Assistive Planning in Complex, Dynamic Environments: a Probabilistic Approach

We explore the probabilistic foundations of shared control in complex dynamic environments. In order to do this, we formulate shared control as a random process and describe the joint distribution that governs its behavior. For tractability, we model the relationships between the operator, autonomy, and crowd as an undirected graphical model. Further, we introduce an interaction function between the operator and the robot, that we call "agreeability"; in combination with the methods developed in~\cite{trautman-ijrr-2015}, we extend a cooperative collision avoidance autonomy to shared control. We therefore quantify the notion of simultaneously optimizing over agreeability (between the operator and autonomy), and safety and efficiency in crowded environments. We show that for a particular form of interaction function between the autonomy and the operator, linear blending is recovered exactly. Additionally, to recover linear blending, unimodal restrictions must be placed on the models describing the operator and the autonomy. In turn, these restrictions raise questions about the flexibility and applicability of the linear blending framework. Additionally, we present an extension of linear blending called "operator biased linear trajectory blending" (which formalizes some recent approaches in linear blending such as~\cite{dragan-ijrr-2013}) and show that not only is this also a restrictive special case of our probabilistic approach, but more importantly, is statistically unsound, and thus, mathematically, unsuitable for implementation. Instead, we suggest a statistically principled approach that guarantees data is used in a consistent manner, and show how this alternative approach converges to the full probabilistic framework. We conclude by proving that, in general, linear blending is suboptimal with respect to the joint metric of agreeability, safety, and efficiency

Towards a Shared Control Navigation Function:Efficiency Based Command Modulation

This paper presents a novel shared control algorithm for robotized wheelchairs. The proposed algorithm is a new method to extend autonomous navigation techniques into the shared control domain. It reactively combines user’s and robot’s commands into a continuous function that approximates a classic Navigation Function (NF) by weighting input commands with NF constraints. Our approach overcomes the main drawbacks of NFs -calculus complexity and limitations on environment modeling- so it can be used in dynamic unstructured environments. It also benefits from NF properties: convergence to destination, smooth paths and safe navigation. Due to the user’s contribution to control, our function is not strictly a NF, so we call it a pseudo-navigation function (PNF) instead.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Powered Wheelchair Platform for Assistive Technology Development

Literature shows that numerous wheelchair platforms, of various complexities, have been developed and evaluated for Assistive Technology purposes. However there has been little consideration to providing researchers with an embedded system which is fully compatible, and communicates seamlessly with current manufacturer's wheelchair systems. We present our powered wheelchair platform which allows researchers to mount various inertial and environment sensors, and run guidance and navigation algorithms which can modify the human desired joystick trajectory, so as to assist users with negotiating obstacles, and moving from room to room. We are also able to directly access other currently manufactured human input devices and integrate new and novel input devices into the powered wheelchair platform for clinical and research assessment

Learning-Based Adaptation for Personalized Mobility Assistance

Mobility assistance is of key importance for people with disabilities to remain autonomous in their preferred environments. In severe cases, assistance can be provided by robotized wheelchairs that can perform complex maneuvers and/or correct the user’s commands. User’s acceptance is of key importance, as some users do not like their commands to be modified. This work presents a solution to improve acceptance. It consists of making the robot learn how the user drives so corrections will not be so noticeable to the user. Case Based Reasoning (CBR) is used to acquire a user’s driving model reactive level. Experiments with volunteers at Fondazione Santa Lucia (FSL) have proven that, indeed, this customized approach at assistance increases acceptance by the user.This work has been partially supported by the Spanish Ministerio de Educacion y Ciencia (MEC), Project TEC2011-29106-C02-01. The authors would like to thank Santa Lucia Hospedale and all volunteers for their kind cooperation and Sauer Medica for providing the power wheelchair

An adaptive scheme for wheelchair navigation collaborative control

In this paper we propose a system where machine and human cooperate at every situation via a reactive emergent behavior, so that the person is always in charge of his/her own motion. Our approach relies on locally evaluating the performance of the human and the wheelchair for each given situation. Then, both their motion commands are weighted according to those efficiencies and combined in a reactive way. This approach benefits from the advantages of typical reactive behaviors to combine different sources of information in a simple, seamless way into an emergent trajectory.Peer ReviewedPostprint (author’s final draft

Goal Blending for Responsive Shared Autonomy in a Navigating Vehicle

You are viewing an article from Good systems from February 2021.Office of the VP for Researc

An ‘Ethical Black Box’, Learning From Disagreement in Shared Control Systems

Shared control, where a human user cooperates with an algorithm to operate a device, has the potential to greatly expand access to powered mobility, but also raises unique ethical challenges. A shared-control wheelchair may perform actions that do not reflect its user’s intent in order to protect their safety, causing frustration or distrust in the process. Unlike physical accidents there is currently no framework for investigating or adjudicating these events, leading to a reduced capability to improve the shared control algorithm’s user experience. In this paper we suggest a system based on the idea of an ‘ethical black box’ that records the sensor context of sub-critical disagreements and collision risks in order to allow human investigators to examine them in retrospect and assess whether the algorithm has taken control from the user without justification