Anders Lyhne Christensen

• PhD
• Professor (full) at University of Southern Denmark

We present the self-organizing nervous system (SoNS), a robot swarm architecture based on self-organized hierarchy. The SoNS approach enables robots to autonomously establish, maintain, and reconfigure dynamic multilevel system architectures. For example, a robot swarm consisting of n independent robots could transform into a single n –robot SoNS a...

Drone swarms offer great potential for wildlife monitoring, but their real-world use is still limited. This paper addresses the challenge of deploying drones to collect high-quality, multi-perspective data over herds of gregarious animals. We formalise this problem using the novel concept of surface of interest, combined with a Lambertian-inspired...

Drones currently serve as a valuable tool for in-situ sampling of volcanic plumes, but they still involve manual piloting. In this paper, we enable autonomous dual plume sampling by using a machine vision model to detect eruptions. When an eruption is detected, a sampling trajectory is automatically generated to intercept the plume twice to collect...

Multi-agent path finding (MAPF) holds significant practical relevance in numerous real-world applications involving fleets of mobile robots. The efficiency of such systems is directly determined by the quality of the paths calculated. Accordingly, extensive effort has been directed toward creating effective algorithms to address the MAPF problem. Y...

and datasets [10.5281/zenodo.5849300] There exist a large number of methods that can be used for anomaly detection/fault detection in collaborative robots. However, studies on these methods tend to only focus on a single or a couple of such methods, which can make it challenging to gauge their relative merits in specific robot scenarios. In this p...

Emergency services organizations are committed to the challenging task of saving people in distress and minimizing harm across a wide range of events, including accidents, natural disasters, and search and rescue. The teams responsible for these operations use advanced equipment to support their missions. Given the risks and the time pressure of th...

A large number of methods for anomaly detection in robotic manipulation have been proposed, but their applicability and performance in real-world scenarios are often not established. In this paper, we therefore perform an experimental comparison of a broad range of practically applicable methods to detect exogenous anomalies in pick-and-place tasks...

and datasets [10.5281/zenodo.5849300] There exist a large number of methods that can be used for anomaly detection/fault detection in collaborative robots. However, studies on these methods tend to only focus on a single or a couple of such methods, which can make it challenging to gauge their relative merits in specific robot scenarios. In this p...

Automated guided vehicles (AGVs) are a key technology to facilitate flexible production systems in the context of Industry 4.0. This paper investigates an optimization model and a solution using a decentralized multi-agent approach for a new capacitated multi-AGV scheduling problem with conflicting products (CMASPCP) to take full advantage of AGVs....

Mobile robots have already made their way into warehouses, and significant effort has consequently been devoted to designing effective algorithms for the related multi-agent path finding (MAPF) problem. However, most of the proposed MAPF algorithms still rely on centralized planning as well as simplistic assumptions, such as that robots have full o...

and datasets [10.5281/zenodo.5849300] There exist a large number of methods that can be used for anomaly detection/fault detection in collaborative robots. However, studies on these methods tend to only focus on a single or a couple of such methods, which can make it challenging to gauge their relative merits in specific robot scenarios. In this p...

and datasets [10.5281/zenodo.5849300] There exist a large number of methods that can be used for anomaly detection/fault detection in collaborative robots. However, studies on these methods tend to only focus on a single or a couple of such methods, which can make it challenging to gauge their relative merits in specific robot scenarios. In this p...

Path planning is a vitally important ability for autonomous mobile robots. Because of the high computational complexity, the optimal solution is generally infeasible since the required computation time increases exponentially with the increase in the problem size. Instead, it is common to rely on heuristic and meta-heuristic algorithms to find near...

Neuromorphic computing currently relies heavily on complicated hardware design to implement asynchronous, parallel and very large-scale brain simulations. This dependency slows down the migration of biological insights into technology. It typically takes several years from idea to finished hardware and once developed the hardware is not broadly ava...

