A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions

Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss their implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer

The convective heat transfer in a turbulent boundary layer (TBL) on a flat plate is enhanced using an artificial intelligence approach based on linear genetic algorithms control (LGAC). The actuator is a set of six slot jets in crossflow aligned with the freestream. An open-loop optimal periodic forcing is defined by the carrier frequency, the duty cycle and the phase difference between actuators as control parameters. The control laws are optimised with respect to the unperturbed TBL and to the actuation with a steady jet. The cost function includes the wall convective heat transfer rate and the cost of the actuation. The performance of the controller is assessed by infrared thermography and characterised also with particle image velocimetry measurements. The optimal controller yields a slightly asymmetric flow field. The LGAC algorithm converges to the same frequency and duty cycle for all the actuators. It is noted that such frequency is strikingly equal to the inverse of the characteristic travel time of large-scale turbulent structures advected within the near-wall region. The phase difference between multiple jet actuation has shown to be very relevant and the main driver of flow asymmetry. The results pinpoint the potential of machine learning control in unravelling unexplored controllers within the actuation space. Our study furthermore demonstrates the viability of employing sophisticated measurement techniques together with advanced algorithms in an experimental investigation.Comment: 20 pages, 13 figure

(Global) Optimization: Historical notes and recent developments

Recent developments in (Global) Optimization are surveyed in this paper. We collected and commented quite a large number of recent references which, in our opinion, well represent the vivacity, deepness, and width of scope of current computational approaches and theoretical results about nonconvex optimization problems. Before the presentation of the recent developments, which are subdivided into two parts related to heuristic and exact approaches, respectively, we briefly sketch the origin of the discipline and observe what, from the initial attempts, survived, what was not considered at all as well as a few approaches which have been recently rediscovered, mostly in connection with machine learning

Mesh-Derived Image Partition for 3D-2D Registration in Image-Guided Interventions

