Abstract
Active assistive systems for mobility aids are largely restricted to environments mapped a-priori, while passive assistance primarily provides collision mitigation and other hand-crafted behaviors in the platform’s immediate space. This paper presents a framework providing active short-term assistance, combining the freedom of location independence with the intelligence of active assistance. Demonstration data consisting of on-board sensor data and driving inputs is gathered from an able-bodied expert maneuvring the mobility aid around a generic interior setting, and used in constructing a probabilistic intention model built with Radial Basis Function Networks. This allows for short-term intention prediction relying only upon immediately available user input and on-board sensor data, to be coupled with real-time path generation based upon the same expert demonstration data via Dynamic Policy Programming, a stochastic optimal control method. Together these two elements provide a combined assistive mobility system, capable of operating in restrictive environments without the need for additional obstacle avoidance protocols. Experimental results in both simulation and on the University of Technology Sydney semi-autonomous wheelchair in settings not seen in training data show promise in assisting users of power mobility aids.
Highlights
The global population is ageing quickly, with predictions that the worldwide proportion of people aged 60 and over is expected to double by 2050 (United Nations 2015)
2 Centre for Autonomous Systems, University of Technology Sydney, Sydney, Australia. As devices such as power wheelchairs are large, heavy and powerful machines it is common for prospective users to meet a strict set of conditions before prescription is approved (Queensland Department of Health 2016) even if they are otherwise capable of independently performing other routine tasks
As these intentions can encompass difficult locations such as doorways for which heuristics are designed to handle in reactively assistive systems, the inference of intentions allows the merging of collision avoidance and selective interaction with artefacts, bypassing the need for situational behaviors found in systems based upon obstacle avoidance
Summary
The global population is ageing quickly, with predictions that the worldwide proportion of people aged 60 and over is expected to double by 2050 (United Nations 2015). Instead it is arguably more desirable for a system to provide assistance ‘anywhere’, like reactively assistive frameworks are capable of, without the need for map-building by only relying upon immediately available on-board sensor data In this “local” space one can infer immediate short-term destinations being points of interest that the user wishes to pass through or stop at at a given instant, to which short-term path planning can take place. The contribution of this work in the literature covering shared control of PMD devices is in short-term intention estimation without depending upon a a-priori map, for enabling subsequent robot path planning and navigation in a non-reactive manner As these intentions can encompass difficult locations such as doorways for which heuristics are designed to handle in reactively assistive systems, the inference of intentions allows the merging of collision avoidance and selective interaction with artefacts, bypassing the need for situational behaviors found in systems based upon obstacle avoidance.
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