Spiking Neuronal Networks (SNNs) realized inneuromorphic hardware lead to low-power and low-latencyneuronal computing architectures. Neuromorphic computingsystems are most efficient when all of perception, decision mak-ing, and motor control are seamlessly integrated into a singleneuronal architecture that can be realized on the neuromorphichardwar...

Formation control in a robot swarm targets the overall swarm shape and relative positions of individual robots during navigation. Existing approaches often use a global reference or have limited topology flexibility. We propose a novel approach without these constraints, by extending the concept of ‘mergeable nervous systems’ to establish distribut...

In evolutionary robotics role allocation studies, it is common that the role assumed by each robot is strongly associated with specific local conditions, which may compromise scalability and robustness because of the dependency on those conditions. To increase scalability, communication has been proposed as a means for robots to exchange signals th...

In large scale systems of embodied agents, such as robot swarms, the ability to flock is essential in many tasks. However, the conditions necessary to artificially evolve self-organised flocking behaviours remain unknown. In this paper, we study and demonstrate how evolutionary techniques can be used to synthesise flocking behaviours, in particular...

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...

Heterogeneous multirobot systems have shown significant potential in many applications. Cooperative coevolutionary algorithms (CCEAs) represent a promising approach to synthesise controllers for such systems, as they can evolve multiple co-adapted components. Although CCEAs allow for an arbitrary level of team heterogeneity, in previous works heter...

In evolutionary robotics role allocation studies, it is common that the role assumed by each robot is strongly associated with specific local conditions, which may compromise scalability and robustness because of the dependency on those conditions. To increase scalability, communication has been proposed as a means for robots to exchange signals th...

Self-assembling robots have the potential to undergo autonomous morphological adaptation. However, due to the simplicity in their hardware makeup and their limited perspective of the environment, self-assembling robots are often not able to reach their potential and adapt their morphologies to tasks or environments without external cues or prior in...

Recent works in evolutionary robotics have shown the viability of evolution driven by behavioural novelty and diversity. These evolutionary approaches have been successfully used to generate repertoires of diverse and high-quality behaviours, instead of driving evolution towards a single, task-specific solution. Having repertoires of behaviours can...

The evolution of task-oriented control for robots with complex locomotor systems is currently out of reach for traditional evolutionary computation techniques, as the coordination of a high number of locomotion parameters in response to the robot’s sensory inputs is extremely challenging. Evolutionary techniques have therefore mainly been applied t...

We propose Hyb-CCEA, a cooperative coevolutionary algorithm for the evolution of genetically heterogeneous multiagent teams. The proposed approach extends the cooperative coevolution architecture with operators that put the number of coevolving populations under evolutionary control. Populations are dynamically merged based on behavioural similarit...

Robots have the potential to display a higher degree of lifetime morphological adaptation than natural organisms. By adopting a modular approach, robots with different capabilities, shapes, and sizes could, in theory, construct and reconfigure themselves as required. However, current modular robots have only been able to display a limited range of...

The original version of this Article contained an error in the author contributions section, whereby credit for design of the experiments was not attributed to N.M. This error has now been corrected in both the PDF and HTML versions of the Article.

A long-standing goal in artificial intelligence and robotics is synthesising agents that can effectively learn and adapt throughout their lifetime. One open-ended approach to behaviour learning in autonomous robots is online evolution, which is part of the evolutionary robotics field of research. In online evolution approaches, an evolutionary algo...

Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key dist...

Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simula...

Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs allow, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however,...

Robots have the potential to replace manned machines and to carry out tasks in environments that are either remote or hazardous, such as space, deep sea, or underground. However, to create intelligent, reliable, mobile robots, capable of operating effectively in a wide variety of environments, the limited learning ability of robots needs to be addr...

Manual design of self-organized behavioral control for swarms of robots is a complex task. Neuroevolution has proved a viable alternative given its capacity to automatically synthesize controllers. In this paper, we introduce the concept of Genome Variations (GV) in the neuroevolution of behavioral control for robotic swarms. In an evolutionary set...

