Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics

Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and used to order the search. The practical improvement of this informed search depends on the accuracy of the heuristic. Selecting an appropriate heuristic is difficult. Heuristics applicable to an entire problem domain are often simple to define and inexpensive to evaluate but may not be beneficial for a specific problem instance. Heuristics specific to a problem instance are often difficult to define or expensive to evaluate but can make the search itself trivial. This paper presents Adaptively Informed Trees (AIT*), an almost-surely asymptotically optimal sampling-based planner based on BIT*. AIT* adapts its search to each problem instance by using an asymmetric bidirectional search to simultaneously estimate and exploit a problem-specific heuristic. This allows it to quickly find initial solutions and converge towards the optimum. AIT* solves the tested problems as fast as RRT-Connect while also converging towards the optimum.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2020, 6 + 2 pages, 5 figures, video available at https://youtu.be/twM723QM9T

Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques

Path planning is an active area of research essential for many applications in robotics. Popular techniques include graph-based searches and sampling-based planners. These approaches are powerful but have limitations. This paper continues work to combine their strengths and mitigate their limitations using a unified planning paradigm. It does this by viewing the path planning problem as the two subproblems of search and approximation and using advanced graph-search techniques on a sampling-based approximation. This perspective leads to Advanced BIT*. ABIT* combines truncated anytime graph-based searches, such as ATD*, with anytime almost-surely asymptotically optimal sampling-based planners, such as RRT*. This allows it to quickly find initial solutions and then converge towards the optimum in an anytime manner. ABIT* outperforms existing single-query, sampling-based planners on the tested problems in $\mathbb{R}^{4}$ and $\mathbb{R}^{8}$, and was demonstrated on real-world problems with NASA/JPL-Caltech.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2020, 6 + 1 pages, 3 figures, video available at https://youtu.be/VFdihv8Lq2

Asymptotically Optimal Sampling-Based Motion Planning Methods

Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews, https://www.annualreviews.org/. 25 pages. 2 figure

Leveraging multiple sources of information to search continuous spaces

Path planning algorithms can solve the problem of finding paths through continuous spaces. This problem appears in a wide range of applications, from navigating autonomous robots to automating assessments of surgical tolerances. The performance requirements on these algorithms tend to become more demanding as the problems they are applied to become more sophisticated. This simultaneous increase in performance requirements and application complexity calls for new approaches to the path planning problem and makes it an active area of research in robotics and beyond. This thesis demonstrates how different types of information can be leveraged to solve the path planning problem more effectively. Optimization-specific information can guide the search towards high-quality solutions, environment-specific information can exploit incremental information about the surroundings, and intent-specific information can directly align the search of a problem with its priorities. These three types of information are leveraged in this thesis by integrating advanced graph-search techniques in sampling-based path planning algorithms. The resulting planners, Advanced BIT* (ABIT*), Adaptively Informed Trees (AIT*), and Effort Informed Trees (EIT*), are theoretically shown to be almost-surely asymptotically optimal and experimentally demonstrated to outperform existing planners on diverse problems in abstract, robotic, and biomedical domains

