{"title":"Haptics: The Present and Future of Artificial Touch Sensation","authors":"Heather Culbertson, Samuel B. Schorr, A. Okamura","doi":"10.1146/ANNUREV-CONTROL-060117-105043","DOIUrl":"https://doi.org/10.1146/ANNUREV-CONTROL-060117-105043","url":null,"abstract":"This article reviews the technology behind creating artificial touch sensations and the relevant aspects of human touch. We focus on the design and control of haptic devices and discuss the best practices for generating distinct and effective touch sensations. Artificial haptic sensations can present information to users, help them complete a task, augment or replace the other senses, and add immersiveness and realism to virtual interactions. We examine these applications in the context of different haptic feedback modalities and the forms that haptic devices can take. We discuss the prior work, limitations, and design considerations of each feedback modality and individual haptic technology. We also address the need to consider the neuroscience and perception behind the human sense of touch in the design and control of haptic devices.","PeriodicalId":147293,"journal":{"name":"Annu. Rev. Control. Robotics Auton. Syst.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133304049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sampling-Based Methods for Motion Planning with Constraints","authors":"Zachary K. Kingston, Mark Moll, L. Kavraki","doi":"10.1146/ANNUREV-CONTROL-060117-105226","DOIUrl":"https://doi.org/10.1146/ANNUREV-CONTROL-060117-105226","url":null,"abstract":"Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: ( a) sampling constraint-satisfying configurations and ( b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints.","PeriodicalId":147293,"journal":{"name":"Annu. Rev. Control. Robotics Auton. Syst.","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124797213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}