This paper presents OpenSwarm, a lightweight easy-to-use open-source operating system. To our knowledge, it is the first operating system designed for and deployed on miniature robots. OpenSwarm operates directly on a robot’s microcontroller. It has a memory footprint of 1 kB RAM and 12 kB ROM. OpenSwarm enables a robot to execute multiple processe...

The potential of cooperative coevolutionary algorithms (CCEAs) as a tool for evolving control for heterogeneous multirobot teams has been shown in several previous works. The vast majority of these works have, however, been confined to simulation-based experiments. In this paper, we present one of the first demonstrations of a real multirobot syste...

In this paper, we introduce online hyper-evolution (OHE) to accelerate and increase the performance of online evolution of robotic controllers. Robots executing OHE use the different sources of feedback information traditionally associated with controller evaluation to find effective evolutionary algorithms and controllers online during task execut...

Animals have inspired numerous studies on robot locomotion, but the problem of how autonomous robots can learn to take advantage of multimodal locomotion remains largely unexplored. In this paper, we study how a robot with two different means of locomotion can effective learn when to use each one based only on the limited information it can obtain...

The use of evolutionary robotics in robots with complex means of locomotion has, so far, mainly been limited to gait evolution. Increasing the number of degrees of freedom available to a controller significantly enlarges the search space, which in turn makes the evolution of solutions for a given task more challenging. In this paper, we present Evo...

We provide a summary of our real-world experiments with a swarm of aquatic surface robots with evolved control. Robotic control was synthesized in simulation, using offline evolutionary robotics techniques, and then successfully transferred to a real swarm. Our study presents one of the first demonstrations of evolved control in a swarm robotics sy...

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a n...

Automated environmental monitoring in marine environments is currently carried out either by small-scale robotic systems, composed of one or few robots, or static sensor networks. In this paper, we propose the use of swarm robotics systems to carry out marine environmental monitoring missions. In swarm robotics systems, each individual unit is rela...

Swarm robotics is a promising approach characterized by large numbers of relatively small and inexpensive robots. Since such systems typically rely on decentralized control and local communication, they exhibit a number of interesting and useful properties, namely scalability, robustness to individual faults, and flexibility. In this paper, we deta...

Control design is one of the prominent challenges in the field of swarm robotics. Evolutionary robotics is a promising approach to the synthesis of self-organized behaviors for robotic swarms but it has, so far, only been shown in relatively simple collective behaviors. In this paper, we explore the use of a hybrid control synthesis approach to pro...

Online evolution of controllers on real robots typically requires a prohibitively long time to synthesise effective solutions. In this paper, we introduce two novel approaches to accelerate online evolution in multirobot systems. We introduce a racing technique to cut short the evaluation of poor controllers based on the task performance of past co...

Studies on swarm robotics systems have shown the potential of large-scale multirobot systems based on decentralized control. So far, studies have been mostly conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we present experiments with an autonomous swarm of up...

Adaptive monitoring experiment. Traces of the robots’ positions when performing the experiment where the robustness of the swarm is assessed using the area monitoring task, as described in Section 6.2. (MP4)

Experimental Details. Document detailing the aquatic robotic platform and the experimental parameters of both the simulated and real experiments, in some cases repeating and extending the information given in the main manuscript. (PDF)

Evolutionary robotics is a field of research that employs evolutionary computation to generate robots that adapt to their environment through a process analogous to natural evolution. The generation and optimisation of robots are based on evolutionary principles of blind variations and survival of the fittest, as embodied in the neo-Darwinian synth...

Robots have the potential to replace manned machines and to carry out tasks in environments that are either remote or hazardous, such as space, deep sea, or underground. However, to create intelligent, reliable, mobile robots, capable of operating effectively in a wide variety of environments, the limited learning ability of robots needs to be addr...

