowledge, the first of such kind. In addition, we address the problem of noisy performance evaluations encountered in these robotic tasks and present a %new distributed PSO algorithm for dealing with noise suitable for resource-constrained mobile robots due to its low requirements in terms of memory and limited local communication.

This dissertation describes a complete methodological framework for designing, modeling and optimizing a specific class of distributed systems whose dynamics result from the multiple, stochastic interactions of their constitutive components. These components can be robots endowed with very minimal capabilities, or even simpler entities such as insects, bacteria, particles, or molecules. We refer to such components as Smart Minimal Particles (SMPs). One of the main difficulties facing the modeling of SMPs is the potential complexity and richness of their dynamics. On the one hand, one needs detailed models that account for the physico-chemical properties of the lower-level components (e.g., shape, material, surface chemistry, charge, etc.), which, in turn, determine the nature and the magnitude of their interactions. On the other hand, one is also interested in models that can yield accurate numerical predictions of macroscopic quantities, and investigate formally their dependence on the system’s design and control parameters. These competing requirements motivate a combination of models at multiple levels of abstraction, as advocated by the Multi-Level Modeling Methodology (MLMM), which was introduced in prior works. The MLMM enables the fulfillment of both requirements in a very efficient way by incrementally building up models at increasing levels of abstraction in order to capture the relevant features of the system. This thesis extends and consolidates the MLMM along several axes. In a first step, we provide a theoretical consolidation of the MLMM. We propose a thorough classification of the different models of SMPs, and we discuss their underlying assumptions and simplifications. We shed light on the fundamental impact of embodiment and spatiality on models’ accuracy, and we define the conditions under which the macro-deterministic approximation is valid. These theoretical considerations are experimentally supported by five case studies of aggregation and Self-Assembly (SA) at different scales. The five case studies utilize three types of components: (i) miniature wheeled robots (Alice, 2 cm in size) endowed with limited computation, sensing, actuation, and communication capabilities, (ii) water-floating passive modules (Lily, 3 cm in size) endowed with four permanent magnets for mutual latching, and (iii) micro-fabricated cylinders (about 100 μm in diameter, studied in realistic simulation only) that can achieve SA in liquids. In a second step, we introduce the core contribution of this thesis, that is, a systematic and generic methodology for bridging the gap between real, physical systems and computationally efficient models at multiple abstraction levels. In particular, we describe the M3 computational framework, which enables the automated construction of models of SMPs. Our approach consists in observing (or simulating realistically) a system of interest, and building a hierarchical suite of models based on the observations (i.e., trajectories) collected during these experiments (or simulations). Internally, the framework first builds up a microscopic representation of the system based on these observations and on a list of interactions of interest specified by the user. This representation, called the Canonical Microscopic Model (CMM), is a formal and generic description of SMPs, and it serves as a blueprint for the construction of a macroscopic model, specified using the Chemical Reaction Network (CRN) formalism. The rates of the CRN are finally calibrated using a Maximum Likelihood Estimation (MLE) scheme. We validate the M3 framework on each of the three platforms discussed earlier, thereby illustrating its relevance both as a modeling and as an analysis tool. Finally, we discuss the role of multi-level modeling when designing and optimizing SMPs. In particular, we show that top-down model-based design of multi-robot systems is generally not amenable to efficient implementations when dealing with very resource-constrained robots. Instead, faithful and computationally efficient models built incrementally from the bottom up prove to be an essential tool for designing such systems. We further corroborate this claim by applying our automated modeling framework to the real-time control of the stochastic SA of Lily modules. Our scientific contribution is therefore three-fold. First, we provide a solid experimental and theoretical consolidation of the MLMM, which has been the subject of intense research efforts for the last decade. Second, we propose, for the first time, an approach to generate models at high abstraction level in a completely automated fashion, based solely on observations of the system of interest. Third, we provide deep insights into the modeling and the design of SMPs, with a specific emphasis on self-assembling systems ranging from the centimeter scale down to the micrometer scale.

Localizing sources of airborne chemicals with mobile sensing systems finds applications in various crucial and perilous situations, such as safety and security investigation for detecting explosives or illegal drugs, search and rescue operations to locate survivors in the aftermath of natural hazards, or environmental monitoring in unsafe sites, following harmful leaks. Using autonomous robots in such situations would eliminate or, at least, reduce human intervention and keep them from harm. Additionally, such operations would be more cost-effective and more time-efficient. That is why, in the past 30 years, gas source localization has been an attractive research topic in robotics and related areas, where different methods have been designed and evaluated for this purpose. However, the inherent complexity of gas dispersal phenomena, which is non-trivial to analyze and predict, especially in complex environments, is the main source of challenges in this field. Therefore, researchers tend to design and evaluate algorithms in simplistic environments before tackling more complex ones. In this thesis, we have designed and investigated a gas source localization algorithm based on source term estimation with a probabilistic approach. After validating the performance of the method in a baseline environment using a wheeled-robot, we gradually enhanced our method by enriching it with new features in order to be adaptable to more complex scenarios. In particular, the algorithm was shown to be successful in a simplified three-dimensional setup as well as in an unknown environment where no global map and positioning system is available. Furthermore, it was deployed on a homogeneous multi-robot system, where different coordination strategies between robots were designed and studied. Finally, designing a data-driven plume model and integrating it to the main framework of the method allowed for adaptation to cluttered environments. The method is systematically evaluated through high-fidelity simulations and in a wind tunnel emulating realistic and repeatable conditions. Lastly, the performance of our algorithm was compared with other state-of-the art methods to show its potentials and limits.

