Giordano B. S. Ferreira

An Improved Cellular Automata-Based Model for Robot Path-Planning

Cellular automata (CA) are able to represent high complex phenomena and can be naturally simulated by digital processors due to its intrinsic discrete nature. CA have been recently considered for path planning in autonomous robotics. In this work we started by adapting a model proposed by Ioannidis et al. to deal with scenarios with a single robot, turning it in a more decentralized approach. However, by simulating this model we noticed a problem that prevents the robot to continue on its path and avoid obstacles. A new version of the model was then proposed to solve it. This new model uses CA transition rules with Moore neighborhood and four possible states per cell. Simulations and experiments involving real e-puck robots were performed to evaluate the model. The results show a real improvement in the robot performance.

Secret key specification for a variable-length cryptographic cellular automata model

Reverse algorithm was previously evaluated as encryption method concluding that its simple adoption is unviable, since it does not assurance the pre-image existence. Variable-Length Encryption Method (VLE) was proposed where a alternative algorithm with extra bits is adopted when pre-image computation is not possible. If an adequate secret key is used with VLE it is expected that the final ciphertext length is close to plaintext size. Several CA static parameters were calculated for a set formed by all radius 2 right-toggle rules. A database was generated associating rules performance in VLE ciphering with its parameters. A genetic algorithm-based data mining was performed to discover an adequate key specification based on CA parameters. Using such specification, ciphertext length is short, encryption process returns high entropy and VLE has a good protection against differential cryptanalysis.

Exhaustive evaluation of radius 2 toggle rules for a variable-length cryptographic cellular automata-based model

A cellular automata (CA) model in cryptography is investigated. A previous work analyzed the usage of reverse algorithm for pre-image computation as an encryption method. The main conclusion was that the simple adoption of such method is not viable, since it does not have 100% of guarantee of pre-image existence. A new approach was proposed that uses extra bits when the pre-image computation is not possible. It is expected that in practice few failures happens and the ciphertext size will be close to the plaintext. Encryption always succeeds and the final length of the ciphertext is not fixed. We better investigate the secret key specification by using a more representative set formed by all radius 2 right-toggle rules, totalizing 65536 rules. An exhaustive analysis of this rule space has shown that using adequate specification the method has a good protection against differential cryptanalysis and a small increase in ciphertext length.

Investigating a cellular automata model that performs three distance diffusion on a robot path planning

An improved cellular automata (CA) model is proposed and evaluated on a path planning problem for an autonomous robot. The objective is to construct a collision-free path from the robot’s initial position to the goal by applying the improved CA model and environment pre-processed images captured during its navigation. CA rules are used to enlarge obstacles and to perform three distance diffusion. Goal distance is spread by a CA rule using the free cells. Distance diffusion spread two new metrics used for cells inside the enlarged obstacle regions. The new distances are then used to plan routes when the robot needs to pass inside enlarged obstacle regions. The algorithm performs a new path planning at each n steps of robot navigation using its current position. Our inspiration came from the possibility to mimic the cognitive behaviour of desert ants, which re-plan their path to the goal constantly in time intervals, using only local cues. An e-puck robot was used in simulated scenarios to evaluate the new CA based adaptive path planning model. The simulations show promising results on the single robot’s performance confirming that the model could also be adapted for robot swarms.

Investigating the effects of noise on a cell-to-cell communication mechanism for structure regeneration.

In this paper we modified a previous cell-cell communication mechanism of dynamic structure discovery and regeneration to account for the presence of noise that could alter the route of messages transmitted across cells. We report results from a large number of simulation runs where noise was applied to the distance and direction of messages dispatched by cells. Based on our analysis of the results, we proposed an “activation” mechanism where missing cells need to receive a certain number of messages first before they divide and recreate missing cells. We then show that, due to the inherent message redundancy in the organism this mechanism improved the performance of the model even when noise is present on packets.

Mate choice strategies in a spatially-explicit model environment

Decisions about the choice of a mate can greatly impact both individual fitness and selection processes. We developed a novel agent-based model to investigate two common mate choice rules that may be used by female gray treefrogs (Hyla versicolor). In this model environment, female agents using the minimum-threshold strategy found higher quality mates and traveled shorter distances on average, compared with female agents using the best-of-n strategy. Females using the minimum-threshold strategy, however, incur significant lost opportunity costs, depending on the male population quality average. The best-of-n strategy leads to significant female:female competition that limits their ability to find high quality mates. Thus, when the sex ratio is 0.8, best-of-5 and best-of-2 strategies yield mates of nearly identical quality. Although the distance traveled by females in the mating task varied depending on male spatial distribution in the environment, this did not interact with female choice for the best-of-n or minimum-threshold strategies. By incorporating empirical data from the frogs in this temporally- and spatially-explicit model, we thus show the emergence of novel interactions of common decision-making rules with realistic environmental variables.

Accidental encounters: can accidents be adaptive?

Accidents happen in nature, from simple incidents like bumping into obstacles, to erroneously arriving at the wrong location, to mating with an unintended partner. Whether accidents are problematic for an animal depends on their context, frequency, and severity. In this article, we investigate the question of how accidents affect the task performance of agents in an agent-based simulation model for a wide class of tasks called “multi-agent territory exploration” tasks (MATE). In MATE tasks, agents have to visit particular locations of varying quality in partially observable environments within a fixed time window. As such, agents have to balance the quality of the location with how much energy they are willing to expend reaching it. Arriving at the wrong location by accident typically reduces task performance. We model agents based on two location selection strategies that are hypothesized to be widely used in nature: best-of-n and min-threshold. Our results show that the two strategies lead to different accident rates and thus overall different levels of performance based on the degree of competition among agents, as well as the quality, density, visibility, and distribution of target locations in the environment. We also show that in some cases, individual accidents can be advantageous for both the individual and the whole group.

Better than average: Analyzing distributions to understand robot behavior in a multi-agent area coverage problem.

Building robots, even for performing simple tasks, requires the designer to assess performance using various parameters. However, sometimes the best solution is not the one that performs best on average. Hence, other ways of evaluating performance are necessary. We ran a broad parameter sweep for an agent-based simulation of a robotic area coverage task with very simple agent controllers in four different task environments. Analysis of the results emphasizes the importance of considering the entire distribution across randomized starting conditions, and not just the mean overall performance, when assessing the effectiveness of parameter settings. Our findings indicate the potential for robotic system designers to constrain or specify the qualities of system performance distributions.

Following Strategies Reduces Accidents, but Makes Outcomes Worse: Evidence from Simulated Treefrog Mating Scenarios.

Accidental matings happen in real environments where females end up with males they did not choose. In this paper, we investigate the frequency and changes in mated male fitness in accidental matings specifically in the context of the female choice of the gray treefrogs hyla versicolor based on the best-of-n and minthresh strategy, which are both hypothesized to be widely used in nature. Theoretical considerations as well as results from agent-based model simulations show how and why accidents occur and how the two strategies lead to different accident rates and reduced fitness values of the mated males.