Wall-following - Phylogenetic context of an enhanced behaviour in stygomorphic Sinocyclocheilus (Cypriniformes: Cyprinidae) cavefishes.

With 75 known species, the freshwater fish genus Sinocyclocheilus is the largest cavefish radiation in the world and shows multiple adaptations for cave-dwelling (stygomorphic adaptations), which include a range of traits such as eye degeneration (normal-eyed, micro-eyed and eyeless), depigmentation of skin, and in some species, the presence of "horns". Their behavioural adaptations to subterranean environments, however, are poorly understood. Wall-following (WF) behaviour, where an organism remains in close contact with the boundary demarcating its habitat when in the dark, is a peculiar behaviour observed in a wide range of animals and is enhanced in cave dwellers. Hence, we hypothesise that wall-following is also present in Sinocyclocheilus, possibly enhanced in eyeless species compared to eye bearing (normal-/micro-eyed species). Using 13 species representative of Sinocyclocheilus radiation and eye morphs, we designed a series of assays, based on pre-existing methods for Astyanax mexicanus behavioural experiments, to examine wall-following behaviour under three conditions. Our results indicate that eyeless species exhibit significantly enhanced intensities of WF compared to normal-eyed species, with micro-eyed forms demonstrating intermediate intensities in the WF distance. Using a mtDNA based dated phylogeny (chronogram with four clades A-D), we traced the degree of WF of these forms to outline common patterns. We show that the intensity of WF behaviour is higher in the subterranean clades compared to clades dominated by normal-eyed free-living species. We also found that eyeless species are highly sensitive to vibrations, whereas normal-eyed species are the least sensitive. Since WF behaviour is presented to some degree in all Sinocyclocheilus species, and given that these fishes evolved in the late Miocene, we identify this behaviour as being ancestral with WF enhancement related to cave occupation. Results from this diversification-scale study of cavefish behaviour suggest that enhanced wall-following behaviour may be a convergent trait across all stygomorphic lineages.

Flour beetles prefer corners over walls and are slowed down with increasing habitat complexity.

Movement affects all key behaviours in which animals engage, including dispersal and habitat use. The red flour beetle, known as a cosmopolitan pest of stored products, was the subject of our study. We examined whether the beetles preferred corners, walls or open areas, and how turns or obstacles in corridors delayed the beetles' arrival at a target cell. Beetles spent significantly more time in corners than expected by chance, while they spent considerably less time in open areas than expected. However, no significant difference was observed between areas with two or three surrounding walls. This could be attributed to the beetles' stronger attraction to corners than crevices or the insufficient proximity of the third wall to the other two. Movement through the corridor was delayed by turns or obstacles, expressed in arrival probabilities, arrival times, time in the corridor or movement speed. Obstacles on the corridor's perimeter had a stronger effect on the beetle movement than those in the corridor's centre owing to the beetles' tendency to follow walls. The research is important also for applied purposes, such as better understanding beetle movement, how to delay their arrival to new patches, and where to place traps.

Experimental arena settings might lead to misinterpretation of movement properties.

Movement is an important animal behavior contributing to reproduction and survival. Animal movement is often examined in arenas or enclosures under laboratory conditions. We used the red flour beetle (Tribolium castaneum) to examine here the effect of the arena size, shape, number of barriers, access to the arena's center, and illumination on six movement properties. We demonstrate great differences among arenas. For example, the beetles moved over longer distances in clear arenas than in obstructed ones. Movement along the arena's perimeter was greater in smaller arenas than in larger ones. Movement was more directional in round arenas than in rectangular ones. In general, the beetles stopped moving closer to the perimeter and closer to corners (in the square and rectangular arenas) than expected by chance. In some cases, the arena properties interacted with the beetle sex to affect several movement properties. All these suggest that arena properties might also interact with experimental manipulations to affect the outcome of studies and lead to results specific to the arena used. In other words, instead of examining animal movement, we in fact examine the animal interaction with the arena structure. Caution is therefore advised in interpreting the results of studies on movement in arenas under laboratory conditions and we recommend paying attention also to barriers or obstacles in field experiments. For instance, movement along the arena's perimeter is often interpreted as centrophobism or thigmotaxis but the results here show that such movement is arena dependent.

Enzymatic Numerical Spiking Neural Membrane Systems and their Application in Designing Membrane Controllers.

