A Novel Concept for the Study of Heterogeneous Robotic Swarms warm robotics systems are characterized by decentralized control, limited communication between robots, use of local information, and emergence of global behavior. Such systems have shown their potential for flexibility and robustness [1]-[3]. However, existing swarm robotics systems are by and large still limited to displaying simple proof-of-concept behaviors under laboratory conditions. It is our contention that one of the factors holding back swarm robotics research is the almost universal insistence on homogeneous system components. We believe that swarm robotics designers must embrace heterogeneity if they ever want swarm robotics systems to approach the complexity required of real-world systems. To date, swarm robotics systems have almost exclusively comprised physically and behaviorally undifferentiated agents. This design decision has its roots in ethological models of self-organizing natural systems. These models serve as inspiration for swarm robotics system designers, but are often highly abstract simplifications of natural systems and, to date, have largely assumed homogeneous agents. Selected dynamics of the systems under study are shown to emerge from the interactions of identical system components, ignoring the heterogeneities (physical, spatial, functional, and informational) that one can find in almost any natural system. The field of swarm robotics currently lacks methods and tools with which to study and leverage the heterogeneity that is present in natural systems. To remedy this deficiency, we propose swarmanoid, an innovative swarm robotics system composed of three different robot types with complementary skills: foot-bots are small autonomous robots specialized in moving on both even and uneven terrains, capable of self-assembling and of transporting objects or other robots; hand-bots are autonomous robots capable of climbing some vertical surfaces and manipulating small objects; and eye-bots are autonomous flying robots that can attach to an indoor ceiling, capable of analyzing the environment from a privileged position to S

Swarms of flying robots are a promising alternative to ground-based robots for search in indoor environments with advantages such as increased speed and the ability to fly above obstacles. However, there are numerous problems that must be surmounted including limitations in available sensory and on-board processing capabilities, and low flight endurance. This paper introduces a novel strategy to coordinate a swarm of flying robots for indoor exploration that significantly increases energy efficiency. The presented algorithm is fully distributed and scalable. It relies solely on local sensing and low-bandwidth communication, and does not require absolute positioning, localisation, or explicit world-models. It assumes that flying robots can temporarily attach to the ceiling, or land on the ground for efficient surveillance over extended periods of time. To further reduce energy consumption, the swarm is incrementally deployed by launching one robot at a time. Extensive simulation experiments demonstrate that increasing the time between consecutive robot launches significantly lowers energy consumption by reducing total swarm flight time, while also decreasing collision probability. As a trade-off, however, the search time increases with increased inter-launch periods. These effects are stronger in more complex environments. The proposed localisation-free strategy provides an energy efficient search behaviour adaptable to different environments or timing constraints.

Abstract-In the growing field of collective robotics, spatial co-ordination between robots is often critical and usually achieved via local relative positioning sensors. We believe that range and bearing sensing, based on infrared technology, has the potential to fulfil the strict requirements of real-world collective robots. These requirements include: small size, light weight, large range, high refresh rate, immunity against tilting and misalignment, immunity against ambient light changes, and good range and bearing accuracy. Currently, there are no range and bearing systems that have been designed to cope with such strict requirements. This paper presents a custom range and bearing system, based on a novel cascaded filtering technology, complemented by hybrid infrared/Radio Frequency (RF) communication, which has been designed specifically to meet all these expectations. The system has been characterised and tested, proving its viability.

Swarms of indoor flying robots are promising for many applications, including searching tasks in collapsing buildings, or mobile surveillance and monitoring tasks in complex man-made structures. For tasks that employ several flying robots, spatial-coordination between robots is essential for achieving collective operation. However, there is a lack of on-board sensors capable of sensing the highlydynamic 3-D trajectories required for spatial-coordination of small indoor flying robots. Existing sensing methods typically utilise complex SLAM based approaches, or absolute positioning obtained from off-board tracking sensors, which is not practical for real-world operation. This paper presents an adaptable, embedded infrared based 3-D relative positioning sensor that also operates as a proximity sensor, which is designed to enable inter-robot spatial-coordination and goal-directed flight. This practical approach is robust to varying indoor environmental illumination conditions and is computationally simple.Keywords Relative positioning sensing · Indoor flying robots · Collective operation · 3D sensor · Spatial-coordination · Proximity sensing J.F.R. developed the concept of relative positioning sensing for enabling goal-directed flight on indoor collective flying robots, wrote the manuscript, developed the sensor hardware/firmware, developed the calibration tools and characterised the sensor. T.S. extensively contributed to the sensor firmware and characterisation. J.-C.Z. and D.F. conceived and directed the project sponsoring the work described in the article. They also provided continue support and feedback towards reaching the attained results.

Abstract-Swarms of flying robots are promising in many applications due to rapid terrain coverage. However, there are numerous challenges in realising autonomous operation in unknown indoor environments. A new autonomous flight methodology is presented using relative positioning sensors in reference to nearby static robots. The entirely decentralised approach relies solely on local sensing without requiring absolute positioning, environment maps, powerful computation or longrange communication. The swarm deploys as a robotic network facilitating navigation and goal directed flight. Initial validation tests with quadrotors demonstrated autonomous flight within a confined indoor environment, indicating that they could traverse a large network of static robots across expansive environments.

Abstract-We present ARGoS, a novel open source multirobot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations to their extensibility and to the accuracy of the robot models. In contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another in a transparent way. This novel feature enables a whole new set of optimizations to improve scalability and paves the way for a new approach to parallelism in robotic simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots in 60% of real-time.

A major challenge in swarm robotics is efficiently deploying robots into unknown environments, minimising energy and time costs. This is especially important with small aerial robots which have extremely limited flight autonomy. This paper compares three deployment strategies characterised by nominal computation, memory, communication and sensing requirements, and hence are suitable for flying robots. Energy consumption is decreased by reducing unnecessary flight following two premises: 1) exploiting environmental information gathered by the robots; 2) avoiding diminishing returns and reducing interference between robots. Using a 3-D dynamics simulator we examine energy and time metrics, and also scalability effects. Results indicate that a novel strategy that controls the density of flying robots is most promising in reducing swarm energy costs while maintaining rapid search times. Furthermore, we highlight the energy-time tradeoff and the importance of measuring both metrics, and also the significance of electronics power in calculating total energy consumption, even if it is small relative to locomotion power.

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