Manual design of self-organized behavioral control for swarms of robots is a complex task. Neuroevolution has proved a viable alternative given its capacity to automatically synthesize controllers. In this paper, we introduce the concept of Genome Variations (GV) in the neuroevolution of behavioral control for robotic swarms. In an evolutionary set...

In this chapter, we consider two-dimensional cellular automata as a tool for modelling the behaviour of multirobot systems. We study the dynamics of a group of stationary robots inspired by studies in mixed natural-artificial societies. We model the behaviour of individual robots as a pulse-coupled oscillator, which influences other oscillators thr...

Online evolution of controllers on real robots typically requires a prohibitively long evolution time. One potential solution is to distribute the evolutionary algorithm across a group of robots and evolve controllers in parallel. No systematic study on the scalability properties and dynamics of such algorithms with respect to the group size has, h...

Novelty search is a state-of-the-art evolutionary approach that promotes behavioural novelty instead of pursuing a static objective. Along with a large number of successful applications, many different variants of novelty search have been proposed. It is still unclear, however, how some key parameters and algorithmic components influence the evolut...

We propose an approach to the synthesis of hierarchical control systems comprising both evolved and manually programmed control for autonomous robots. We recursively divide the goal task into sub-tasks until a solution can be evolved or until a solution can easily be programmed by hand. Hierarchical composition of behavior allows us to overcome the...

We define the nervous system of a robot as the processing unit responsible for controlling the robot body, together with the links between the processing unit and the sensorimotor hardware of the robot - i.e., the equivalent of the central nervous system in biological organisms. We present autonomous robots that can merge their nervous systems when...

Spatially targeted communication (STC) allows a message sender to choose message recipients based on their location in space. Currently, STC in multirobot systems is limited to centralized systems. In this paper, we propose a novel communication protocol that enables STC in decentralized multirobot systems. The proposed protocol dispenses with the...

Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot...

Inevolutionaryrobotics,themappingfromrawsensoryinput to neural network input is typically decided by the experimenter or encoded in the genome. Either way, the mapping remains fixed throughout a robot’s lifetime. Inspired by biological sensory organs and the mammalian brain’s capacity for selective attention, we evaluate an alternative approach in...

The availability of relatively capable and inexpensive hardware components has made it feasible to consider large-scale systems of autonomous aquatic drones for maritime tasks. In this paper, we present the CORATAM and HANCAD projects, which focus on the fundamental challenges related to communication and control in swarms of aquatic drones. We arg...

The constituent robots in swarm robotics systems are typically equipped with relatively simple, onboard sensors of limited quality and range. When robots have the capacity to communicate with one another, communication has so far been exclusively used for coordination. In this paper, we present a novel approach in which local, situated communicatio...

Cooperative coevolution algorithms (CCEAs) facilitate the evolution of heterogeneous, cooperating multiagent systems. Such algorithms are, however, subject to inherent scalability issues, since the number of required evaluations increases with the number of agents. A possible solution is to use partially heterogeneous (hybrid) teams: behaviourally...

Evolutionary computation techniques have been widely studied to automate the synthesis of behavioural control for robots. In online evolution, an evolutionary algorithm is executed on the robots themselves during task execution so as to continuously optimise the robot controllers. Online evolution provides numerous potential benefits, including ena...

Neuroevolution, the optimisation of artificial neural networks (ANNs) through evolutionary computation, is a promising approach to the synthesis of controllers for autonomous agents. Traditional neuroevolution approaches employ direct encodings, which sire limited in their ability to evolve complex or large-scale controllers because each ANN parame...

In swarm robotics systems, the constituent robots are typically equipped with simple onboard sensors of limited quality and range. In this paper, we propose to use local communication to enable sharing of sensory information between neighboring robots to overcome the limitations of onboard sensors. Shared information is used to compute readings for...

Abstract Online evolution gives robots the capacity to learn new tasks and to adapt to changing environmental conditions during task execution. Previous approaches to online evolution of neural controllers are typically limited to the optimisation of weights in networks with a pre-specified, fixed topology. In this article, we propose a novel appro...

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviou...