Autonomous indoor service robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions. Particularly, for complex manipulation tasks which are subject to geometric constraints, spatial information and a rich semantic knowledge about objects, types, and functionality are required, together with the way in which these objects can be manipulated. In this line, this paper presents an ontological-based reasoning framework called Perception and Manipulation Knowledge (PMK) that includes: (1) the modeling of the environment in a standardized way to provide common vocabularies for information exchange in human-robot or robot-robot collaboration, (2) a sensory module to perceive the objects in the environment and assert the ontological knowledge, (3) an evaluation-based analysis of the situation of the objects in the environment, in order to enhance the planning of manipulation tasks. The paper describes the concepts and the implementation of PMK, and presents an example demonstrating the range of information the framework can provide for autonomous robots.

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.

New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS’ components needs special improvements. Since software contribution in ADAS’ development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS’ safety critical components; e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper presents a remote diagnosis, maintenance and prognosis (RDMP) framework for ADAS, which can be used during development phase and mainly after production. An overview of RDMP framework’s elements is explained to demonstrate how/when this framework is connected to database servers and remote analysis servers. Moreover, Sensors fusion is used in RDMP to detect some sensor failures and even to predict their RUL. Additionally, some well-known machine learning algorithms (MLA) are used to predict RUL of ADAS’ components, and different types of input attributes to these MLA are proposed for some basic ADAS’ components. MLA use training data set, which shall be constructed ideally from actual records reported remotely by RDMP (Prognosis Analysis and Self-Learning System). However, initial dataset before production of the vehicle can be created from ADAS laboratory tests (e.g. Assessments on test tracks), ADAS simulation and theoretical analytical methods. Also, experiments of using the proposed RDMP in some ADAS’ components (Sensor fusion and Braking system as ADAS actuator) are presented. Summary, conclusion with proven results and future work are explained.

In human-robot interaction, trust is affected by human, robot, and environmental factors. In the proposed research, we consider each of these factors, by focusing on the contribution of robot theory of mind (ToM), human visual perspective taking (a concept related to ToM), and environmental complexity, to the development and maintenance of human-robot trust. To do so, our experiment combines a psychological assessment of visual perspective taking (the Director Task), with a trust-based robot navigation task. Using the AREA model of Responsible Research and Innovation (RRI), we also highlight the implications of our experiment, in the context of trustworthy robotics and human-robot collaboration. We round off the article with a theoretical and research development synopses related to robot ToM and trust, and future work to be conducted in this area.