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In the paper, the formation control problem in unknown environments for networked multi-unmanned aerial vehicle systems (UAVSs) is resolved under a nonlinear differential game (NL-DDG) framework. The main challenge of this framework is how to obtain feedback Nash strategies, which typically overly rely on global information and cannot ensure the existence of Nash strategies in unknown environments. Toward this goal, we initially design collision avoidance rules to ensure the safety of each UAV. Subsequently, we utilize an inverse optimal control method to construct the NL-DDG that incorporates both formation control and collision avoidance costs, enabling the derivation of analytical forms of Nash strategies relying solely on local information and minimizing performance metrics. In addition, the existence of feedback Nash strategy can be guaranteed with an undirected and connected information topology, which represents the optimality for the UAVSs. Moreover, we analyze the stability of the closed-loop system. Finally, the simulation results validate the effectiveness of the proposed scheme.

Suppose that a circular fire spreads in the plane at unit speed. A single fire fighter can build a barrier at speed $v>1$. How large must $v$ be to ensure that the fire can be contained, and how should the fire fighter proceed? We contribute two results.

First, we analyze the natural curve ${\text{FF}}_v$ that develops when the fighter keeps building, at speed $v$, a barrier along the boundary of the expanding fire. We prove that the behavior of this spiralling curve is governed by a complex function ${(e^{wZ}-sZ)}^{-1}$, where $w$ and $s$ are real functions of $v$. For $v>v_c=2.6144\dots$ all zeroes are complex conjugate pairs. If $\varphi$ denotes the complex argument of the conjugate pair nearest to the origin then, by residue calculus, the fire fighter needs $\mathrm{\Theta }(1/\varphi )$ rounds before the fire is contained. As $v$ decreases towards $v_c$ these two zeroes merge into a real one, so that argument $\varphi$ goes to 0. Thus, curve ${\text{FF}}_v$ does not contain the fire if the fighter moves at speed $v=v_c$. (That speed $v>v_c$ is sufficient for containing the fire has been proposed before by Bressan et al. [6], who constructed a sequence of logarithmic spiral segments that stay strictly away from the fire.)

Second, we show that for any curve that visits the four coordinate half-axes in cyclic order, and in increasing distances from the origin the fire can not be contained if the speed $v$ is less than 1.618…, the golden ratio.

Space and time are fundamental attributes of the external world. Deciphering the brain mechanisms involved in processing the surrounding environment is one of the main challenges in neuroscience. This is particularly defiant when situations change rapidly over time because of the intertwining of spatial and temporal information. However, understanding the cognitive processes that allow coping with dynamic environments is critical, as the nervous system evolved in them due to the pressure for survival. Recent experiments have revealed a new cognitive mechanism called time compaction. According to it, a dynamic situation is represented internally by a static map of the future interactions between the perceived elements (including the subject itself). The salience of predicted interactions (e.g. collisions) over other spatiotemporal and dynamic attributes during the processing of time-changing situations has been shown in humans, rats, and bats. Motivated by this ubiquity, we study an artificial neural network to explore its minimal conditions necessary to represent a dynamic stimulus through the future interactions present in it. We show that, under general and simple conditions, the neural activity linked to the predicted interactions emerges to encode the perceived dynamic stimulus. Our results show that this encoding improves learning, memorization and decision making when dealing with stimuli with impending interactions compared to no-interaction stimuli. These findings are in agreement with theoretical and experimental results that have supported time compaction as a novel and ubiquitous cognitive process.

Efficient co-channel and adjacent channel interference rejection is often one of the most demanding requirements for wireless receivers. Independent Component Analysis (ICA) has been previously applied to realize interference suppression. In particular, the fixed-point FastICA and complex FastICA algorithms can successfully perform blind signal extraction for real and complex valued communication signals in stationary or slow fading environments. Both algorithms exhibit fast convergence speed and impressive accuracy due to their Newton type fixed-point iteration. However, under dynamic channel conditions often encountered in practice, the fixed-point algorithms' performance is significantly degraded. In this contribution, a novel complex block adaptive ICA algorithm and its simplified real version is proposed, that overcome this limitation for the separation of complex valued and real signals with known source distributions. The new methods exploit prior information about the modulation scheme of the communication signals of interest, and achieve improved interference suppression performance in dynamic channel environments. The proposed complex ICA algorithm is called Complex Optimum Block Adaptive ICA (Complex OBA-ICA), and its abridged version for separating real signals is called General Optimum Block Adaptation ICA (GOBA-ICA). The proposed methods are applied to interference rejection in linearly and abruptly flat fading dynamic environments for diversity QPSK and BPSK wireless receivers. Simulation results show that the presented techniques demonstrate better convergence properties and accuracy as compared to the complex FastICA and FastICA algorithms.

