Timed trajectory generation combined with an Extended Kalman Filter for a vision-based autonomous mobile robot

Series : Advances in intelligent systems and computing, vol. 193, ISSN 2194-5357Planning collision-free trajectories requires the combination of generation and modulation techniques. This is especially important if temporal stabilization of the generated trajectories is considered. Temporal stabilization means to conform to the planned movement time, in spite of environmental conditions or perturbations. This timing problem has not been addressed in most current robotic systems, and it is critical in several robotic tasks such as sequentially structured actions or human-robot interaction. This work focuses on generating trajectories for a mobile robot, whose goal is to reach a target within a constant time, independently of the world complexity. Trajectories are generated by nonlinear dynamical systems. Herein, we extend our previous work by including an Extended Kalman Filter (EKF) to estimate the target location relative to the robot. A simulated hospital environment and a Pioneer 3-AT robot are used to demonstrate the robustness and reliability of the proposed approach in cluttered, dynamic and uncontrolled scenarios. Multiple experiments confirm that the inclusion of the EKF preserves the timing properties of the overall architecture.Work supported by the Portuguese Science Foundation (grant PTDC/EEA-CRO/100655/2008), and by project FCT PEst-OE/EEI/LA0009/2011. Jorge B. Silva is supported by PhD Grant SFRH/BD/68805/2010, granted by the Portuguese Science Foundation

Generating timed trajectories foran autonomous robot

Tese de Doutoramento Programa Doutoral em Engenharia Electrónica e ComputadoresThe inclusion of timed movements in control architectures for mobile navigation has received an increasing attention over the last years. Timed movements allow modulat- ing the behavior of the mobile robot according to the elapsed time, such that the robot reaches a goal location within a specified time constraint. If the robot takes longer than expected to reach the goal location, its linear velocity is increased for compen- sating the delay. Timed movements are also relevant when sequences of missions are considered. The robot should follow the predefined time schedule, so that the next mission is initiated without delay. The performance of the architecture that controls the robot can be validated through simulations and field experiments. However, ex- perimental tests do not cover all the possible solutions. These should be guided by a stability analysis, which might provide directions to improve the architecture design in cases of inadequate performance of the architecture. This thesis aims at developing a navigation architecture and its stability analysis based on the Contraction Theory. The architecture is based on nonlinear dynamical systems and must guide a mobile robot, such that it reaches a goal location within a time constraint while avoiding unexpected obstacles in a cluttered and dynamic real environment. The stability analysis based on the Contraction Theory might provide conditions to the dynamical systems parameters, such that the dynamical systems are designed as contracting, ensuring the global exponential stability of the architecture. Furthermore, Contraction Theory provides solutions to analyze the success of the mis- sion as a stability problem. This provides formal results that evaluate the performance of the architecture, allowing the comparison to other navigation architectures. To verify the ability of the architecture to guide the mobile robot, several experi- mental tests were conducted. The obtained results show that the proposed architecture is able to drive mobile robots with timed movements in indoor environments for large distances without human intervention. Furthermore, the results show that the Con- traction Theory is an important tool to design stable control architectures and to analyze the success of the robotic missions as a stability problem.A inclusão de movimentos temporizados em arquitecturas de controlo para navegação móvel tem aumentado ao longo dos últimos anos. Movimentos temporizados permitem modular o comportamento do robô de tal forma que ele chegue ao seu destino dentro de um tempo especificado. Se o robô se atrasar, a sua velocidade linear deve ser aumen- tada para compensar o atraso. Estes movimentos são também importantes quando se consideram sequências de missões. O robô deve seguir o escalonamento da sequência, de tal forma que a próxima missão seja iniciada sem atraso. O desempenho da arqui- tectura pode ser validado através de simulações e experiências reais. Contudo, testes experimentais não cobrem todas as possíveis soluções. Estes devem ser conduzidos por uma análise de estabilidade, que pode fornecer direcções para melhorar o desempenho da arquitectura. O objectivo desta tese é desenvolver uma arquitectura de navegação e analisar a sua estabilidade através da teoria da Contracção. A arquitectura é baseada em sistemas dinâmicos não lineares e deve controlar o robô móvel num ambiente real, desordenado e dinâmico, de tal modo que ele chegue à posição alvo dentro de uma restrição de tempo especificada. A análise de estabilidade baseada na teoria da Contracção pode fornecer condições aos parâmetros dos sistemas dinâmicos de modo a desenha-los como contracções, e assim garantir a estabilidade exponencial global da arquitectura. Esta teoria fornece ainda soluções interessantes para analisar o sucesso da missão como um problema de estabilidade. Isto providencia resultados formais que avaliam o desem- penho da arquitectura e permitem a comparação com outras arquitecturas. Para verificar a habilidade da arquitectura em controlar o robô móvel, foram con- duzidos vários testes experimentais. Os resultados obtidos mostram que a arquitectura proposta é capaz de controlar robôs móveis com movimentos temporizados em ambi- entes interiores durante grandes distâncias e sem intervenção humana. Além disso, os resultados mostram que a teoria da Contracção é uma ferramenta importante para desenhar arquitecturas de controlo estáveis e para analisar o sucesso das missões efec- tuadas pelo robô como um problema de estabilidade.Portuguese Science and Technology Foundation (FCT) SFRH/BD/68805/2010

