Motion planning in crowds using statistical model checking to enhance the social force model

J Reliable Intell Environ

Sedwards

et al. 2018

Self Cite

The ACANTO project is developing robotic assistants to aid the confidence and recovery of older adults. A key requirement of these assistants is aiding with navigation in complex and potentially chaotic environments. Prior work has addressed this for a single user, using a single robotic assistant in an intelligent environment. However, for therapeutic purposes, ACANTO supports social groups and group activities. ACANTO's robotic assistants must therefore be able to plan the motion of groups of older adults walking together. This requires an efficient navigation solution that can handle large numbers of users and that can operate rapidly on embedded computing devices. To increase user confidence, the solution must encourage group cohesion without trying to impose its own rigid structure; it must try to maintain the natural (de facto) group structure despite unpredictable behaviours and environmental conditions. Our on-the-fly group motion planner addresses these challenges by: using intelligent environment information to develop behavioural traces, clustering traces to determine groups, constructing a predictive model of the groups as a whole, and finding an optimal suggested trajectory using Statistical Model Checking. In this work we describe our proposed approach in detail and validate some of its novel aspects on the ETH Zürich pedestrian motion dataset.

“…For a single user with a single walker, the DALi solution has proven successful [11,12]. This holds even though the user is free to ignore or override the navigational advice at any time.…”

Section: Challengesmentioning

confidence: 96%

Section: Human Motion Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Group abstraction for assisted navigation of social activities in intelligent environments

Given-Wilson

J Reliable Intell Environ

Sedwards

et al. 2018

Self Cite

“…In this particular case, the long term planner is the algorithm proposed in this work while the short term planner outputs a suggested trajectory and is reactive to the potentially uncooperative response of the AP [7].…”

Section: Case Study: the Dali Projectmentioning

confidence: 99%

Efficient customisable dynamic motion planning for assistive robots in complex human environments

Colombo

Fontanelli

et al. 2015

AIS

Self Cite

Abstract. People with impaired physical and mental ability often find it challenging to negotiate crowded or unfamiliar environments, leading to a vicious cycle of deteriorating mobility and sociability. To address this issue we present a novel motion planning algorithm that is able to intelligently deal with crowded areas, permanent or temporary anomalies in the environment (e.g., road blocks, wet floors) as well as hard and soft constraints (e.g., "keep a toilet within reach of 10 meters during the journey", "always avoid stairs"). Constraints can be assigned a priority tailored on the user's needs. The planner has been validated by means of simulations and experiments with elderly people within the context of the DALi European project.

“…Plasma Lab has been integrated with robotic devices for the DALi 9 FP7 and ACANTO 10 H2020 projects, in the context of motion planning for assisted living [7,8]. Both projects rely on a novel online motion planning application of SMC to help those with impaired ability to negotiate complex crowded environments, such as museums and shopping malls.…”

Section: Dynamic Motion Planning In Dali and Acanto Projectsmentioning

confidence: 99%

Plasma Lab: A Modular Statistical Model Checking Platform

Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques

Sedwards

Traonouez

2016

Self Cite

Abstract. We present an overview of Plasma Lab, a modular statistical model checking (SMC) platform that facilitates multiple SMC algorithms, multiple modelling and query languages and has multiple modes of use. Plasma Lab may be used as a stand-alone tool with a graphical development environment or invoked from the command line for high performance scripting applications. Plasma Lab is written in Java for maximum cross-platform compatibility, but it may interface with tools and libraries written in arbitrary programming languages. Plasma Lab's API also allows it to be incorporated as a library within other tools. We first describe the motivation and architecture of Plasma Lab, then proceed to describe some of its important algorithms, including those for rare events and nondeterminism. We conclude with a number of industrially-relevant case studies and applications.