A new algorithm named random particle optimization algorithm (RPOA) for local path planning problem of mobile robots in dynamic and unknown environments is proposed. The new algorithm inspired from bacterial foraging technique is based on particles which are randomly distributed around a robot. These particles search the optimal path toward the target position while avoiding the moving obstacles by getting help from the robot’s sensors. The criterion of optimal path selection relies on the particles distance to target and Gaussian cost function assign to detected obstacles. Then, a high level decision making strategy will decide to select best mobile robot path among the proceeded particles, and finally a low level decision control provides a control signal for control of considered holonomic mobile robot. This process is implemented without requirement to tuning algorithm or complex calculation, and furthermore, it is independent from gradient base methods such as heuristic (artificial potential field) methods. Therefore, in this paper, the problem of local mobile path planning is free from getting stuck in local minima and is easy computed. To evaluate the proposed algorithm, some simulations in three various scenarios are performed and results are compared by the artificial potential field.
Agricultural digitalization is providing growing amounts of real-time digital data. Biophysical simulation models can help interpret these data. However, these models are subject to complex uncertainties, which has prompted calls for interdisciplinary research to better understand and communicate modelling uncertainties and their impact on decision-making. This article develops two corresponding insights from an interdisciplinary project in a New Zealand agricultural research organization. First, we expand on a recent
Royal Society Open Science
journal article (van der Bles
et al
. 2019
Royal Society Open Science
6
, 181870 (
doi:10.1098/rsos.181870
)) and suggest a threefold conceptual framework to describe direct, indirect and contextual uncertainties associated with biophysical models. Second, we reflect on the process of developing this framework to highlight challenges to successful collaboration and the importance of a deeper engagement with interdisciplinarity. This includes resolving often unequal disciplinary standings and the need for early collaborative problem framing. We propose that both insights are complementary and informative to researchers and practitioners in the field of modelling uncertainty as well as to those interested in interdisciplinary environmental research generally. The article concludes by outlining limitations of interdisciplinary research and a shift towards transdisciplinarity that also includes non-scientists. Such a shift is crucial to holistically address uncertainties associated with biophysical modelling and to realize the full potential of agricultural digitalization.
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