RÉSUMÉ Les interventions guidées par images effectuées sous modalité 2D bénéficient de la superposition d'images 3D prises en stage préopératoire. La technologie nécessaire pour cette superposition est le recalage 3D-2D, qui consiste à trouver la position et l'orientation de l'image préopératoire 3D par rapport aux images intraopératoires 2D. Une intégration adéquate d'un algorithme de recalage à un processus chirurgical a le potentiel d'avoir un impact positif sur l'issue de la chirurgie et la durée de l'intervention. Cependant, beaucoup de chirurgies sont effectuées sans l'assistance du recalage, car aucune des solutions actuelles n’est applicable dans leur contexte clinique spécifique. Pour remédier à cette situation, cette thèse porte sur la recherche de solutions pratiques applicables à des interventions guidées par images spécifiques. La première chirurgie étudiée est l'ablation par cathéter pour fibrillation atriale/auriculaire (AC pour FA) effectuée sous fluoroscopie rayons X, une procédure électrophysiologique traitant l'arythmie cardiaque. Dans cette chirurgie, une image volumétrique (soit résonance magnétique (RM) ou tomodensitométrie (TDM)) est prise avant l'opération pour définir l'anatomie de l'atrium gauche (AG) et des veines pulmonaires (VP)s. Un maillage segmenté de ce volume est ensuite utilisé pour offrir un support visuel intraopératoire lors du placement du cathéter d'ablation via sa superposition aux images fluoroscopiques. Cependant, les solutions de recalage actuelles sont trop lentes et requièrent des interventions manuelles, ce qui est problématique quand un recalage intraopératoire est nécessaire pour permettre de pallier aux mouvements du patient. Aussi, les solutions automatiques actuelles qui recalent les images 3D et 2D directement, sans passer par l'identification manuelle de points ficudiaux, ne sont pas assez précises pour être cliniquement utilisables. De plus, les solutions qui n'utilisent pas la cartographie électromagnétique ne fonctionnent pas avec les modalités RM/fluoroscopie rayons X. Ceci est un problème, car nous visons les interventions de AC pour FA qui utilisent la modalité RM sans la cartographie électromagnétique. Il y a deux défis principaux pour arriver à une solution utile cliniquement. Premièrement, résoudre le difficile problème du recalage RM/fluoroscopie complexifié dans le cas de AC pour FA à cause de la correspondance partielle entre les modalités au niveau des VPs. Deuxièmement, de faire ce recalage assez rapidement pour permettre une mise à jour intraopératoire en temps réel dans les cas où le patient bouge pendant l'opération. Afin de remédier à cette situation, nous introduisons une nouvelle méthode de recalage basée sur la partition d'image dérivée d'un maillage (recalage PIDM). Cette méthode utilise les projections d'un maillage segmenté de la modalité 3D pour inférer une segmentation des images fluoroscopiques 2D. Ceci est beaucoup plus rapide que de faire des projections volumétriques et, puisque le maillage peut être segmenté d'une image RM ou TDM sans distinction, la même procédure est valide pour les deux modalités. La justesse du recalage est évaluée par des mesures de similarité qui comparent les propriétés statistiques des zones segmentées et incorporent l'information de profondeur des maillages afin de tenir compte de la correspondance partielle au niveau des VPs. Nous validons l'algorithme de recalage PIDM sur des interventions chirurgicales de AC pour FA provenant de 7 patients différents. Quatre mesures de similarité basées sur le principe de la partition à partir du maillage sont introduites et mises à l'épreuve sur 1400 cas biplans chacune. La précision, la portée et la robustesse de la solution sont évaluées en calculant la distribution de l'erreur (distance de projection) en fonction de la justesse de la pose initiale pour chacun des 5600 recalages. La précision est également évaluée de manière visuelle, en superposant les résultats du recalage et les valeurs-vérité sur les images fluoroscopiques. Pour donner une juste appréciation de la performance attendue de notre algorithme, les exemples visuels sont tirés de cas représentant l'erreur moyenne ainsi que d'un écart-type au-dessus et en dessous. Afin d'évaluer l'extension du recalage PIDM à d'autres types de chirurgies, celui-ci est appliqué à des cas de sclérothérapie de malformation veineuse (SdMV). Ce type de chirurgie est particulièrement délicat à recaler car la malformation peut être présente sur toutes les parties du corps, ce qui offre peu de prévisibilité sur les propriétés des images médicales à recaler d'un patient à l'autre. De plus, cette chirurgie est effectuée en imagerie monoplan et les données ne sont pas accompagnées de méta-information permettant la calibration géométrique du système. Nous démontrons que le recalage PIDM est applicable aux cas de SdMV, mais doit être modifié pour être applicable à la grande variété de parties du corps où les malformations veineuses peuvent être présentes. Le protocole développé pour les chirurgies de AC pour FA peut être utilisé dans les cas où une embolisation ou une démarcation intérieure/extérieure d'une partie du corps est proéminente, mais il est nécessaire d'intégrer l'information de gradients dans les mesures de similarité pour recaler les organes où les os sont prédominants.----------ABSTRACT Image-guided interventions conducted under a 2D modality benefit from the overlay of relevant 3D information from the preoperative stage. The enabling technology for this overlay is 3D-2D registration: the process of finding the spatial pose of a 3D preoperative image in relation to 2D intraoperative images. The successful integration of a registration solution to a surgery has the potential for significant positive impact in terms of likelihood of treatment success and intervention duration. However, many surgeries are routinely done without the assistance of registration because no current solution is practical in their clinical context. In order to remedy these issues, we focus on producing practical, targeted registration solutions to assist image-guided interventions. The first surgery we address is catheter ablation for atrial fibrillation (CA for AF), an electrophysiology procedure to treat heart arrhythmia conducted under X-ray fluoroscopy. In this surgery, a 3D image, either magnetic resonance (MR) or computed tomography (CT), is taken preoperatively to define the anatomy of the left atrium (LA) and pulmonary veins (PV)s. A mesh, segmented from the 3D image, is subsequently used to help positioning the ablation catheter via its overlay on the intraoperative fluoroscopic images. Current clinical registration solutions for CA for AF are slow and often require extensive manual manipulations such as the identification of fiducial points, which is problematic when intraoperative updates of the 3D image's pose are required because of patient movement. The automatic solutions are currently not precise enough to be used clinically. Also, the solutions which do not involve electroanatomic mapping are not suitable for MR/fluoroscopy registration. This is problematic since we target CA for AF interventions where the 3D modality is MR and electroanatomic mapping is not used. There are two principal challenges to overcome in order to provide a clinically useful registration algorithm. First, solving the notoriously hard MR to X-ray fluoroscopy registration problem which is further complicated in cases of CA for AF because of the partial match between modalities at the level of the PVs. Second, solving the registration quickly enough to allow for intraoperative updates required due to the patient's movement. We introduce a new registration methodology based on mesh-derived image partition (MDIP) which uses projections of a mesh segmented from the 3D image in order to infer a segmentation of the 2D X-ray fluoroscopy images. This is orders of magnitude faster than producing volumetric projections and, since the mesh can be segmented from either MR or CT, the same procedure is valid for both modalities. The fitness of the registration is evaluated by custom-built similarity measures that compare the statistical properties of the segmented zones and incorporates mask-depth information to account for the partial match at the level of the PVs. We validate the MDIP algorithm on 7 cases of patients undergoing CA for AF surgery. Four MDIP-based similarity measures are introduced; each one is validated on 1400 biplane registrations. The precision, range, speed and robustness of the solution is assessed by calculating the distribution of projection distance error in function of the correctness of the initial pose for all 5600 biplane registrations. The precision is also evaluated visually by overlaying the ground-truths with results from the registration algorithm. To give a fair appraisal of the expected behavior, the examples are taken from cases exemplifying the average error measured as well as one standard deviation above and under. The registration algorithm is also applied to cases of sclerotherapy for venous malformation (SfVM) in order to assess its portability to other type of surgeries. SfVM are especially challenging because the malformation can be present on any body part, which offers little predictability on the properties of the medical images from one patient to another. Our dataset is sampled from monoplane surgeries and did not come with metadata allowing a geometrical calibration of the system. We demonstrate that MDIP-based registration is applicable to cases of monoplane SfVM, but that modifications are required in order to account for the wide variety of body parts where VMs are common. The protocol developed for CA for AF surgeries can be used for embolizations or when the interior/exterior border of the organ is prominent, but gradient information has to be taken into account by the similarity measures in order to properly register cases where bones are predominant

Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective

Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage. In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient. The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions. Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Dense Vision in Image-guided Surgery

Image-guided surgery needs an efficient and effective camera tracking system in order to perform augmented reality for overlaying preoperative models or label cancerous tissues on the 2D video images of the surgical scene. Tracking in endoscopic/laparoscopic scenes however is an extremely difficult task primarily due to tissue deformation, instrument invasion into the surgical scene and the presence of specular highlights. State of the art feature-based SLAM systems such as PTAM fail in tracking such scenes since the number of good features to track is very limited. When the scene is smoky and when there are instrument motions, it will cause feature-based tracking to fail immediately. The work of this thesis provides a systematic approach to this problem using dense vision. We initially attempted to register a 3D preoperative model with multiple 2D endoscopic/laparoscopic images using a dense method but this approach did not perform well. We subsequently proposed stereo reconstruction to directly obtain the 3D structure of the scene. By using the dense reconstructed model together with robust estimation, we demonstrate that dense stereo tracking can be incredibly robust even within extremely challenging endoscopic/laparoscopic scenes. Several validation experiments have been conducted in this thesis. The proposed stereo reconstruction algorithm has turned out to be the state of the art method for several publicly available ground truth datasets. Furthermore, the proposed robust dense stereo tracking algorithm has been proved highly accurate in synthetic environment (< 0.1 mm RMSE) and qualitatively extremely robust when being applied to real scenes in RALP prostatectomy surgery. This is an important step toward achieving accurate image-guided laparoscopic surgery.Open Acces