2023 EELS field tests at Athabasca Glacier as an icy moon analogue environment

JPL is developing a versatile and highly intelligent Exobiology Extant Life Surveyor (EELS) robot that would enable access to subsurface oceans and near-surface liquid reservoirs through existing conduits, such as the vents at the south pole of Enceladus or the putative geysers on Europa. A key mobility requirement for future vent exploration missions will be the ability to carefully descend and hold position in the vent to collect and analyze samples while withstanding plume forces without human intervention. Furthermore, this must be accomplished in a highly uncertain environment, requiring versatile hardware and intelligent autonomy. To work towards that goal, we have prototyped the EELS 1.0 and EELS 1.5 robots for horizontal and vertical mobility, respectively, in icy terrain. Autonomous surface mobility of EELS 1.0 was previously validated in a variety of terrain, including snowy mountains, ice rinks, and desert sand. Vertical mobility of EELS 1.5 was developed on laboratory ice walls. This paper presents the first mobility trials for both robots on large-scale, natural icy terrain: the Athabasca Glacier located in Alberta, Canada, a terrestrial analogue to the surfaces and subsurfaces of icy moons. This paper provides a preliminary written record of the test campaign’s four major trials: 1) surface mobility with EELS 1.0, 2) vertical mobility with EELS 1.5, 3) science instrument validation, and 4) terramechanics experiments. During this campaign, EELS 1.5 successfully held position and descended ~1.5 m vertically in an icy conduit and EELS 1.0 demonstrated surface mobility on icy surfaces with undulations and slopes. A miniaturized capillary electrophoresis (CE) instrument built to the form factor of an EELS module was tested in flowing water on the glacier and successfully demonstrated automated sampling and in-situ analysis. Terramechanics experiments designed to better understand the interaction between different ice properties and the screws that propel the robot forwards were performed on horizontal and vertical surfaces. In this paper we report the outcomes of the four tests and discuss their implications for potential future icy missions. The field test also demonstrated EELS’s ability to support Earth science missions. Another potential near-term follow-on could be a technology demonstration on the Moon. This paper is a high level report on the execution of the field test. Data and results will be detailed in subsequent publications

Amelogenesis imperfecta: Next-generation sequencing sheds light on Witkop’s classification

Amelogenesis imperfecta (AI) is a heterogeneous group of genetic rare diseases disrupting enamel development (Smith et al., Front Physiol, 2017a, 8, 333). The clinical enamel phenotypes can be described as hypoplastic, hypomineralized or hypomature and serve as a basis, together with the mode of inheritance, to Witkop’s classification (Witkop, J Oral Pathol, 1988, 17, 547–553). AI can be described in isolation or associated with others symptoms in syndromes. Its occurrence was estimated to range from 1/700 to 1/14,000. More than 70 genes have currently been identified as causative.Objectives: We analyzed using next-generation sequencing (NGS) a heterogeneous cohort of AI patients in order to determine the molecular etiology of AI and to improve diagnosis and disease management.Methods: Individuals presenting with so called “isolated” or syndromic AI were enrolled and examined at the Reference Centre for Rare Oral and Dental Diseases (O-Rares) using D4/phenodent protocol (www.phenodent.org). Families gave written informed consents for both phenotyping and molecular analysis and diagnosis using a dedicated NGS panel named GenoDENT. This panel explores currently simultaneously 567 genes. The study is registered under NCT01746121 and NCT02397824 (https://clinicaltrials.gov/).Results: GenoDENT obtained a 60% diagnostic rate. We reported genetics results for 221 persons divided between 115 AI index cases and their 106 associated relatives from a total of 111 families. From this index cohort, 73% were diagnosed with non-syndromic amelogenesis imperfecta and 27% with syndromic amelogenesis imperfecta. Each individual was classified according to the AI phenotype. Type I hypoplastic AI represented 61 individuals (53%), Type II hypomature AI affected 31 individuals (27%), Type III hypomineralized AI was diagnosed in 18 individuals (16%) and Type IV hypoplastic-hypomature AI with taurodontism concerned 5 individuals (4%). We validated the genetic diagnosis, with class 4 (likely pathogenic) or class 5 (pathogenic) variants, for 81% of the cohort, and identified candidate variants (variant of uncertain significance or VUS) for 19% of index cases. Among the 151 sequenced variants, 47 are newly reported and classified as class 4 or 5. The most frequently discovered genotypes were associated with MMP20 and FAM83H for isolated AI. FAM20A and LTBP3 genes were the most frequent genes identified for syndromic AI. Patients negative to the panel were resolved with exome sequencing elucidating for example the gene involved ie ACP4 or digenic inheritance.Conclusion: NGS GenoDENT panel is a validated and cost-efficient technique offering new perspectives to understand underlying molecular mechanisms of AI. Discovering variants in genes involved in syndromic AI (CNNM4, WDR72, FAM20A … ) transformed patient overall care. Unravelling the genetic basis of AI sheds light on Witkop’s AI classification