Mapping dynamic environments using Markov random field models

This paper focuses on dynamic environments for mobile robots and proposes a new mapping method combining hidden Markov models (HMMs) and Markov random fields (MRFs). Grid cells are used to represent the dynamic environment. The state change of every grid cell is modelled by an HMM with an unknown transition matrix. MRFs are applied to consider the dependence between different transition matrices. The unknown parameters are learnt from not only the corresponding observations but also its neighbours. Given the dependence, parameter maps are smooth. Expectation maximization (EM) is applied to obtain the best parameters from observations. Finally, a simulation is done to evaluate the proposed method

Regression-Based Online Situation Recognition for Vehicular Traffic Scenarios

Abstract-In this paper, we present an approach for learning generalized models for traffic situations. We formulate the problem using a dynamic Bayesian network (DBN) from which we learn the characteristic dynamics of a situation from labeled trajectories using kernel regression. For a new and unlabeled trajectory, we can then infer the corresponding situation by evaluating the data likelihood for the individual situation models. In experiments carried out on laser range data gathered on a car in real traffic and in simulation, we show that we can robustly recognize different traffic situations even from trajectories corresponding to partial situation instances

Highly strained, radially π-conjugated porphyrinylene nanohoops

Small π-conjugated nanohoops are difficult to prepare, but offer an excellent platform for studying the interplay between strain and optoelectronic properties, and, increasingly, these shape-persistent macrocycles find uses in host-guest chemistry and self-assembly. We report the synthesis of a new family of radially π-conjugated porphyrinylene/phenylene nanohoops. The strain energy in the smallest nanohoop [2]CPT is approximately 54 kcal mol⁻¹, which results in a narrowed HOMO-LUMO gap and a red shift in the visible part of the absorption spectrum. Because of its high degree of preorganization and a diameter of ca. 13 Å, [2]CPT was found to accommodate C₆₀ with a binding affinity exceeding 10⁸ M⁻¹ despite the fullerene not fully entering the cavity of the host (X-ray crystallography). Moreover, the ?-extended nanohoops [2]CPTN, [3]CPTN, and [3]CPTA (N for 1,4-naphthyl; A for 9,10-anthracenyl) have been prepared using the same strategy, and [2]CPTN has been shown to bind C₇₀ 5 times more strongly than [2]CPT. Our failed synthesis of [2]CPTA highlights a limitation of the experimental approach most commonly used to prepare strained nanohoops, because in this particular case the sum of aromatization energies no longer outweighs the buildup of ring strain in the final reaction step (DFT calculations). These results indicate that forcing ring strain onto organic semiconductors is a viable strategy to fundamentally influence both optoelectronic and supramolecular properties

Physical and functional interactions between human mitochondrial single-stranded DNA-binding protein and tumour suppressor p53

Single-stranded DNA-binding proteins (SSB) form a class of proteins that bind preferentially single-stranded DNA with high affinity. They are involved in DNA metabolism in all organisms and serve a vital role in replication, recombination and repair of DNA. In this report, we identify human mitochondrial SSB (HmtSSB) as a novel protein-binding partner of tumour suppressor p53, in mitochondria. It binds to the transactivation domain (residues 1–61) of p53 via an extended binding interface, with dissociation constant of 12.7 (± 0.7) μM. Unlike most binding partners reported to date, HmtSSB interacts with both TAD1 (residues 1–40) and TAD2 (residues 41–61) subdomains of p53. HmtSSB enhances intrinsic 3′-5′ exonuclease activity of p53, particularly in hydrolysing 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG) present at 3′-end of DNA. Taken together, our data suggest that p53 is involved in DNA repair within mitochondria during oxidative stress. In addition, we characterize HmtSSB binding to ssDNA and p53 N-terminal domain using various biophysical measurements and we propose binding models for both

ARMO - Adaptive Road Map Optimization for Large Robot Teams

Autonomous robot teams that simultaneously dispatch transportation tasks are playing more and more an important role in present logistic centers and manufacturing plants. In this paper we consider the problem of robot motion planning for large robot teams in the industrial domain. We present adaptive road map optimization (ARMO) that is capable of adapting the road map in real time whenever the environment has changed. Based on linear programming, ARMO computes an optimal road map according to current environmental constraints (including human whereabouts) and the current demand for transportation tasks from loading stations in the plant. For detecting dynamic changes, the environment is describe by a grid map augmented with a hidden Markov model (HMM). We show experimentally that ARMO outperforms decoupled planning in terms of computation time and time needed for task completion.Artificial Intelligence & Integrated Computer System