Real-time path-planning using mixed-integer linear programming and global cost-to-go maps

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 93-95).With the advance in the fields of computer science, control and optimization, it is now possible to build aerial vehicles which do not need pilots. An important capability for such autonomous vehicles is to be able to generate their own path to navigate in a constrained environment and accomplish mission objectives, such as reaching waypoints in minimal time. To account for dynamic changes in the environment, perturbations, modeling errors and modifications in the mission scenario, the trajectory needs to be continuously re-optimized online based on the latest available updates. However, to allow for high update rates, the trajectory optimization problem needs to be simple enough to be solved quickly. Optimizing for a continuous trajectory of a dynamically-constrained vehicle in the presence of obstacles is an infinite-dimension nonlinear optimal control problem. Such a problem is intractable in real-time and simplifications need to be made. In this thesis, the author presents the mechanisms used to design a path-planner with real-time and long-range capabilities. The approach relies on converting the optimal control problem into a parameter optimization one whose horizon can be reduced by using a global cost-to-go function to provide an approximate cost for the tail of the trajectory.(cont.) Thus only the short-term trajectory is being constantly optimized online based on a mixed integer linear programming formulation that accounts for the vehicle's performance. The cost-to-go function presented in this thesis has the feature to be tailored to both the environment and the vehicle's maneuvering capabilities. The author then implements and demonstrates a path-planner software based on the presented approach for a real unmanned helicopter, the Renegade, that flew within the DARPA SEC program. A full description of the capabilities and functions supported by the planner software are provided. Hardware-in-the-loop simulation results are provided to illustrate the performance of the system.by Olivier Toupet.S.M

Formation of six-membered palladacycles from phenanthroline Pd(II) bisacetate precursors and phenylisocyanate

Synthesis and characterization of 5a-d. CCDC-221834 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via: www.ccdc.cam.ac.uk/data_request.cif.International audiencePhenylisocyanate reacts with palladium(II) bis-acetate phenanthroline complexes to give six-membered palladacycles in nearly quantitative yields. In this new reaction, the acetate ligands act as decarbonylating agents toward the isocyanate functionality by possibly forming the isolated palladacycles via an intramolecular rearrangement

Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems

We present a light-weight body-terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated safety checks on all the candidate paths at a small interval. Predicting the future rover state requires simulating the vehicle settling on the terrain, which involves an inverse-kinematics problem with iterative nonlinear optimization under geometric constraints. However, such expensive computation is intractable for slow spacecraft computers, such as RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover. We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation. It obtains those bounds by estimating the lowest and highest heights that each wheel may reach given the underlying terrain, and calculating the worst-case vehicle configuration associated with those extreme wheel heights. The bounds are guaranteed to be conservative, hence ensuring vehicle safety during autonomous navigation. ACE is planned to be used as part of the new onboard path planner of the Mars 2020 rover. This paper describes the algorithm in detail and validates our claim of conservatism and fast computation through experiments

Continuous Symmetry Breaking Induced by Ion Pairing Effect in Heptamethine Cyanine Dyes: Beyond the Cyanine Limit

WOS:000276009500058International audienceThe association of heptamethine cyanine cation 1(+) with various counterions A (A = Br(-), I(-), PF(6)(-), SbF(6)(-), B(C(6)F(5))(4)(-), TRISPHAT) was realized. The six different ion pairs have been characterized by X-ray diffraction, and their absorption properties were studied in polar (DCM) and apolar (toluene) solvents. A small, hard anion (Br(-)) is able to strongly polarize the polymethine chain, resulting in the stabilization of an asymmetric dipolar-like structure in the crystal and in nondissociating solvents. On the contrary, in more polar solvents or when it is associated with a bulky soft anion (TRISPHAT or B(C(6)F(5))(4)(-)), the same cyanine dye adopts preferentially the ideal polymethine state. The solid-state and solution absorption properties of heptamethine dyes are therefore strongly correlated to the nature of the counterion

Optical electron transfer through 2,7-diethynylfluorene spacers in mixed-valent complexes containing electron-rich "(η2-dppe)(η5-C5Me5)Fe" endgroups.

International audienceWe report in this communication the study of the intramolecular electron transfer through a 2,7-diethynylfluorenyl spacer in the Fe(II)/Fe(III) mixed-valent (MV) complex [(η(2)-dppe)(η(5)-C(5)Me(5))FeC≡C(2,7-C(21)H(24))C≡CFe(η(5)-C(5)Me(5))(η(2)-dppe)][PF(6)] (1[PF(6)]). The complex is generated in situ by comproportionation from its homovalent dinuclear Fe(II) and Fe(III) parents (1 and 1[PF(6)](2)). It is shown that electronic delocalization is much more effective through a 2,7-fluorenyl than through a 4,4'-biphenyl bridging unit

Cycloalkyl-based unsymmetrical unsaturated (U2)-NHC ligands : flexibility and dissymmetry in ruthenium-catalysed olefin metathesis.

International audienceAir-stable Ru-indenylidene and Hoveyda-type complexes bearing new unsymmetrical unsaturated N-heterocyclic carbene (U2-NHC) ligands combining a mesityl unit and a flexible cycloalkyl moiety as N-substituents were synthesised. Structural features, chemical stabilities and catalytic profiles in olefin metathesis of this new library of cycloalkyl-based U2-NHC Ru complexes were studied and compared with their unsymmetrical saturated NHC-Ru homologues as well as a set of commercially available Ru-catalysts bearing either symmetrical SIMes or IMes NHC ligands

Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)

Enhanced AutoNav (ENav), the baseline surface navigation software for NASA's Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software

Stable Near-Infrared Anionic Polymethine Dyes: Structure, Photophysical, and Redox Properties

International audienceThe concept of cyanine has been successfully extended to anionic heptamethine dye featuring tricyanofuran (TCF) moieties in terms of structure, reactivity and photophysical properties. Importantly, absorption and emission are red-shifted compared to its classical cationic analog without any cost in term of thermal stability. In addition to its "cyanine" behavior, this molecule exhibits further redox properties: oxidation and reduction led to the reversible formation of radical species whose absorption is in marked contrast with that of cyanines

NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)