Omnidirectional Sensory and Motor Volumes in Electric Fish

Active sensing organisms, such as bats, dolphins, and weakly electric fish, generate a 3-D space for active sensation by emitting self-generated energy into the environment. For a weakly electric fish, we demonstrate that the electrosensory space for prey detection has an unusual, omnidirectional shape. We compare this sensory volume with the animal's motor volume—the volume swept out by the body over selected time intervals and over the time it takes to come to a stop from typical hunting velocities. We find that the motor volume has a similar omnidirectional shape, which can be attributed to the fish's backward-swimming capabilities and body dynamics. We assessed the electrosensory space for prey detection by analyzing simulated changes in spiking activity of primary electrosensory afferents during empirically measured and synthetic prey capture trials. The animal's motor volume was reconstructed from video recordings of body motion during prey capture behavior. Our results suggest that in weakly electric fish, there is a close connection between the shape of the sensory and motor volumes. We consider three general spatial relationships between 3-D sensory and motor volumes in active and passive-sensing animals, and we examine hypotheses about these relationships in the context of the volumes we quantify for weakly electric fish. We propose that the ratio of the sensory volume to the motor volume provides insight into behavioral control strategies across all animals

Mobile robotic network deployment for intruder detection and tracking

This thesis investigates the problem of intruder detection and tracking using mobile robotic networks. In the first part of the thesis, we consider the problem of seeking an electromagnetic source using a team of robots that measure the local intensity of the emitted signal. We propose a planner for a team of robots based on Particle Swarm Optimization (PSO) which is a population based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this thesis. Our results are also validated on an experimental testbed. In the second part, we present a game theoretic framework for visibility-based target tracking in multi-robot teams. A team of observers (pursuers) and a team of targets (evaders) are present in an environment with obstacles. The objective of the team of observers is to track the team of targets for the maximum possible time. While the objective of the team of targets is to escape (break line-of-sight) in the minimum time. We decompose the problem into two layers. At the upper level, each pursuer is allocated to an evader through a minimum cost allocation strategy based on the risk of each evader, thereby, decomposing the agents into multiple single pursuer-single evader pairs. Two decentralized allocation strategies are proposed and implemented in this thesis. At the lower level, each pursuer computes its strategy based on the results of the single pursuer-single evader target-tracking problem. We initially address this problem in an environment containing a semi-infinite obstacle with one corner. The pursuer\u27s optimal tracking strategy is obtained regardless of the evader\u27s strategy using techniques from optimal control theory and differential games. Next, we extend the result to an environment containing multiple polygonal obstacles. We construct a pursuit field to provide a guiding vector for the pursuer which is a weighted sum of several component vectors. The performance of different combinations of component vectors is investigated. Finally, we extend our work to address the case when the obstacles are not polygonal, and the observers have constraints in motion

Integration and adaptation of a 3D reactive navigator in the ROS robotic architecture

Debido a sus altos costes, el difícil acceso a componentes, así como la dificultad de los problemas de ingeniería que surgen en ella, no resulta viable dedicarse al campo de la Robótica sin atajarla aunando esfuerzos e inversión entre muchos profesionales de la investigación alrededor del mundo. El open source es fundamental más que nunca como filosofía para la transmisión de los últimos avances en cuanto a software robótico. ROS ofrece la plataforma principal, más conocida, con más paquetes y documentación y la posibilidad de contactar con profesionales. Uno de los problemas principales de la Robótica es el problema de la navegación hacia una meta. Existen multitud de estrategias para llevarla a cabo, pero en general, se sigue un enfoque divide y vencerás en el que cada subproblema del mismo se resuelve por separado. El presente TFG tiene como objetivo integrar una aplicación de navegación reactiva, esto es, sensorial, sin memoria, con el stack tecnológico de ROS dedicado a la navegación. Además, este trabajo pretende ofrecer una revisión bibliográfica de los algoritmos de navegación reactiva más relevantes y una fase de experimentación con unos resultados. Finalmente, el planteamiento de cómo extender el motor reactivo para soportar 3 dimensiones estructuradas en bloques