Brazil is an agricultural nation whose process of spraying pesticides is mainly carried out by using aircrafts. However, the use of aircrafts with on-board pilots has often resulted in chemicals being sprayed outside the intended areas. The precision required for spraying on crop fields is often impaired by external factors, like changes in wind speed and direction. To address this problem, ensuring that the pesticides are sprayed accurately, this paper proposes the use of artificial neural networks (ANN) on programmable UAVs. For such, the UAV is programmed to spray chemicals on the target crop field considering dynamic context. To control the UAV ight route planning, we investigated several optimization techniques including Particle Swarm Optimization (PSO). We employ PSO to find near-optimal parameters for static environments and then train a neural network to interpolate PSO solutions in order to improve the UAV route in dynamic environments. Experimental results showed a gain in the spraying precision in dynamic environments when ANN and PSO were combined. We demonstrate the improvement in figures when compared against the exclusive use of PSO. This approach will be embedded in UAVs with programmable boards, such as Raspberry PIs or Beaglebones. The experimental results demonstrate that the proposed approach is feasible and can meet the demand for a fast response time needed by the UAV to adjust its route in a highly dynamic environment, while seeking to spray pesticides accurately.

The swarm intelligence optimization algorithms are used widely in static purposes and applications. They solve the static optimization problems successfully. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments as well. The dynamic optimization problems indicate the ones whose solutions change over time. The artificial bee colony algorithm is one of the swarm intelligence optimization algorithms. In this study, a clustering and memory-based chaotic artificial bee colony algorithm, denoted by CMCABC, has been proposed for solving the dynamic optimization problems. A chaotic system has a more accurate prediction for future in the real-world applications compared to a random system, because in the real-world chaotic behaviors have emerged, but random behaviors havenot been observed. In the proposed CMCABC method, explicit memory has been used to save the previous good solutions which are not very old. Maintaining diversity in the dynamic environments is one of the fundamental challenges while solving the dynamic optimization problems. Using clustering technique in the proposed method can well maintain the diversity of the problem environment. The proposed CMCABC method has been tested on the moving peaks benchmark (MPB). The MPB is a good simulator to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMCABC method compared to the other state-of-the-art methods in solving dynamic optimization problems.

We present a novel optimization-based motion planning algorithm for high degree-of-freedom (DOF) robots in dynamic environments. Our approach decomposes the high-dimensional motion planning problem into a sequence of low-dimensional sub-problems. We compute collision-free and smooth paths using optimization-based planning and trajectory perturbation for each sub-problem. The overall algorithm does not require a priori knowledge about global motion or trajectories of dynamic obstacles. Rather, we compute a conservative local bound on the position or trajectory of each obstacle over a short time and use the bound to incrementally compute a collision-free trajectory for the robot. The high-DOF robot is treated as a tightly coupled system, and we incrementally use constrained coordination to plan its motion. We highlight the performance of our planner in simulated environments on robots with tens of DOFs.

