Collective decision-making is a key function of autonomous robot swarms, enabling them to reach a consensus on actions based on environmental features. Existing strategies require the participation of all robots in the decision-making process, which is resource-intensive and prevents the swarm from allocating the robots to any other tasks. We propose Subset-Based Collective Decision-Making (SubCDM), which enables decisions using only a swarm subset. The construction of the subset is dynamic and decentralized, relying solely on local information. Our method allows the swarm to adaptively determine the size of the subset for accurate decision-making, depending on the difficulty of reaching a consensus. Simulation results using one hundred robots show that our approach achieves accuracy comparable to using the entire swarm while reducing the number of robots required to perform collective decision-making, making it a resource-efficient solution for collective decision-making in swarm robotics.

Navigating autonomous robots through dense forests and rugged terrains is especially daunting when exteroceptive sensors—such as cameras and LiDAR sensors— fail under occlusions, low-light conditions, or sensor noise. We present Blind-Wayfarer, a probing-driven navigation framework inspired by maze-solving algorithms that relies primarily on a compass to robustly traverse complex, unstructured environments. In 1,000 simulated forest experiments, Blind-Wayfarer achieved a 99.7% success rate. In real-world tests in two distinct scenarios—with rover platforms of different sizes—our approach successfully escaped forest entrapments in all 20 trials. Remarkably, our framework also enabled a robot to escape a dense woodland, traveling from 45 m inside the forest to a paved pathway at its edge. These findings highlight the potential of probing-based methods for reliable navigation in challenging perception-degraded field conditions. Videos and code are available on our website https://sites.google.com/view/blind-wayfarer

One of the main tasks for autonomous robot swarms is to collectively decide on the best available option. Achieving that requires a high quality communication between the agents that may not always be available in a real world environment. In this paper we introduce the communication-constrained collective decision-making problem where some areas of the environment limit the agents' ability to communicate, either by reducing success rate or blocking the communication channels. We propose a decentralised algorithm for mapping environmental features for robot swarms as well as improving collective decision making in communication-limited environments without prior knowledge of the communication landscape. Our results show that making a collective aware of the communication environment can improve the speed of convergence in the presence of communication limitations, at least 3 times faster, without sacrificing accuracy.

Quality-diversity (QD) algorithms evolve behaviorally diverse and high-performing solutions. To illuminate the elite solutions for a space of behaviors, QD algorithms require the definition of a suitable behavior space. If the behavior space is high-dimensional, a suitable dimensionality reduction technique is required to maintain a limited number of behavioral niches. While current methodologies for automated behavior spaces focus on changing the geometry of the behavior space or on unsupervised learning of its key features, there remains a need for customizing behavioral diversity to a particular meta-objective specified by the end user. In the newly emerging framework of QD Meta-Evolution, or QD-Meta for short, one evolves a population of QD algorithms, each with different algorithmic and representational characteristics, to optimize the algorithms and their resulting archives to a user-defined meta-objective. Despite promising results compared to traditional QD algorithms, QD-Meta has yet to be compared to state-of-the-art behavior space automation methods, such as centroidal voronoi tessellations multidimensional archive of phenotypic elites (CVT-MAP-Elites) and autonomous robots realizing their abilities (AURORAs). This article performs an empirical study of QD-Meta on function optimization and multilegged robot locomotion benchmarks. Results demonstrate that QD-Meta archives provide improved average performance and faster adaptation to a priori unknown changes to the environment when compared to CVT-MAP-Elites and AURORA. A qualitative analysis shows how the resulting archives are tailored to the meta-objectives provided by the end user.

Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. We propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. A simulation study investigates adaptation of a Thymio robot swarm in a collective foraging task. First, we investigate different groups of sensory-motor disturbances, including fault to proximity sensors, ground sensors, or actuators of individual robots, with 100 unique combinations for each type. Second, we investigate changes to the surrounding environment of the swarm, where the number of available resources drops or where one robot disrupts the rest of the swarm; for each such change, we include 30 unique conditions. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within less than 30 evaluations.

The energy of ocean waves is the key distinguishing factor of marine environments compared to other aquatic environments such as lakes and rivers. Waves significantly affect the dynamics of marine vehicles; hence it is imperative to consider the dynamics of vehicles in waves when developing efficient control strategies for autonomous surface vehicles (ASVs). However, most marine simulators available open-source either exclude dynamics of vehicles in waves or use methods with high computational overhead. This paper presents ASVLite, a computationally efficient ASV simulator that uses frequency domain analysis for wave force computation. ASVLite is suitable for applications requiring low computational overhead and high run-time performance. Our tests on a Raspberry Pi 2 and a mid-range desktop computer show that the simulator has a high run-time performance to efficiently simulate irregular waves with a component wave count of up to 260 and large-scale swarms of up to 500 ASVs.

In swarm robotics, any of the robots in a swarm may be affected by different faults, resulting in significant performance declines. To allow fault recovery from randomly injected faults to different robots in a swarm, a model-free approach may be preferable due to the accumulation of faults in models and the difficulty to predict the behaviour of neighbouring robots. One model-free approach to fault recovery involves two phases: during simulation, a quality-diversity algorithm evolves a behaviourally diverse archive of controllers; during the target application, a search for the best controller is initiated after fault injection. In quality-diversity algorithms, the choice of the behavioural descriptor is a key design choice that determines the quality of the evolved archives, and therefore the fault recovery performance. Although the environment is an important determinant of behaviour, the impact of environmental diversity is often ignored in the choice of a suitable behavioural descriptor. This study compares different behavioural descriptors, including two generic descriptors that work on a wide range of tasks, one hand-coded descriptor which fits the domain of interest, and one novel type of descriptor based on environmental diversity, which we call Quality-Environment-Diversity (QED). Results demonstrate that the above-mentioned model-free approach to fault recovery is feasible in the context of swarm robotics, reducing the fault impact by a factor 2-3. Further, the environmental diversity obtained with QED yields a unique behavioural diversity profile that allows it to recover from high-impact faults.

The MAP-Elites quality-diversity algorithm has been successful in robotics because it can create a behaviorally diverse set of solutions that later can be used for adaptation, for instance to unanticipated damages. In MAP-Elites, the choice of the behaviour space is essential for adaptation, the recovery of performance in unseen environments, since it defines the diversity of the solutions. Current practice is to hand-code a set of behavioural features, however, given the large space of possible behaviour-performance maps, the designer does not know a priori which behavioural features maximise a map's adaptation potential. We introduce a new meta-evolution algorithm that discovers those behavioural features that maximise future adaptations. The proposed method applies Covariance Matrix Adaptation Evolution Strategy to evolve a population of behaviour-performance maps to maximise a meta-fitness function that rewards adaptation. The method stores solutions found by MAP-Elites in a database which allows to rapidly construct new behaviour-performance maps on-the-fly. To evaluate this system, we study the gait of the RHex robot as it adapts to a range of damages sustained on its legs. When compared to MAP-Elites with user-defined behaviour spaces, we demonstrate that the meta-evolution system learns high-performing gaits with or without damages injected to the robot.

Rapid progress in embedded computing hardware increasingly enables on-board image processing on small robots. This development opens the path to replacing costly sensors with sophisticated computer vision techniques. A case in point is the prediction of scene depth information from a monocular camera for autonomous navigation. Motivated by the aim to develop a robot swarm suitable for sensing, monitoring, and search applications in forests, we have collected a set of RGB images and corresponding depth maps. Over 100000 RGB/depth image pairs were recorded with a custom rig from the perspective of a small ground rover moving through a forest. Taken under different weather and lighting conditions, the images include scenes with grass, bushes, standing and fallen trees, tree branches, leaves, and dirt. In addition GPS, IMU, and wheel encoder data were recorded. From the calibrated, synchronized, aligned and timestamped frames about 9700 image-depth map pairs were selected for sharpness and variety. We provide this dataset to the community to fill a need identified in our own research and hope it will accelerate progress in robots navigating the challenging forest environment. This paper describes our custom hardware and methodology to collect the data, subsequent processing and quality of the data, and how to access it.

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

The ability to reliably detect faults is essential in many real-world tasks that robot swarms have the potential to perform. Most studies on fault detection in swarm robotics have been conducted exclusively in simulation, and they have focused on a single type of fault or a specific task. In a series of previous studies, we have developed a robust fault-detection approach in which robots in a swarm learn to distinguish between normal and faulty behaviors online. In this paper, we assess the performance of our fault-detection approach on a swarm of seven physical mobile robots. We experiment with three classic swarm robotics tasks and consider several types of faults in both sensors and actuators. Experimental results show that the robots are able to reliably detect the presence of hardware faults in one another even when the swarm behavior is changed during operation. This paper is thus an important step toward making robot swarms sufficiently reliable and dependable for real-world applications.

The recently introduced Intelligent Trial and Error algorithm (IT&E) both improves the ability to automatically generate controllers that transfer to real robots, and enables robots to creatively adapt to damage in less than 2 minutes. A key component of IT&E is a new evolutionary algorithm called MAP-Elites, which creates a behavior-performance map that is provided as a set of "creative" ideas to an online learning algorithm. To date, all experiments with MAP-Elites have been performed with a directly encoded list of parameters: it is therefore unknown how MAP-Elites would behave with more advanced encodings, like HyperNeat and SUPG. In addition, because we ultimately want robots that respond to their environments via sensors, we investigate the ability of MAP-Elites to evolve closed-loop controllers, which are more complicated, but also more powerful. Our results show that the encoding critically impacts the quality of the results of MAP-Elites, and that the differences are likely linked to the locality of the encoding (the likelihood of generating a similar behavior after a single mutation). Overall, these results improve our understanding of both the dynamics of the MAP-Elites algorithm and how to best harness MAP-Elites to evolve effective and adaptable robotic controllers.

The engineering of fault-detection systems for multirobot systems (MRS) is a well-studied problem (e.g, Christensen et al. (2009)). Most fault-detection models are built on the assumption that normal behavior is known, and can be characterized in advance. The models are trained to recognize predefined normal behaviors, and behaviors not recognized are labeled abnormal. While such an approach does provide some interesting results of robust fault detection and fault tolerance, they may not be applicable when normal behavior can change as a result of unforeseen environmental conditions and online learning for instance. Furthermore, prior information required to characterize normal behaviors, may not always be available.

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Top-down causation has been suggested to occur at all scales of biological organization as a mechanism for explaining the hierarchy of structure and causation in living systems. Here we propose that a transition from bottom-up to top-down causation -- mediated by a reversal in the flow of information from lower to higher levels of organization, to that from higher to lower levels of organization -- is a driving force for most major evolutionary transitions. We suggest that many major evolutionary transitions might therefore be marked by a transition in causal structure. We use logistic growth as a toy model for demonstrating how such a transition can drive the emergence of collective behavior in replicative systems. We then outline how this scenario may have played out in those major evolutionary transitions in which new, higher levels of organization emerged, and propose possible methods via which our hypothesis might be tested.