A Short Paper About Motion Safety

Motion safety for robotic systems operating in the real world is critical (especially when their size and dynamics make them potentially harmful for themselves or their environment). Motion safety is a taken-for-granted and ill-defined notion in the Robotics literature and the primary contribution of this paper is to propose three safety criteria that helps in understanding a number of key aspects related to the motion safety issue. A number of navigation schemes used by robotic systems operating in the real-world are then evaluated with respect to these safety criteria. It is established that, in all cases, they violate one or several of them. Accordingly, motion safety, especially in the presence of moving objects, cannot be guaranteed (in the sense that these robotic systems may end up in a situation where a collision inevitably occurs later in the future). Finally, it is shown that the concept of Inevitable Collision States introduced in [Fraichard, Asama, 2004] does respect the three above-mentioned safety criteria and therefore offers a theoretical answer to the motion safety issue

Mobile robots and vehicles motion systems: a unifying framework

Robots perform many different activities in order to accomplish their tasks. The robot motion capability is one of the most important ones for an autonomous be- havior in a typical indoor-outdoor mission (without it other tasks can not be done), since it drastically determines the global success of a robotic mission. In this thesis, we focus on the main methods for mobile robot and vehicle motion systems and we build a common framework, where similar components can be interchanged or even used together in order to increase the whole system performance

Robot introspection through learned hidden Markov models

In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behavioural models to provide a robot with an introspective capability. We assume that the behaviour of a robot in achieving a task can be modelled as a finite stochastic state transition system. Beginning with data recorded by a robot in the execution of a task, we use unsupervised learning techniques to estimate a hidden Markov model (HMM) that can be used both for predicting and explaining the behaviour of the robot in subsequent executions of the task. We demonstrate that it is feasible to automate the entire process of learning a high quality HMM from the data recorded by the robot during execution of its task.The learned HMM can be used both for monitoring and controlling the behaviour of the robot. The ultimate purpose of our work is to learn models for the full set of tasks associated with a given problem domain, and to integrate these models with a generative task planner. We want to show that these models can be used successfully in controlling the execution of a plan. However, this paper does not develop the planning and control aspects of our work, focussing instead on the learning methodology and the evaluation of a learned model. The essential property of the models we seek to construct is that the most probable trajectory through a model, given the observations made by the robot, accurately diagnoses, or explains, the behaviour that the robot actually performed when making these observations. In the work reported here we consider a navigation task. We explain the learning process, the experimental setup and the structure of the resulting learned behavioural models. We then evaluate the extent to which explanations proposed by the learned models accord with a human observer's interpretation of the behaviour exhibited by the robot in its execution of the task

Development Of Smart Navigation System For The Visually Impaired

Smart navigation system for the visually impaired is very important for them to travel from one place to another. Compared to readily available devices such as white cane, guide dog and others, which are time consuming, expensive and potential injuries for the visually impaired, this project serves way better in reducing error potential. Ultrasonic sensors will be attached to the front, right side and to the left side. The front ultrasonic sensor is to detect any obstacle so that visually impaired can avoid it. The side sensors act as anti-veering to ensure the visually impaired stay on the track. GPS module with GSM module also used in this project, which is to trace the location of the visually impaired. With this, the visually impaired can go anywhere since their location can be traced by the guardian. Investigation on the ultrasonic sensors during walking is carried out. The performance of the GPS with GSM also analyzed. As a result, this system can be used as a navigation system to increase the performance in walking of the visually impaired to ensure they walk safely by understanding the surrounding environment

Direction finding in the presence of a more realistic environment model

Direction-of-arrival (DOA) estimation is susceptible to errors introduced by the presence of real-ground and resonant size scatterers in the vicinity of the antenna array. To compensate for these errors pre-calibration and auto-calibration techniques are presented. The effects of real-ground constituent parameters on the mutual coupling (MC) of wire type antenna arrays for DOA estimation are investigated. This is accomplished by pre-calibration of the antenna array over the real-ground using the finite element method (FEM). The mutual impedance matrix is pre-estimated and used to remove the perturbations in the received terminal voltage. The unperturbed terminal voltage is incorporated in MUSIC algorithm to estimate DOAs. First, MC of quarter wave monopole antenna arrays is investigated. This work augments an existing MC compensation technique for ground-based antennas and proposes reduction in MC for antennas over finite ground as compared to the perfect ground. A factor of 4 decrease in both the real and imaginary parts of the MC is observed when considering a poor ground versus a perfectly conducting one for quarter wave monopoles in the receiving mode. A simulated result to show the compensation of errors direction of arrival (DOA) estimation with actual realization of the environment is also presented. Secondly, investigations for the effects on received MC of λ/2 dipole arrays placed near real-earth are carried out. As a rule of thumb, estimation of mutual coupling can be divided in two regions of antenna height that is very near ground

Motion planning and obstacle avoidance for mobile robots in highly cluttered dynamic environments

After a quarter century of mobile robot research, applications of this fascinating technology appear in real-world settings. Some require operation in environments that are densely cluttered with moving obstacles. Public mass exhibitions or conventions are examples of such challenging environments. This dissertation addresses the navigational challenges that arise in settings where mobile robots move among people and possibly need to directly interact with humans who are not used to dealing with technical details. Two important aspects are solved: Reliable reactive obstacle avoidance to guarantee safe operation, and smooth path planning that allows to dynamically adapt environment information to the motion of surrounding persons and objects. Given the existing body of research results in the field of obstacle avoidance and path planning, which is reviewed in this context, particular attention is paid to integration aspects for leveraging advantages while compensating drawbacks of various methods. In particular, grid-based wavefront propagation (NF1 and fast marching level set methods), dynamic path representation (bubble band concept), and high-fidelity execution (dynamic window approach) are combined in novel ways. Experiments demonstrate the robustness of the obstacle avoidance and path planning systems

Self-Driving of a Model Car

Cieľom práce je demonštrácia možností samočinného riadenia modelu vozidla, so zameraním na metódy plánovania lokálnej trajektórie a vyhýbania sa prekážkam. V rámci práce bol model doplnený o výpočtovú platformu Raspberry Pi a vhodné senzory. Konkrétne 2D LiDAR na detekciu a meranie vzdialenosti okolitých objektov, inkrementálny rotačný enkóder na meranie urazenej vzdialenosti a aktuálnej rýchlosti, a gyroskop, ktorý sníma relatívnu orientáciu vozidla. Následne bol implementovaný riadiaci systém schopný prijímať a spracovávať senzorové dáta, využiť ich pri odhade aktuálnej polohy a výpočte optimálnej trajektórie v nezmapovanom prostredí, a podľa parametrov tejto trajektórie ovládať akčné členy na ceste do cieľovej destinácie. Výsledkom je funkčný model vozidla schopný navigácie v neznámom prostredí a dosiahnutia zadaných cieľov jazdou po trajektórii tvorenej dynamicky v závislosti na okolitých prekážkach.The aim of this thesis is to demonstrate options for self-driving model cars, focused on local path planning methods and obstacle avoidance. As a part of the project, the model was supplemented by a computing platform Raspberry Pi and appropriate sensors. Specifically, a 2D LiDAR sensor was used for detection and measuring the distance of surrounding objects, an incremental rotary encoder for measuring the distance travelled and current speed, and a gyroscope to keep track of the vehicle's relative orientation. Subsequently, a control system was implemented. This system is able to receive and process sensor data, use it to estimate vehicle's current location, compute an optimal trajectory in an uncharted environment, and control the vehicle's actuators accordingly. The result is a functional model car able to navigate in an unknown environment and reach specified goals by following a trajectory, dynamically generated depending on the surrounding obstacles.