Purpose: This study aims to analyse the influence of dynamic environments on technology and market-sensing capability. It also aims to examine the influence of technology and market-sensing capability on innovation performance. Besides, it aims to explore the influence of innovation performance on the organisation’s financial performance. It also aims to examine the influence of technology and market-sensing capability on the organisation’s financial performance. Design/methodology/approach: The research model was tested using a structural equation modelling design based on survey data from 183 Saudi companies from different sectors, used by software (SPSS v. 28 and SmartPLS v. 4). Findings: This study shows that dynamic environments strongly influence both financial and innovation performance. However, this study shows that innovation performance does not directly affect financial performance. Furthermore, this study shows that dynamic environments strongly influence both technology and market-sensing capability. Likewise, this study shows that both technology and market-sensing capability strongly influence financial performance. However, this study shows that both technology and market-sensing capability do not directly affect innovation performance. Originality/value: This study has demonstrated that dynamic environments will have a direct effect on the financial performance of companies by compelling them to allocate their resources more strategically and flexibly in order to adapt to the changing and unpredictable conditions of dynamic environments. Moreover, this study has also demonstrated that dynamic environments will have a direct effect on the technology and market-sensing capability of organisations. This will motivate organisations to gather and analyse information about customers, competitors, and market trends, and to use this information to identify and exploit opportunities for innovation, differentiation, and value creation for customers and stakeholders. In addition to the previous findings, this study has also shown that dynamic environments will improve the technology and market-sensing capability of organisations. This will inspire organisations to collect and analyse information about the needs, preferences, and behaviours of customers, the strategies, actions, and strengths of competitors, and the trends, opportunities, and threats of the market, and to use this information to recognize and leverage opportunities for innovation, differentiation, and value creation for customers and stakeholders. Furthermore, this capability allows organisations to use their dynamic capabilities to innovate with their resources and competencies, which are essential for gaining competitive advantage and superior performance in dynamic environments.

To improve the understanding of the GA in dynamic environments we explore a set of test problems, the shaky ladder hyper-defined functions (sl-hdf), and extend these functions to create versions that are equivalent to many classical dynamic problems. We do this by constraining the space of all sl-hdfs to create representations of these classical functions. We have examined three classical problems, and compared sl-hdf versions of these problems with their standard representations. These results show that the sl-hdfs are representative of a larger class of problems, and can represent a larger class of test suite. Previous results on sl-hdf showed that GA performance is best in the Defined Cliffs variant of the sl-hdf. We build upon these results to improve GA performance in several classes of real world dynamic problems by modifying the problem representation. These results lend insight into dynamic problems where the GA will perform well.

One of the major challenges facing Artificial Intelligence in the future is the design of trustworthy algorithms. The development of trustworthy algorithms will be a key challenge in Artificial Intelligence for years to come. Cultural Algorithms (CAs) are viewed as one framework that can be employed to produce a trustable evolutionary algorithm. They contain features to support both sustainable and explainable computation that satisfy requirements for trustworthy algorithms proposed by Cox [Nine experts on the single biggest obstacle facing AI and algorithms in the next five years, Emerging Tech Brew, January 22, 2021]. Here, two different configurations of CAs are described and compared in terms of their ability to support sustainable solutions over the complete range of dynamic environments, from static to linear to nonlinear and finally chaotic. The Wisdom of the Crowds method was selected for the one configuration since it has been observed to work in both simple and complex environments and requires little long-term memory. The Common Value Auction (CVA) configuration was selected to represent those mechanisms that were more data centric and required more long-term memory content.

Both approaches were found to provide sustainable performance across all the dynamic environments tested from static to chaotic. Based upon the information collected in the Belief Space, they produced this behavior in different ways. First, the topologies that they employed differed in terms of the “in degree” for different complexities. The CVA approach tended to favor reduced “indegree/outdegree”, while the WM exhibited a higher indegree/outdegree in the best topology for a given environment. These differences reflected the fact the CVA had more information available for the agents about the network in the Belief Space, whereas the agents in the WM had access to less available knowledge. It therefore needed to spread the knowledge that it currently had more widely throughout the population.

Patrolling is the most commonly requested type of unmanned aerial vehicle (UAV) mission; thus we use it as a prototypical example of autonomous mission planning. In this paper, we design an autonomous mission planner for UAV patrolling missions in unpredictably dynamic environments (UDEs) using recomposable restricted finite state machines (ReRFSM). UDEs admit online changes to the number of waypoints, their priorities, and the availability of paths without any prediction. ReRFSM can model UDEs and restricted inputs. Dynamic programming is used to generate policies and limited breadth-first search (LBFS) is used to generate solutions with a small computation time that plan single UAV tours. Finally, we introduce patrolling policies for multiple UAVs by composing multiple mission RFSMs and modifying the inputs of the ReRFSMs. We demonstrate the case of two vehicles as a proof of concept.