We provide a comparison of a series of original coordination mechanisms for the distributed boundary coverage problem with a swarm of miniature robots. Our analysis is based on real robot experimentation and models at different levels of abstraction. Distributed boundary coverage is an instance of the distributed coverage problem and has applications such as inspection of structures, de-mining, cleaning, and painting. Coverage is a particularly good example for the benefits of a multi-robot approach due to the potential for parallel task execution and additional robustness out of redundancy. The constraints imposed by a potential application, the autonomous inspection of a jet turbine engine, were our motivation for the algorithms considered in this thesis. Thus, there is particular emphasis on how algorithms perform under the influence of sensor and actuator noise, limited computational and communication capabilities, as well as on the policies about how to cope with such problems. The algorithms developed in this dissertation can be classified into reactive and deliberative algorithms, as well as non-collaborative and collaborative algorithms. The performance of these algorithms ranges from very low to very high, corresponding to highly redundant coverage to near-optimal partitioning of the environments, respectively. At the same time, requirements and assumptions on the robotic platform and the environment (from no communication to global communication, and from no localization to global localization) are incrementally raised. All the algorithms are robust to sensor and actuator noise and gracefully decay to the performance of a randomized algorithm as a function of an increased noise level and/or additional hardware constraints. Although the deliberative algorithms are fully deterministic, the actual performance is probabilistic due to inevitable sensor and actuator noise. For this reason, probabilistic models are used for predicting time to complete coverage and take into account sensor and actuator noise calibrated by using real hardware. For reactive systems with limited memory, the performance is captured using a compact representation based on rate equations that track the expected number of robots in a certain state. As the number of states explode for the deliberative algorithms that require a substantial use of memory, this approach becomes less tractable with the amount of deliberation performed, and we use Discrete Event System (DES) simulation in these cases. Our contribution to the domain of multi-robot systems is three-fold. First, we provide a methodology for system identification and optimal control of a robot swarm using probabilistic models. Second, we develop a series of algorithms for distributed coverage by a team of miniature robots that gracefully decay from a near-optimal performance to the performance of a randomized approach under the influence of sensor and actuator noise. Third, we design an implement a miniature inspection platform based on the miniature robot Alice with ZigBee ready communication capabilities and color vision on a foot-print smaller than 2 × 2 × 3 cm3.

This thesis focuses on the analysis of the trajectories of a mobile agent. It presents different techniques to acquire a quantitative measure of the difference between two trajectories or two trajectory datasets. A novel approach is presented here, based on the Point Distribution Model (PDM). This model was developed by computer vision scientists to compare deformable shapes. This thesis presents the mathematical reformulation of the PDM to fit spatiotemporal data, such as trajectory information. The behavior of a mobile agent can rarely be represented by a unique trajectory, as its stochastic component will not be taken into account. Thus, the PDM focuses on the comparison of trajectory datasets. If the difference between datasets is greater than the variation within each dataset, it will be observable in the first few dimensions of the PDM. Moreover, this difference can also be quantified using the inter-cluster distance defined in this thesis. The resulting measure is much more efficient than visual comparisons of trajectories, as are often made in existing scientific literature. This thesis also compares the PDM with standard techniques, such as statistical tests, Hidden Markov Models (HMMs) or Correlated Random Walk (CRW) models. As a PDM is a linear transformation of space, it is much simpler to comprehend. Moreover, spatial representations of the deformation modes can easily be constructed in order to make the model more intuitive. This thesis also presents the limits of the PDM and offers other solutions when it is not adequate. From the different results obtained, it can be pointed out that no universal solution exists for the analysis of trajectories, however, solutions were found and described for all of the problems presented in this thesis. As the PDM requires that all the trajectories consist of the same number of points, techniques of resampling were studied. The main solution was developed for trajectories generated on a track, such as the trajectory of a car on a road or the trajectory of a pedestrian in a hallway. The different resampling techniques presented in this thesis provide solutions to all the experimental setups studied, and can easily be modified to fit other scenarios. It is however very important to understand how they work and to tune their parameters according to the characteristics of the experimental setup. The main principle of this thesis is that analysis techniques and data representations must be appropriately selected with respect to the fundamental goal. Even a simple tool such as the t-test can occasionally be sufficient to measure trajectory differences. However, if no dissimilarity can be observed, it does not necessarily mean that the trajectories are equal – it merely indicates that the analyzed feature is similar. Alternatively, other more complex methods could be used to highlight differences. Ultimately, two trajectories are equal if and only if they consist of the exact same sequence of points. Otherwise, a difference can always be found. Thus, it is important to know which trajectory features have to be compared. Finally, the diverse techniques used in this thesis offer a complete methodology to analyze trajectories.