Gagandeep Kaur

Das Multi-Roboter-System erfordert eine angemessene Koordination zwischen mehreren Robotern, eine angemessene Verteilung der Teilaufgaben wie Aufgabenzuweisung, Wegplanung, Hindernisvermeidung und viele weitere Teilprobleme. All diese Teilprobleme m ssen f r die effektive Ausf hrung des Systems ber cksichtigt werden. Bei der Multi-Roboter-Jagd jagen die Roboter die Fl chtigen; die Roboter m ssen ihre Teammitglieder erkennen und ihre aktuellen Positionen und F higkeiten ber cksichtigen, um die station ren oder sich bewegenden Fl chtigen durch den kooperativen Wegplanungsansatz effektiv zu fangen. Diese Forschungsarbeit zielt darauf ab, eine L sung f r das Problem der Mehrroboterjagd in einer unbekannten Umgebung zu entwickeln. Die Roboter im Feld verf gen nicht ber Informationen ber das Gebiet, wie z. B. die Position der Hindernisse und Ausrei er. Die dynamische Umgebung wird in dieser Arbeit ber cksichtigt. Das Ziel dieser Arbeit ist es, eine L sung zu finden, die weniger Rechenaufwand f r die Roboter verursacht und die Gesamtzahl der Jagdschritte reduziert. Der erste Beitrag der Forschungsarbeit stellt das Problem der Jagd in einer unbekannten Umgebung vor.

Le syst me multi-robots n cessite une bonne coordination entre plusieurs robots, une bonne r partition des t ches secondaires telles que l'attribution des t ches, la planification du chemin, l' vitement des obstacles et bien d'autres probl mes secondaires. Tous ces sous-probl mes doivent tre pris en consid ration pour une ex cution efficace du syst me. Dans la chasse multi-robots, les robots chassent les fuyards; les robots doivent reconna tre les membres de leur quipe et tenir compte de leurs positions et capacit s actuelles pour attraper les fuyards stationnaires ou en mouvement de mani re efficace gr ce l'approche de planification de trajectoire coop rative. Ce travail de recherche vise concevoir une solution au probl me de la chasse multi-robots dans un environnement inconnu. Les robots sur le terrain ne disposent pas d'informations sur la zone, notamment sur la position des obstacles et des fuyards. L'environnement dynamique est pris en compte dans ce travail. L'objectif de ce travail est de fournir une solution qui entra nera moins de frais de calcul pour les robots et r duira le nombre total d' tapes de la chasse. La contribution initiale du travail de recherche introduit le probl me de la chasse dans un environnement inconnu.

Il sistema multirobot richiede un'adeguata coordinazione tra pi robot, una corretta distribuzione dei compiti secondari come l'assegnazione dei compiti, la pianificazione del percorso, l'evitamento degli ostacoli e molti altri problemi secondari. Tutti questi sottoproblemi devono essere presi in considerazione per un'esecuzione efficace del sistema. Nella caccia multi robot, i robot danno la caccia agli evasori; i robot devono riconoscere i membri della loro squadra e considerare le loro posizioni e capacit attuali per catturare efficacemente gli evasori fermi o in movimento attraverso un approccio di pianificazione del percorso cooperante. Questo lavoro di ricerca mira a progettare una soluzione al problema della caccia multi robot in un ambiente sconosciuto. I robot sul campo non dispongono di informazioni sull'area, come la posizione degli ostacoli e degli evasori. In questo lavoro si considera l'ambiente dinamico. L'obiettivo di questo lavoro fornire una soluzione che comporti un minor carico di calcolo per i robot e riduca le fasi di caccia complessive. Il contributo iniziale del lavoro di ricerca introduce il problema della caccia in un ambiente sconosciuto.

O sistema multi-rob requer uma coordena o adequada entre v rios rob s, uma distribui o adequada das sub-tarefas, tais como a atribui o de tarefas, o planeamento do caminho, a preven o de obst culos e muitos outros sub-problemas. Todos estes subproblemas devem ser tidos em considera o para a execu o eficaz do sistema. Na ca a com v rios rob s, os rob s ca am os evasores; os rob s precisam de reconhecer os membros da sua equipa e considerar as suas posi es e capacidades actuais para capturar eficazmente os evasores estacion rios ou em movimento atrav s da abordagem de planeamento do caminho cooperativo. Este trabalho de investiga o tem por objetivo conceber uma solu o para o problema da ca a por v rios rob s num ambiente desconhecido. Os robots no terreno n o disp em de informa o sobre a rea, nomeadamente sobre a posi o dos obst culos e dos evasores. O ambiente din mico considerado neste trabalho. O objetivo deste trabalho apresentar uma solu o que implique menos despesas de computa o para os rob s e reduza o n mero total de etapas de ca a. A contribui o inicial do trabalho de investiga o introduz o problema da ca a num ambiente desconhecido.

People In Our Lives Everyday Psychology explores the complexities of human existence, rooted in the cyclical nature of birth and life. It reflects on the uncertainties of life span and the perseverance driven by hope and belief. Amidst daily routines and varied experiences of joy, sorrow, and growth, relationships emerge as the core of human fulfillment, transcending material wealth. Dr Gagandeep''s medical background underscores a deeper understanding of human nature, emphasizing empathy and understanding in relationships. The spiritual beliefs about karma, reincarnation, and the soul''s journey weave into reflections on life''s purpose and ethical living. The text encourages living in the present, learning from experiences, and fostering meaningful connections. Ultimately, it posits that personal growth and fulfillment arise from embracing life''s challenges with compassion and a quest for deeper understanding, resonating with the hope of evolving into a better self throughout life''s journey.

The multi robot system requires a proper coordination among multiple robots, proper distribution of the sub - tasks such as task allocation, path planning, obstacle avoidance and many more other sub problems. All these sub problems must be taken into consideration for the effective execution of the system. In multi robot hunting, the robots hunt the evaders; the robots require recognizing their team members and considering their current positions and capabilities to catch the stationary or moving evaders effectively through the cooperating path planning approach. This research work is aimed to design a solution to problem of multi - robot hunting in an unknown environment. The robots in the field do not have information about the area like where the obstacles and evaders are positioned. The dynamic environment is considered in this work. The objective of this work is to provide solution which will incur less computation overhead to the robots and reduce the overall hunting steps. Initial contribution of research work introduces the problem of hunting in an unknown environment.

The rapid increase in textual content generation from sources like WhatsApp, Instagram, and Amazon produces massive amounts of data daily. Interpreting this data can help business owners understand the public perception of their products or services and make informed decisions. Due to the large volume of text, Natural Language Processing (NLP), a crucial aspect of Sentiment Analysis (SA), is essential for content interpretation. This research focuses on developing a Consumer Review Summarization (CRS) model using NLP techniques and Long Short-Term Memory (LSTM) to summarize data and provide businesses with significant insights into consumer behavior and preferences. The CRS model's effectiveness relies on the SA model and consists of two phases: SADL and CRS. The SADL phase includes review preprocessing, feature extraction, and sentiment classification, while the CRS phase performs automatic summarization based on the SADL outcomes.