Practical application of pseudospectral optimization to robot path planning

To obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platform’s dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simplified kino-dynamic models to avoid the significant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robot’s dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predefined analytical functions to enable real world application

System Integration of a Tour Guide Robot

In today\u27s world, people visit many attractive places. On such an occasion, It is of utmost importance to be accompanied by a tour guide, who is known to explain about the cultural and historical importance of places. Due to the advancements in technology, smartphones today have the capability to help a person navigate to any place in the world and can itself act as a tour guide by explaining a significance of a place. However, the person while looking into his phone might not watch his/her step and might collide with other moving person or objects. With a phone tour guide, the person is alone and is missing a sense of contact with other travelers. therefore a human guide is necessary to provide tours for a group of visitors. However, Human tour guides might face tiredness, distraction, and the effects of repetitive tasks while providing tour service to visitors. Robots eliminate these problems and can provide tour consistently until it drains its battery. This experiment introduces a tour-guide robot that can be used on such an occasion. Tour guide robots can navigate autonomously in a known map of a given place and at the same time interact with people. The environment is equipped with artificial landmarks. Each landmark provides information about that specific region. An Animated avatar is simulated on the screen. IBM Watson provides voice recognition and text-to-speech services for human-robot interaction

Rajaplaneerimine multi-robot süsteemile jagatud lasti transportimisel

Shared payload transportation has emerged as one of the key real-world applications that warrants the deployment of multiple robots. The key motivation stems from the fact that actuation and sensing abilities of multiple robots can be pooled together to transport objects that are either too big or heavy to be handled by a single robot. This thesis proposes algorithmic and software frameworks to achieve precise multi-robot coordination for object transportation. On the algorithmic side, a trajectory optimization formulation is developed which generates collision-free and smooth trajectories for the robots transporting the object. State-of-the art Gradient Descent variants are utilized for obtaining the solution. On the software side, a trajectory planner (local planner) is developed and integrated to Robot Operating System (ROS). The local planner is responsible for calculating individual velocities for any number of robots forming a rigid geometric in-plane constellation. Extensive simulation as well as real-world experiments are performed to demonstrate the validity of the developed solutions. It is demonstrated that how the proposed trajectory optimization approach outperforms off-the-shelf planners with respect to metrics like smoothness and collision avoidance. In estonian: Ühise lasti transportimine mitme roboti poolt on kujunenud üheks rakendusvaldkonnaks, kus mitme roboti samaaegne kasutamine on õigustatud. Mitme roboti andureid ja ajameid on eriti kasulik kasutada transportimaks objekte, mis on ühe roboti jaoks kas liiga suured ja/või rasked. Käesolev lõputöö pakub välja algoritmilise ja tarkvaralise raamistiku, mis võimaldab täpselt koordineerida mitme roboti koostööd ühise lasti liigutamisel. Välja on töötatud trajektooride optimeerimise algoritm, mis genereerib kokkupõrkevabad ja sujuvad ühist objekti kandvate robotite trajektoorid. Selleks on kasutatud nüüdisaegset gradientlaskumise (ingl Gradient Descent) meetodit. Tarkvara poolelt on loodud trajektoori planeerija (lokaalne planeerija) ja see on integreeritud arendusplatvormil ROS (Robot Operating System). Lokaalne planeerija arvutab individuaalsed kiirused igale robotile, mis moodustavad ühise jäiga tasapinnalise kujundi, kusjuures robotite arv kujundis ei ole piiratud. Väljatöötatud lahenduse toimimist on kontrollitud ulatuslike simulatsioonide abil aga ka viies läbi praktilisi katseid. Väljapakutud trajektoori optimeerimise lahendus ületab olemasolevaid planeerijaidd nii trajektoori sujuvuse kui ka kokkupõrgete vältimise võime osas

Learning to Prevent Monocular SLAM Failure using Reinforcement Learning

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.Comment: Accepted at the 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) 2018 More info can be found at the project page at https://robotics.iiit.ac.in/people/vignesh.prasad/SLAMSafePlanner.html and the supplementary video can be found at https://www.youtube.com/watch?v=420QmM_Z8v

Socially-Aware Navigation Planner Using Models of Human-Human Interaction

A real-time socially-aware navigation planner helps a mobile robot to navigate alongside humans in a socially acceptable manner. This navigation planner is a modification of nav_core package of Robot Operating System (ROS), based upon earlier work and further modified to use only egocentric sensors. The planner can be utilized to provide safe as well as socially appropriate robot navigation. Primitive features including interpersonal distance between the robot and an interaction partner and features of the environment (such as hallways detected in real-time) are used to reason about the current state of an interaction. Gaussian Mixture Models (GMM) are trained over these features from human-human interaction demonstrations of various interaction scenarios. This model is both used to discriminate different human actions related to their navigation behavior and to help in the trajectory selection process to provide a social-appropriateness score for a potential trajectory. This thesis presents a model based framework for navigation planning, a simulation-based evaluation of the model-based navigation behavior

Path planning algorithms for autonomous navigation of a non-holonomic robot in unstructured environments

openPath planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms.Path planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms

Navegação semântica aplicada a passagens estreitas em cadeira de rodas inteligente

With the development of robotics technology, new opportunities for improving the quality of life of people with mobility impairments arise. The IntellWheels project was born with the goal of developing a hardware and software kit that can turn a motorized wheelchair into an autonomous Intelligent Wheelchair. This dissertation fits into this project on the topic of indoor navigation with the goal of adding a semantic layer to the navigation framework. Semantic in robotics is the ability of a robot to understand its environment. In the case of an Intelligent Wheelchair, this is especially important considering it is a robot that carries a passenger. A solution was developed where concepts of semantic navigation are used to tackle the problem of crossing narrow passages, building an additional mapping layer where these passages are automatically detected and marked on the map and an extra planning layer that takes in this semantic information and makes decisions for controlling the lower-level planners. In order to validate this solution tests, in both simulated and real-world environments, were performed to verify if the individual components worked as intended. This thesis resulted in an improvement of the indoor navigation capabilities of the framework and is a base for further development in the area of semantic navigation of Intelligent Wheelchairs.Com o desenvolvimento da tecnologia em robótica, surgem novas oportunidades para melhorar a qualidade de vida de pessoas com problemas de mobilidade. O projeto IntellWheels nasce com o objetivo de criar um kit de hardware e software capaz de transformar uma cadeira de rodas motorizada numa cadeira de rodas inteligente e autónoma. Esta dissertação encaixa neste projeto no tópico de navegação em ambientes interiores com o objetivo de adicionar uma camada semântica à framework de navegação. Em robótica, semântica é a capacidade de um robô entender o seu ambiente. No caso de uma cadeira de rodas inteligente, isto é especialmente importante tendo em conta que transporta um passageiro. Uma solução foi desenvolvida em que conceitos de navegação semântica são usados para abordar o problema de atravessar passagens estreitas, construindo uma camada adicional de mapeamento em que estas passagens são automaticamente identificadas e marcadas no mapa e uma camada extra de planeamento que recebe esta informação e toma decisões controlando os planeadores de nível mais baixo. Para validar esta solução, foram realizados testes, em ambiente simulado e ambiente real para verificar se os componentes individuais funcionavam como pretendido. Esta dissertação resultou numa melhoria das capacidades de navegação em ambientes interiores da framework e é uma base para futuros desenvolvimentos na área de navegação semântica para cadeiras de rodas inteligentes.Mestrado em Engenharia de Computadores e Telemátic