1,059 research outputs found

    Assistive Planning in Complex, Dynamic Environments: a Probabilistic Approach

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    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

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    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

    A multi-modal utility to assist powered mobility device navigation tasks

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    © Springer International Publishing Switzerland 2014. This paper presents the development of a shared control systemfor power mobility device users of varying capability in order toreduce carer oversight in navigation. Weighting of a user’s joystick inputagainst a short-tem trajectory prediction and obstacle avoidancealgorithm is conducted by taking into consideration proximity to obstaclesand smoothness of user driving, resulting in capable users rewardedgreater levels of manual control for undertaking maneuvres that can beconsidered more challenging. An additional optional comparison with aVector Field Histogram applied to leader-tracking provides further activities,such as completely autonomous following and a task for the userto follow a leading entity. Indoor tests carried out on university campusdemonstrate the viability of this work, with future trials at a care homefor the disabled intended to show the system functioning in one of itsintended settings

    Low complex sensor-based shared control for power wheelchair navigation

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    International audienceMotor or visual impairments may prevent a user from steering a wheelchair effectively in indoor environments. In such cases, joystick jerks arising from uncontrolled motions may lead to collisions with obstacles. We here propose a perceptive shared control system that progressively corrects the trajectory as a user manually drives the wheelchair, by means of a sensor-based shared control law capable of smoothly avoiding obstacles. This control law is based on a low complex optimization framework validated through simulations and extensive clinical trials. The provided model uses distance information. Therefore, for low-cost considerations, we use ultrasonic sensors to measure the distances around the wheelchair. The solution therefore provides an efficient assistive tool that does not alter the quality of experience perceived by the user, while ensuring his security in hazardous situations

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

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    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

    Mechatronic Systems

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    Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

    Understanding Interactions for Smart Wheelchair Navigation in Crowds

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