Implications of Motion Planning: Optimality and k-survivability

We study motion planning problems, finding trajectories that connect two configurations of a system, from two different perspectives: optimality and survivability. For the problem of finding optimal trajectories, we provide a model in which the existence of optimal trajectories is guaranteed, and design an algorithm to find approximately optimal trajectories for a kinematic planar robot within this model. We also design an algorithm to build data structures to represent the configuration space, supporting optimal trajectory queries for any given pair of configurations in an obstructed environment. We are also interested in planning paths for expendable robots moving in a threat environment. Since robots are expendable, our goal is to ensure a certain number of robots reaching the goal. We consider a new motion planning problem, maximum k-survivability: given two points in a stochastic threat environment, find n paths connecting two given points while maximizing the probability that at least k paths reach the goal. Intuitively, a good solution should be diverse to avoid several paths being blocked simultaneously, and paths should be short so that robots can quickly pass through dangerous areas. Finding sets of paths with maximum k-survivability is NP-hard. We design two algorithms: an algorithm that is guaranteed to find an optimal list of paths, and a set of heuristic methods that finds paths with high k-survivability

Contributions to shared control and coordination of single and multiple robots

L’ensemble des travaux présentés dans cette habilitation traite de l'interface entre un d'un opérateur humain avec un ou plusieurs robots semi-autonomes aussi connu comme le problème du « contrôle partagé ».Le premier chapitre traite de la possibilité de fournir des repères visuels / vestibulaires à un opérateur humain pour la commande à distance de robots mobiles.Le second chapitre aborde le problème, plus classique, de la mise à disposition à l’opérateur d’indices visuels ou de retour haptique pour la commande d’un ou plusieurs robots mobiles (en particulier pour les drones quadri-rotors).Le troisième chapitre se concentre sur certains des défis algorithmiques rencontrés lors de l'élaboration de techniques de coordination multi-robots.Le quatrième chapitre introduit une nouvelle conception mécanique pour un drone quadrirotor sur-actionné avec pour objectif de pouvoir, à terme, avoir 6 degrés de liberté sur une plateforme quadrirotor classique (mais sous-actionné).Enfin, le cinquième chapitre présente une cadre général pour la vision active permettant, en optimisant les mouvements de la caméra, l’optimisation en ligne des performances (en terme de vitesse de convergence et de précision finale) de processus d’estimation « basés vision »

Obstacle distance for car-like robots

This paper shows how to compute the nonholonomic distance between a point-wise car-like robot and polygonal obstacles, Geometric constructions to compute the shortest paths from a configuration (given orientation and position in the plane of the robot) to a position (i.e., a configuration with unspecified final orientation) are first presented. The geometric structure of the reachable set (set of points in the plane reachable by paths of given length l) is then used to compute the shortest paths to straight-line segments. Obstacle distance is defined as the length of such shortest paths. The algorithms are developed for robots that can move both forward and backward (Reeds&Shepp's car) or only forward (Dubins' car). They are based on the convexity analysis of the reachable set

Obstacle Distance for Car-Like Robots

This paper shows how to compute the nonholonomic distance between a point-wise car-like robot and polygonal obstacles. Geometric constructions to compute the shortest paths from a configuration (given orientation and position in the plane of the robot) to a position (i.e., a configuration with unspecified final orientation) are first presented. The geometric structure of the reachable set (set of points in the plane reachable by paths of given length #) is then used to compute the shortest paths to straight-line segments. Obstacle distance is defined as the length of such shortest paths. The algorithms are developed for robots that can move both forward and backward (Reeds&Shepp's car) or only forward (Dubins' car). They are based on the convexity analysis of the reachable set. Keywords--- Car-like robots, shortest paths, nonholonomic distance. I. Introduction Distance computation plays a crucial role in robot motion planning. Numerous motion planning algorithms rely on obstacle dis..