Towards Adaptive Planning of Assistive-care Robot Tasks

Whilst assistive robots [7] have been embedded into social and health care environments [1, 2, 10], they have largely been limited to simple applications, such as support for social and physical activities and hall monitoring, but often without considering potential interactions with humans. To expand the range of these applications, the human user and the robot need to interact in order to perform tasks together [4]. As such, this interaction, which is still underexplored in the social care domain, should be prioritised, with an emphasis on the safety of the human [3, 9]. To enable the development of applications that support such interaction and to ensure its safety, we propose an adaptive mission and path finding framework for an autonomous robot operating in a homecare environment. The framework models the environment as a graph, with nodes representing key locations within the environment where the robot can perform local tasks. Missions are modelled as a repertoire of locations within the environment where a task requires completion. The main contributions of our'research preview' paper are: (i) a generalised approach for modelling environments as graphs with edges represented as levels of risk, (ii) a modified Dijkstra's algorithm for performing path finding in uncertain environments with a cost function to reduce risk, (iii) simple human predictive behaviour model that forecasts human intention allowing for adaptive path finding using heat maps to artificially increase the risk associated with specific edges in the graph, (iv) a framework that combines modelling methods, adaptive path finding techniques and run-time probabilistic model generation for safety verification into an end-to-end solution for autonomous robotic mission planning, (v) finally, a simulation-based case study that shows the effectiveness of the framework.