Adaptive Perception, State Estimation, and Navigation Methods for Mobile Robots

In this cumulative habilitation, publications with focus on robotic perception, self-localization, tracking, navigation, and human-machine interfaces have been selected. While some of the publications present research on a PR2 household robot in the Robotics Learning Lab of the University of California Berkeley on vision and machine learning tasks, most of the publications present research results while working at the AutoNOMOS-Labs at Freie Universität Berlin, with focus on control, planning and object tracking for the autonomous vehicles "MadeInGermany" and "e-Instein"

Trajectory generation for lane-change maneuver of autonomous vehicles

Lane-change maneuver is one of the most thoroughly investigated automatic driving operations that can be used by an autonomous self-driving vehicle as a primitive for performing more complex operations like merging, entering/exiting highways or overtaking another vehicle. This thesis focuses on two coherent problems that are associated with the trajectory generation for lane-change maneuvers of autonomous vehicles in a highway scenario: (i) an effective velocity estimation of neighboring vehicles under different road scenarios involving linear and curvilinear motion of the vehicles, and (ii) trajectory generation based on the estimated velocities of neighboring vehicles for safe operation of self-driving cars during lane-change maneuvers. ^ We first propose a two-stage, interactive-multiple-model-based estimator to perform multi-target tracking of neighboring vehicles in a lane-changing scenario. The first stage deals with an adaptive window based turn-rate estimation for tracking maneuvering target vehicles using Kalman filter. In the second stage, variable-structure models with updated estimated turn-rate are utilized to perform data association followed by velocity estimation. Based on the estimated velocities of neighboring vehicles, piecewise Bezier-curve-based methods that minimize the safety/collision risk involved and maximize the comfort ride have been developed for the generation of desired trajectory for lane-change maneuvers. The proposed velocity-estimation and trajectory-generation algorithms have been validated experimentally using Pioneer3- DX mobile robots in a simulated lane-change environment as well as validated by computer simulations

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Sensors and Technologies in Spain: State-of-the-Art

The aim of this special issue was to provide a comprehensive view on the state-of-the-art sensor technology in Spain. Different problems cause the appearance and development of new sensor technologies and vice versa, the emergence of new sensors facilitates the solution of existing real problems. [...

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

Planetary rovers and data fusion

This research will investigate the problem of position estimation for planetary rovers. Diverse algorithmic filters are available for collecting input data and transforming that data to useful information for the purpose of position estimation process. The terrain has sandy soil which might cause slipping of the robot, and small stones and pebbles which can affect trajectory. The Kalman Filter, a state estimation algorithm was used for fusing the sensor data to improve the position measurement of the rover. For the rover application the locomotion and errors accumulated by the rover is compensated by the Kalman Filter. The movement of a rover in a rough terrain is challenging especially with limited sensors to tackle the problem. Thus, an initiative was taken to test drive the rover during the field trial and expose the mobile platform to hard ground and soft ground(sand). It was found that the LSV system produced speckle image and values which proved invaluable for further research and for the implementation of data fusion. During the field trial,It was also discovered that in a at hard surface the problem of the steering rover is minimal. However, when the rover was under the influence of soft sand the rover tended to drift away and struggled to navigate. This research introduced the laser speckle velocimetry as an alternative for odometric measurement. LSV data was gathered during the field trial to further simulate under MATLAB, which is a computational/mathematical programming software used for the simulation of the rover trajectory. The wheel encoders came with associated errors during the position measurement process. This was observed during the earlier field trials too. It was also discovered that the Laser Speckle Velocimetry measurement was able to measure accurately the position measurement but at the same time sensitivity of the optics produced noise which needed to be addressed as error problem. Though the rough terrain is found in Mars, this paper is applicable to a terrestrial robot on Earth. There are regions in Earth which have rough terrains and regions which are hard to measure with encoders. This is especially true concerning icy places like Antarctica, Greenland and others. The proposed implementation for the development of the locomotion system is to model a system for the position estimation through the use of simulation and collecting data using the LSV. Two simulations are performed, one is the differential drive of a two wheel robot and the second involves the fusion of the differential drive robot data and the LSV data collected from the rover testbed. The results have been positive. The expected contributions from the research work includes a design of a LSV system to aid the locomotion measurement system. Simulation results show the effect of different sensors and velocity of the robot. The kalman filter improves the position estimation process