As an attempt to steer away from developing an autonomous robot with complex centralised intelligence, this thesis proposes an intelligent environment infrastructure where intelligences are distributed in the environment through collaborative vision sensors mounted in a physical architecture, forming a wireless sensor network, to enable the navigation of unintelligent robots within that physical architecture. The aim is to avoid the bottleneck of centralised robot intelligence that hinders the application and exploitation of autonomous robot. A bio-mimetic snake algorithm is proposed to coordinate the distributed vision sensors for the generation of a collision free Reference-snake (R-snake) path during the path planning process. By following the R-snake path, a novel Accompanied snake (A-snake) method that complies with the robot's nonholonomic constraints for trajectory generation and motion control is introduced to generate real time robot motion commands to navigate the robot from its current position to the target position. A rolling window optimisation mechanism subject to control input saturation constraints is carried out for time-optimal control along the A-snake. A comprehensive simulation software and a practical distributed intelligent environment with vision sensors mounted on a building ceiling are developed. All the algorithms proposed in this thesis are first verified by the simulation and then implemented in the practical intelligent environment. A model car with less on-board intelligence is successfully controlled by the distributed vision sensors and demonstrated superior mobility.

Syftet med denna studie är att bidra med en djupare förståelse för hur rekryterare använder Artificiell Intelligens (AI) i en rekryteringsprocess för att medvetandegöra bias. Tidigare forskning visar att arbetssökandens chanser till arbete påverkas av rekryterarens bias, detta gör att arbetssökanden inte bedöms utefter kompetens. Tidigare studier visar att arbetssökanden missgynnas baserat på olika egenskaper, kopplat till etnicitet, ålder och kön. Rekryteringsprocessen är i ett behov av verktyg som minskar denna bias, där forskning visar att AI-system kan vara ett sådant verktyg. I denna studie har vi inkluderat respondenter som besitter erfarenhet av AI-system i en urvalsprocess. Studien genomförs med en kvalitativ forskningsansats där åtta respondenter har inkluderats. Empirin har analyserats genom en tematisk analys där sex teman identifierats. Resultatet presenterar olika faktorer som jämförs mot tidigare forskning där diskussionen behandlar de mest centrala från studien. Resultatet visar att alla respondenter är överens om att alla människor innehar bias som påverkar urvalsprocessen. AI-system tar bort fokus från etnicitet, ålder och kön, därmed upplever respondenterna att AI-systemet kan medvetandegöra bias eftersom systemet baserar rangordning av arbetssökande utifrån kompetens. Studien lyfter vad som anses behövas av rekryterare för att möjliggöra för AI att kunna medvetandegöra bias. Avslutningsvis visar resultatet att AI-system kräver kontinuerlig utveckling. Med rätt förutsättningar kan AI medvetandegöra bias, bortse från synliga attribut och bedöma arbetssökande efter kompetens. / The purpose of this study is to contribute with a deeper understanding of how recruiters use Artificial Intelligence (AI) in a recruitment process to raise awareness of bias. Previous research shows that applicant chances of getting a job are affected by the recruiter's bias, this means that applicants are not assessed on competence. Previous studies show that applicants are disadvantaged based on different characteristics, linked to ethnicity, age and gender. The recruitment process has a need of tools that reduce this bias, where research shows that AI systems can be such a tool. In this study, we have included participants who have experience of AI systems in a selection process. The study is carried out with a qualitative research approach where eight participants have been included. The empirics have been analysed through a thematic analysis where six themes have been identified. The results present various factors that are compared to previous research where the discussion deals with the most central from the study. The results show that all participants agree that all people have biases that affect the selection process. AI systems remove focus from ethnicity, age and gender, participants believe that the AI system can raise awareness of bias because the ranking is based on applicant’s competence. The study highlights what is considered needed by recruiters to enable AI to be able to raise awareness of bias. In conclusion, the results show that AI systems require continuous development. With the right conditions, AI can raise awareness of bias, ignore visible attributes and assess jobseekers according to competence.

As an attempt to steer away from developing an autonomous robot with complex centralised intelligence, this thesis proposes an intelligent environment infrastructure where intelligences are distributed in the environment through collaborative vision sensors mounted in a physical architecture, forming a wireless sensor network, to enable the navigation of unintelligent robots within that physical architecture. The aim is to avoid the bottleneck of centralised robot intelligence that hinders the application and exploitation of autonomous robot. A bio-mimetic snake algorithm is proposed to coordinate the distributed vision sensors for the generation of a collision free Reference-snake (R-snake) path during the path planning process. By following the R-snake path, a novel Accompanied snake (A-snake) method that complies with the robot's nonholonomic constraints for trajectory generation and motion control is introduced to generate real time robot motion commands to navigate the robot from its current position to the target position. A rolling window optimisation mechanism subject to control input saturation constraints is carried out for time-optimal control along the A-snake. A comprehensive simulation software and a practical distributed intelligent environment with vision sensors mounted on a building ceiling are developed. All the algorithms proposed in this thesis are first verified by the simulation and then implemented in the practical intelligent environment. A model car with less on-board intelligence is successfully controlled by the distributed vision sensors and demonstrated superior mobility.