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This paper considers the problem of the absent-minded driver who must choose between alternatives with different payoff under the conditions of imperfect recall and varying degrees of knowledge of the system. We show that agents with access to quantum resources, or with a quantum mechanical basis, can obtain superior performance as compared to classical agents. The paper also considers the problem...
The concept of electrical-mobility in opposition to the present oil-mobility is becoming even more attractive worldwide. Fast Charging Station (FCS) refers charging stations with nominal power equal or higher than 50 kW. The impacts of EV charging on electricity grids is becoming an increasingly important subject of study, but detailed knowledge about the future charging profiles of EVs appears to...
This paper considers the problem of motion planning for linear systems subject to Gaussian motion noise and proposes a risk-aware planning algorithm: CC-RRT∗-D. The proposed CC-RRT∗-D employs the chance-constraint approximation and leverages the asymptotically optimal property of RRT∗ framework to compute risk-aware and asymptotically optimal trajectories. By explicitly considering the state dependence...
Two ideas have gained traction in research in the robotics planning community. Activity planning has become popular where a library of predefined manipulation of the vehicle state is accessible, and is commonly used for missions with complex goal specifications. Another focus has been chance-constrained programming as a method of providing robust motion planning, in which the probability of failure...
This paper addresses the problem of large scale multi-agent motion planning in the presence of various uncertainties and under limited communication bandwidth. Obtaining an optimal solution while simultaneously addressing all the issues is a difficult problem. Towards this, we develop a decentralized motion planner that combines probabilistic approaches including the rapidly-exploring random tree...
Micro aerial vehicles operating outdoors must be able to maneuver through both dense vegetation and across empty fields. Existing approaches do not exploit the nature of such an environment. We have designed an algorithm which plans rapidly through free space and is efficiently guided around obstacles. In this paper we present SPARTAN (Sparse Tangential Network) as an approach to create a sparsely...
This paper presents a method for reasoning about the safety of traffic situations. More precisely, the problem of safety assessment for partial trajectories for vehicles is addressed. Therefore, the Inevitable Collision States (ICS) as well as its probabilistic generalization the Probabilistic Collision States (PCS) are used. Thereby, the assessment is performed for an infinite time horizon. For solving...
A predictive optimal velocity planning algorithm is proposed in this paper that uses traffic Signal Phase And Timing (SPAT) information to increase a vehicle's energy efficiency. Encouraged by positive results based on full SPAT information in [1], [2], this current paper focuses on benefits attainable with partial probabilistic information. Availability of signal phase data is categorized into none,...
This paper presents a strategy for a rover navigation in initially unknown or poorly known environments. The strategy consists in determining the areas in which information is relevant to gather for the rover to reach the goal. The approach relies on a probabilistic reasoning on the currently available information on the environment, and on the models of the vehicle perception and motion abilities...
In this paper, we compare deterministic and probabilistic path planning strategies for an autonomous unmanned aerial vehicle (UAV) network, where the objective is to explore a given area with obstacles and provide an overview image. We present both online and offline implementations of the algorithms as alternative solutions, where applicable, and analyze the performance of the offline implementations...
We present a novel way to learn sampling distributions for sampling-based motion planners by making use of expert data. We learn an estimate (in a non-parametric setting) of sample densities around semantic regions of interest, and incorporate these learned distributions into a sampling-based planner to produce natural plans. Our motivation is that certain aspects of the workspace have a local influence...
This paper introduces a new algorithm for probabilistic motion planning in arbitrary, uncertain vector fields, with emphasis on high-level planning for Montgolfieré balloons in the atmosphere of Titan. The goal of the algorithm is to determine what altitude-and what horizontal actuation, if any is available on the vehicle-to use to reach a goal location in the fastest expected time. The winds can...
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