Magnetic tests for magnetosome chains in Martian meteorite ALH84001

Transmission electron microscopy studies have been used to argue that magnetite crystals in carbonate from Martian meteorite ALH84001 have a composition and morphology indistinguishable from that of magnetotactic bacteria. It has even been claimed from scanning electron microscopy imaging that some ALH84001 magnetite crystals are aligned in chains. Alignment of magnetosomes in chains is perhaps the most distinctive of the six crystallographic properties thought to be collectively unique to magnetofossils. Here we use three rock magnetic techniques, low-temperature cycling, the Moskowitz test, and ferromagnetic resonance, to sense the bulk composition and crystallography of millions of ALH84001 magnetite crystals. The magnetic data demonstrate that although the magnetite is unusually pure and fine-grained in a manner similar to terrestrial magnetofossils, most or all of the crystals are not arranged in chains

Optimal Routing for Autonomous Taxis using Distributed Reinforcement Learning

In this paper, a learning-based optimal transportation algorithm for autonomous taxis and ridesharing vehicles is introduced. The goal is to design a mechanism to solve the routing problem for a fleet of autonomous vehicles in real-time in order to maximize the transportation company’s profit. To solve this problem, the system is modeled as a Markov Decision Process (MDP) using past customers data. By solving the defined MDP, a centralized high-level planning recommendation is obtained, where this offline solution is used as an initial value for the real-time learning. Then, a distributed SARSA reinforcement learning algorithm is proposed to capture the model errors and the environment changes, such as variations in customer distributions in each area, traffic, and fares, thereby providing an accurate model and optimal policies in real-time. Agents are using only their local information and interaction, such as current passenger requests and estimates of neighbors’ tasks and their optimal actions, to obtain the optimal policies in a distributed fashion. The agents use the estimated values of each action, provided by distributed SARSA reinforcement learning, in a distributed game-theory based task assignment to select their conflict-free customers. Finally, the customers data provided by the city of Chicago is used to validate the proposed algorithms

GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning

We present GLAS: Global-to- Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage of centralized planning of avoiding local minima with the advantage of decentralized controllers of scalability and distributed computation. In particular, our synthesized policies only require relative state information of nearby neighbors and obstacles, and compute a provably-safe action. Our approach has three major components: i) we generate demonstration trajectories using a global planner and extract local observations from them, ii) we use deep imitation learning to learn a decentralized policy that can run efficiently online, and iii) we introduce a novel differentiable safety module to ensure collision-free operation, thereby allowing for end-to-end policy training. Our numerical experiments demonstrate that our policies have a 20% higher success rate than optimal reciprocal collision avoidance, ORCA, across a wide range of robot and obstacle densities. We demonstrate our method on an aerial swarm, executing the policy on low-end microcontrollers in real-time

Holding On: Older Californians With Disabilities Rely on Public Services to Remain Independent

Presents findings from a study of low-income older Californians with disabilities receiving Medicare, Medi-Cal, and In-Home Supportive Services; their unmet physical, mental health, and social needs; and limited care options. Outlines policy implications

Coordinated Motion Planning for On-Orbit Satellite Inspection using a Swarm of Small-Spacecraft

This paper addresses the problem of how to plan optimal motion for a swarm of on-orbit servicing (OOS) small-spacecraft remotely inspecting a non-cooperative client spacecraft in Earth orbit. With the goal being to maximize the information gathered from the coordinated inspection, we present an integrated motion planning methodology that is a) fuel-efficient to ensure extended operation time and b) computationally-tractable to make possible on-board re-planning for improved exploration. Our method is decoupled into first offline selection of optimal orbits, followed by online coordinated attitude planning. In the orbit selection stage, we numerically evaluate the upper and lower bounds of the information gain for a discretized set of passive relative orbits (PRO). The algorithm then sequentially assigns orbits to each spacecraft using greedy heuristics. For the attitude planning stage, we propose a dynamic programming (DP) based attitude planner capable of addressing vehicle and sensor constraints such as attitude control system specifications, sensor field of view, sensing duration, and sensing angle. Finally, we validate the performance of the proposed algorithms through simulation of a design reference mission involving 3U CubeSats inspecting a satellite in low Earth orbit

CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems

The key innovation of our analytical method, CaRT, lies in establishing a new hierarchical, distributed architecture to guarantee the safety and robustness of a given learning-based motion planning policy. First, in a nominal setting, the analytical form of our CaRT safety filter formally ensures safe maneuvers of nonlinear multi-agent systems, optimally with minimal deviation from the learning-based policy. Second, in off-nominal settings, the analytical form of our CaRT robust filter optimally tracks the certified safe trajectory, generated by the previous layer in the hierarchy, the CaRT safety filter. We show using contraction theory that CaRT guarantees safety and the exponential boundedness of the trajectory tracking error, even under the presence of deterministic and stochastic disturbance. Also, the hierarchical nature of CaRT enables enhancing its robustness for safety just by its superior tracking to the certified safe trajectory, thereby making it suitable for off-nominal scenarios with large disturbances. This is a major distinction from conventional safety function-driven approaches, where the robustness originates from the stability of a safe set, which could pull the system over-conservatively to the interior of the safe set. Our log-barrier formulation in CaRT allows for its distributed implementation in multi-agent settings. We demonstrate the effectiveness of CaRT in several examples of nonlinear motion planning and control problems, including optimal, multi-spacecraft reconfiguration.Comment: IEEE Conference on Decision and Control (CDC), Preprint Version, Accepted July, 202