Situation-Aware Self-Adaptive Localisation Framework A Knowledge Representation and Reasoning approach

by Shreyash Palande/

Shreyash Palande


Substantial efforts are being made to make robots more reliable and safe to work around humans. Robots often perform flawless demos in a controlled environment under the supervision of an operator but tend to fail in the real world when deployed for a long period of time due to faults and environmental disturbances. A robotic system is composed of different physical and software components whose characteristics are likely to change over time. Assumptions made about the system during the design phase may change over time, especially when a system is deployed for long periods. Such changes that are often ignored, need to be considered. Environments in which a robot operates are dynamic with high uncertainty and unpredictability. In such scenarios, capabilities such as situational awareness and self-adaptation will be useful to create more robust, resilient and reliable solutions. The objective for this thesis work is to develop a framework which will embed capabilities such as situational-awareness, contextawareness and self-adaptation within a robot. This research provides a novel, reusable and generalised localisation framework called Situation-Aware Self-Adaptive (SASA) localisation framework for robotics application. This framework is developed using knowledge representation and reasoning which will provide a robot with the capability of adapting according to the situation. We have demonstrated the applicability of the SASA framework to a mobile robot localisation use case. In this research work we have demonstrated the performance of the framework during environmental disturbances due to poor illumination and featureless environment and internal fault due to component failure. We have also demonstrated the reusability, changeability and the consistency of SASA framework. This work showed that the situational-awareness and self-adaptation capability enhances the robot’s localisation ability and provides reliable localisation even in the case of environmental uncertainties and internal faults where conventional localisation systems fail. This thesis represents a leap forward in the direction of creating more reliable and resilient solutions for robotic applications and it lays the foundations for further research in this direction. Service robots are being developed for assisting humans in homes and offices [1], and substantial efforts are being made to make robots more reliable and safe to work around humans. But robots usually fail in real-world when deployed for a long period of time. This literature survey focuses on various questions which are related to dependable autonomous robots in retail environment such as what are the important aspect for dependability, what kind of challenges and failures can occur in retail environment related to localization, what methods are available to perform Fault detection and diagnosis (FDD) and how to recover from a fault, how can reconfiguration can be useful for fault recovery, etc. This literature tries to provide the answers to the questions and form a basis for final thesis work which aims at developing a dependable robot localization system using a system reconfiguration approach.

  • Student projects
  • TU Delft