Spiking neural P systems (SN P systems), inspired by biological neurons, are introduced as symbolical neural-like computing models that encode information with multisets of symbolized spikes in neurons and process information by using spike-based rewriting rules. Inspired by neuronal activities affected by enzymes, a numerical variant of SN P systems called enzymatic numerical spiking neural P systems (ENSNP systems) is proposed wherein each neuron has a set of variables with real values and a set of enzymatic activation-production spiking rules, and each synapse has an assigned weight. By using spiking rules, ENSNP systems can directly implement mathematical methods based on real numbers and continuous functions. Furthermore, ENSNP systems are used to model ENSNP membrane controllers (ENSNP-MCs) for robots implementing wall following. The trajectories, distances from the wall, and wheel speeds of robots with ENSNP-MCs for wall following are compared with those of a robot with a membrane controller for wall following. The average error values of the designed ENSNP-MCs are compared with three recently fuzzy logical controllers with optimization algorithms for wall following. The experimental results showed that the designed ENSNP-MCs can be candidates as efficient controllers to control robots implementing the task of wall following.

Design of a Reconfigurable Wall Disinfection Robot.

During a viral outbreak, such as COVID-19, autonomously operated robots are in high demand. Robots effectively improve the environmental concerns of contaminated surfaces in public spaces, such as airports, public transport areas and hospitals, that are considered high-risk areas. Indoor spaces walls made up most of the indoor areas in these public spaces and can be easily contaminated. Wall cleaning and disinfection processes are therefore critical for managing and mitigating the spread of viruses. Consequently, wall cleaning robots are preferred to address the demands. A wall cleaning robot needs to maintain a close and consistent distance away from a given wall during cleaning and disinfection processes. In this paper, a reconfigurable wall cleaning robot with autonomous wall following ability is proposed. The robot platform, Wasp, possess inter-reconfigurability, which enables it to be physically reconfigured into a wall-cleaning robot. The wall following ability has been implemented using a Fuzzy Logic System (FLS). The design of the robot and the FLS are presented in the paper. The platform and the FLS are tested and validated in several test cases. The experimental outcomes validate the real-world applicability of the proposed wall following method for a wall cleaning robot.

Wall-Following Behavior for a Disinfection Robot Using Type 1 and Type 2 Fuzzy Logic Systems.

Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A robot intended for disinfecting walls should be capable of following the wall concerned, while maintaining a given distance, to be effective. The ability to operate in an unknown environment while coping with uncertainties is crucial for a wall disinfection robot intended for deployment in public spaces. Therefore, this paper contributes to the state-of-the-art by proposing a novel method of establishing the wall-following behavior for a wall disinfection robot using fuzzy logic. A non-singleton Type 1 Fuzzy Logic System (T1-FLS) and a non-singleton Interval Type 2 Fuzzy Logic System (IT2-FLS) are developed in this regard. The wall-following behavior of the two fuzzy systems was evaluated through simulations by considering heterogeneous wall arrangements. The simulation results validate the real-world applicability of the proposed FLSs for establishing the wall-following behavior for a wall disinfection robot. Furthermore, the statistical outcomes show that the IT2-FLS has significantly superior performance than the T1-FLS in this application.

Vision Based Wall Following Framework: A Case Study With HSR Robot for Cleaning Application.

Periodic cleaning of all frequently touched social areas such as walls, doors, locks, handles, windows has become the first line of defense against all infectious diseases. Among those, cleaning of large wall areas manually is always tedious, time-consuming, and astounding task. Although numerous cleaning companies are interested in deploying robotic cleaning solutions, they are mostly not addressing wall cleaning. To this end, we are proposing a new vision-based wall following framework that acts as an add-on for any professional robotic platform to perform wall cleaning. The proposed framework uses Deep Learning (DL) framework to visually detect, classify, and segment the wall/floor surface and instructs the robot to wall follow to execute the cleaning task. Also, we summarized the system architecture of Toyota Human Support Robot (HSR), which has been used as our testing platform. We evaluated the performance of the proposed framework on HSR robot under various defined scenarios. Our experimental results indicate that the proposed framework could successfully classify and segment the wall/floor surface and also detect the obstacle on wall and floor with high detection accuracy and demonstrates a robust behavior of wall following.

Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments.

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

Wall following in Xenopus laevis is barrier-driven.

The tendency of animals to follow boundaries within their environment can serve as a strategy for spatial learning or defensive behaviour. We examined whether Xenopus laevis tadpoles and froglets employ such a strategy by characterizing their swimming pattern in a square tank with shallow water. Trajectories obtained from video recordings were analysed for proximity to the nearest wall. With the exception of young larvae, the vast majority of animals (both tadpoles and froglets) spent a disproportionately large amount of time near the wall. The total distance covered was not a confounding factor, but animals were stronger wall followers in smaller tanks. Wall following was also not influenced by whether the surrounding walls of the tank were black or white, illuminated by infrared light, or by the presence or absence of tentacles. When given a choice in a convex tank to swim straight and leave the wall or turn to follow the wall, the animals consistently left the wall, indicating that wall following in X. laevis is barrier-driven. This implies that wall following behaviour in Xenopus derives from constraints imposed by the environment (or the experimenter) and is unlikely a strategy for spatial learning or safety seeking.