{"id":890,"date":"2018-06-12T07:54:47","date_gmt":"2018-06-12T06:54:47","guid":{"rendered":"http:\/\/babel.isa.uma.es\/kipr\/?p=890"},"modified":"2018-07-23T07:56:21","modified_gmt":"2018-07-23T06:56:21","slug":"evaluating-the-safeness-of-a-motion-plan-for-mobile-robot-navigation","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=890","title":{"rendered":"Evaluating the safeness of a motion plan for mobile robot navigation"},"content":{"rendered":"

Brian Axelrod, Leslie Pack Kaelbling, and Tom\u00e1s Lozano-P\u00e9rez Provably safe robot navigation with obstacle uncertainty<\/strong>, The International Journal of Robotics Research Vol 37, Issue 7 DOI: 10.1177\/0278364918778338<\/a>.<\/h4>\n

As drones and autonomous cars become more widespread, it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy has at most \u03f5 collision probability (\u03f5-safe) with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We explore the idea of shadows that generalize the notion of a confidence set to estimated shapes and present a theorem that allows us to understand the relationship between shadows and their classical statistical equivalents such as confidence and credible sets. We present efficient algorithms that use shadows to prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method\u2019s ability to evaluate whether a trajectory or policy is safe. We then use these safety-checking methods to design a safe variant of the rapidly exploring random tree (RRT) planning algorithm.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"

Brian Axelrod, Leslie Pack Kaelbling, and Tom\u00e1s Lozano-P\u00e9rez Provably safe robot navigation with obstacle uncertainty, The International Journal of Robotics …<\/span> Read More →<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[23],"tags":[257],"class_list":["post-890","post","type-post","status-publish","format-standard","hentry","category-robot-motion-planning","tag-obstacle-avoidance"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/890"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=890"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/890\/revisions"}],"predecessor-version":[{"id":891,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/890\/revisions\/891"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=890"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=890"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=890"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}