A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.

A new path planning method based on sparse A* algorithm with map segmentation

Due to the complexity of map modeling, the massive computation and high redundancy of the traditional A* algorithm will greatly reduce the efficiency of pathfinding, resulting in huge performance consumption. Meanwhile, limited by neighborhood search strategy in grid map, the traditional A* algorithm is actually unable to achieve the optimal path in the global sense. To solve these problems, this paper proposes an improved A* algorithm based on graph preprocessing. First, the free space on the map was decomposed into several polygon regions using the improved convex decomposition method based on Maklink. Then, each region was coded into feature nodes according to A* algorithm. Finally, an optimal region passage was found based on the principle of A* algorithm, in which the global optimal path solution was obtained. Compared with the traditional A* algorithm and other classical path planning algorithms, the proposed algorithm has significant advantages in planning speed, path cost, stability, and completeness.

Alienation among Management Scholars

In a conceptual synthesis of a large body of literature, we explore drivers of alienation among management scholars through the lens of a classical path goal motivational model. Some scholars have become alienated from doing research due to the socio-political context of publishing. Although their unitary complaints are irksome, collectively these can amount to a career gauntlet – “a perfect storm” – of compounding challenges that permanently drive away scholars from doing academic research. To better understand this process, we show the pernicious interplay of these challenges in an expectancy model of costs, risks and rewards, each manifesting several of Blauner's (1964) drivers of work alienation – meaninglessness, powerlessness, self-estrangement and isolation. We conclude by suggesting mitigating conditions, summary propositions, and remedial implications.

Direct Non-adiabatic Simulations of the Photoinduced Charge Transfer Dynamics

We apply direct non-adiabatic dynamics simulations to investigate photoinduced charge transfer reactions. Our approach is based on the mixed quantum-classical fewest switches surface hopping (FSSH) method that treats the transferring electron through time-dependent density functional theory and the nuclei classically. The photoinduced excited state is modeled as a transferring single-electron that initially occupies the LUMO of the donor molecule/moiety. This single-particle electronic wavefunction is then propagated quantum mechanically by solving the time-dependent Schr\"odinger equation in the basis of the instantaneous molecular orbitals (MOs) of the entire system. The non-adiabatic transitions among electronic states are modeled using the FSSH approach within the classical-path approximation. We apply this approach to simulate the photoinduced charge transfer dynamics in a few well-characterized molecular systems. Our results are in excellent agreement with both the experimental measurements and high-level (yet expensive) theoretical results.

Direct Non-adiabatic Simulations of the Photoinduced Charge Transfer Dynamics

We apply direct non-adiabatic dynamics simulations to investigate photoinduced charge transfer reactions. Our approach is based on the mixed quantum-classical fewest switches surface hopping (FSSH) method that treats the transferring electron through time-dependent density functional theory and the nuclei classically. The photoinduced excited state is modeled as a transferring single-electron that initially occupies the LUMO of the donor molecule/moiety. This single-particle electronic wavefunction is then propagated quantum mechanically by solving the time-dependent Schr\"odinger equation in the basis of the instantaneous molecular orbitals (MOs) of the entire system. The non-adiabatic transitions among electronic states are modeled using the FSSH approach within the classical-path approximation. We apply this approach to simulate the photoinduced charge transfer dynamics in a few well-characterized molecular systems. Our results are in excellent agreement with both the experimental measurements and high-level (yet expensive) theoretical results.

Quantum Calcium-Ion Affective Influences Measured by EEG

Background: Since circa 1980, a model of neocortical interactions, Statistical Mechanics of Neocortical Interactions (SMNI) has been successful in calculating many experimental phenomena, including fits to electroencephalographic (EEG) data in attention tasks, using an importance-sampling code Adaptive Simulated Annealing (ASA). The SMNI model is developed in the context of classical path-integrals, which affords intuitive insights as well as direct numerical benefits, e.g., using the effective Action as a a cost/objective function for parameter fits to data. Objective: Previous authors have fit affective EEG data to neural-network models. This project seeks to use models based on physics and biology to fit this same data. Previous work showed improvements in fits to EEG for attention states; this project extends these methods to affective states. Method: Path integrals are used in both classical and quantum contexts. Classical path integrals are used to define a cost/objective function to fit data, and quantum path integrals are used to derive a closed-form analytic expression for Ca-ion waves in the presence of a magnetic vector potential which is generated by highly synchronous neuronal firings which give rise to EEG. ASA is used to fit EEG data. Results: The mathematical-physics and computer parts of the study are successful, in that cost/objective functions used to fit EEG data using these models are consistent with previous work published by other authors. However, since the SMNI model includes these quantum effects, this is another reason to continue examining these issues. The results here are consistent, not better, than previous work using neural-network models, albeit only one parameter was used here, instead of multiple filters and kernels used previously on such data. Conclusion: Although these quantum effects are highly speculative, explicit calculations have shown them to be consistent with experimental data, at least to date. The current supercomputer project extends this model to affective/emotion data. Results from several authors using neural-network approaches at individual electrode sites show some predictive capabilities; the results given here are consistent with these other results. However, since the SMNI model includes these quantum effects, this is another reason to continue examining these issues.

Amir Goldstein, Derekh rabat panim: tziyonuto shel Zeev Jabotinsky lenokhaḥ haantishemiyut (Zionism and Anti-Semitism in the Thought and Action of Ze’ev Jabotinsky). Sdeh Boker: The Ben-Gurion Institute for the Study of Israel and Zionism, 2015, 496 pp.

No matter how much has been written about Zeev Jabotinsky, founder of the Revisionist movement, his persona and writings continue to fascinate scholars. Recently, it seems, there has been a tendency to examine Jabotinsky’s early thinking and activity in subject-focused contexts.1 Amir Goldstein’s book takes a more classical path: by probing Jabotinsky’s attitude toward antisemitism, he proposes to shed light on Jabotinsky’s Zionist patterns of thinking throughout his lifetime....