Bayesian Active Edge Evaluation on Expensive Graphs

Robots operate in environments with varying implicit structure. For instance, a helicopter flying over terrain encounters a very different arrangement of obstacles than a robotic arm manipulating objects on a cluttered table top. State-of-the-art motion planning systems do not exploit this structure, thereby expending valuable planning effort searching for implausible solutions. We are interested in planning algorithms that actively infer the underlying structure of the valid configuration space during planning in order to find solutions with minimal effort. Consider the problem of evaluating edges on a graph to quickly discover collision-free paths. Evaluating edges is expensive, both for robots with complex geometries like robot arms, and for robots with limited onboard computation like UAVs. Until now, this challenge has been addressed via laziness i.e. deferring edge evaluation until absolutely necessary, with the hope that edges turn out to be valid. However, all edges are not alike in value - some have a lot of potentially good paths flowing through them, and some others encode the likelihood of neighbouring edges being valid. This leads to our key insight - instead of passive laziness, we can actively choose edges that reduce the uncertainty about the validity of paths. We show that this is equivalent to the Bayesian active learning paradigm of decision region determination (DRD). However, the DRD problem is not only combinatorially hard, but also requires explicit enumeration of all possible worlds. We propose a novel framework that combines two DRD algorithms, DIRECT and BISECT, to overcome both issues. We show that our approach outperforms several state-of-the-art algorithms on a spectrum of planning problems for mobile robots, manipulators and autonomous helicopters

The Provable Virtue of Laziness in Motion Planning

The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along shortest paths between the source and target. These algorithms were designed to minimize the number of edge evaluations in settings where edge evaluation dominates the running time of the algorithm; but how close to optimal are LazySP algorithms in terms of this objective? Our main result is an analytical upper bound, in a probabilistic model, on the number of edge evaluations required by LazySP algorithms; a matching lower bound shows that these algorithms are asymptotically optimal in the worst case

Belief Representations for Planning with Contact Uncertainty

While reaching for your morning coffee you may accidentally bump into the table, yet you reroute your motion with ease and grab your cup. An effective autonomous robot will need to have a similarly seamless recovery from unexpected contact. As simple as this may seem, even sensing this contact is a challenge for many robots, and when detected contact is often treated as an error that an operator is expected to resolve. Robots operating in our daily environments will need to reason about the information they have gained from contact and replan autonomously. This thesis examines planning under uncertainty with contact sensitive robot arms. Robots do not have skin and cannot precisely sense the location of contact. This leads to the proposed Collision Hypothesis Set model for representing a belief over the possible occupancy of the world sensed through contact. To capture the specifics of planning in an unknown world with this measurement model, this thesis develops a POMDP approach called the Blindfolded Traveler's Problem. A good prior over the possible obstacles the robot might encounter is key to effective planning. This thesis develops a neural network approach for sampling potential obstacles that are consistent with both what a robot sees from its camera and what it feels through contact.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169845/1/bsaund_1.pd

Lazy Receding Horizon A* for Efficient Path Planning in Graphs with Expensive-to-Evaluate Edges

Motion-planning problems, such as manipulation in cluttered environments, often require a collision-free shortest path to be computed quickly given a roadmap graph. Typically, the computational cost of evaluating whether an edge of the roadmap graph is collision-free dominates the running time of search algorithms. Algorithms such as Lazy Weighted A* (LWA*) and LazySP have been proposed to reduce the number of edge evaluations by employing a lazy lookahead (one-step lookahead and infinite-step lookahead, respectively). However, this comes at the expense of additional graph operations: the larger the lookahead, the more the graph operations that are typically required. We propose Lazy Receding-Horizon A* (LRA*) to minimize the total planning time by balancing edge evaluations and graph operations. Endowed with a lazy lookahead, LRA* represents a family of lazy shortest-path graph-search algorithms that generalizes LWA* and LazySP. We analyze the theoretic properties of LRA* and demonstrate empirically that, in many cases, to minimize the total planning time, the algorithm requires an intermediate lazy lookahead. Namely, using an intermediate lazy lookahead, our algorithm outperforms both LWA* and LazySP. These experiments span simulated random worlds in $\mathbb{R}^2$ and $\mathbb{R}^4$, and manipulation problems using a 7-DOF manipulator.Comment: 16 pages; typos corrected; revised text; results unchange

NASA Technology Plan 1998

This NASA Strategic Plan describes an ambitious, exciting vision for the Agency across all its Strategic Enterprises that addresses a series of fundamental questions of science and research. This vision is so challenging that it literally depends on the success of an aggressive, cutting-edge advanced technology development program. The objective of this plan is to describe the NASA-wide technology program in a manner that provides not only the content of ongoing and planned activities, but also the rationale and justification for these activities in the context of NASA's future needs. The scope of this plan is Agencywide, and it includes technology investments to support all major space and aeronautics program areas, but particular emphasis is placed on longer term strategic technology efforts that will have broad impact across the spectrum of NASA activities and perhaps beyond. Our goal is to broaden the understanding of NASA technology programs and to encourage greater participation from outside the Agency. By relating technology goals to anticipated mission needs, we hope to stimulate additional innovative approaches to technology challenges and promote more cooperative programs with partners outside NASA who share common goals. We also believe that this will increase the transfer of NASA-sponsored technology into nonaerospace applications, resulting in an even greater return on the investment in NASA

Towards (R)evolving Cities Urban fragilities and prospects in the 21st century

Towards (R)evolving Cities: Urban Fragilities and Prospects in the 21st century first questions how we perceive the ‘intelligence’ of a city. The New Frontier of development for urban civilisations certainly includes digital and technological evolution, but it does not consider technology to be the final answer to all contemporary cities’ problems. The formidable challenges of the COVID-19 pandemic have thrown existing urban fragilities into stark relief. At the same time however they have highlighted the potential of digital solutions for reaching a new level of interconnected civility. (R)evolving cities evolve by adopting the principles of the circular economy in the higher interest of their citizens’ well-being: they consume therefore without devouring, recycle as much as possible what they metabolize, limit the effects of their ecological footprint and ultimately lead their inhabitants, with maternal guidance and care, to a new idea of citizenship. As protagonists of this evolutionary leap, the citizens of (R)evolving cities will abandon their predatory approach, reaching a higher stage of integration in the ecosystem and becoming more respectful of reciprocal relationships. (R)evolving cities are above all ‘polite’ cities, or rather cities whose citizens are consciously educated in the principles of sustainable development, the essential basis for contemporary civil coexistence

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so