An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms

Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area and incomplete data about the environment. This search task is an abstraction of many real-world applications such as search and rescue, hazardous material clean-up, and space exploration. The collective foraging behavior of robot swarms is an effective approach for this task. In our work, individual robots have limited sensing and communication range (like ants), but they are organized and work together to complete foraging tasks collectively. An efficient foraging algorithm coordinates robots to search and collect as many resources as possible in the least amount of time. In the foraging algorithms we study, robots act independently with little or no central control. As the swarm size and arena size increase (e.g., thousands of robots searching over the surface of Mars or ocean), the foraging performance per robot decreases. Generally, larger robot swarms produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel distances causing the phenomenon of diminishing returns. The foraging performance per robot (measured as a number of collected resources per unit time) is sublinear with the arena size and the swarm size. Our goal is to design a scale-invariant foraging robot swarm. In other words, the foraging performance per robot should be nearly constant as the arena size and the swarm size increase. We address these problems with the Multiple-Place Foraging Algorithm (MPFA), which uses multiple collection zones distributed throughout the search area. Robots start from randomly assigned home collection zones but always return to the closest collection zones with found resources. We simulate the foraging behavior of robot swarms in the robot simulator ARGoS and employ a Genetic Algorithm (GA) to discover different optimized foraging strategies as swarm sizes and the number of resources is scaled up. In our experiments, the MPFA always produces higher foraging rates, fewer collisions, and lower travel and search time than the Central-Place Foraging Algorithm (CPFA). To make the MPFA more adaptable, we introduce dynamic depots that move to the centroid of recently collected resources, minimizing transport times when resources are clustered in heterogeneous distributions. Finally, we extend the MPFA with a bio-inspired hierarchical branching transportation network. We demonstrate a scale-invariant swarm foraging algorithm that ensures that each robot finds and delivers resources to a central collection zone at the same rate, regardless of the size of the swarm or the search area. Dispersed mobile depots aggregate locally foraged resources and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as animal cardiovascular networks that deliver resources to cells and determine the scale and pace of life. The transportation of resources through the cardiovascular system from the heart to dispersed cells is the inverse problem of transportation of dispersed resources to a central collection zone through the hierarchical branching transportation network in robot swarms. We demonstrate that biological scaling laws predict how quickly robots forage in simulations of up to thousands of robots searching over thousands of square meters. We then use biological scaling predictions to determine the capacity of depot robots in order to overcome scaling constraints and produce scale-invariant robot swarms. We verify the predictions using ARGoS simulations. While simulations are useful for initial evaluations of the viability of algorithms, our ultimate goal is predicting how algorithms will perform when physical robots interact in the unpredictable conditions of environments they are placed in. The CPFA and the Distributed Deterministic Spiral Algorithm (DDSA) are compared in physical robots in a large outdoor arena. The physical experiments change our conclusion about which algorithm has the best performance, emphasizing the importance of systematically comparing the performance of swarm robotic algorithms in the real world. We illustrate the feasibility of implementing the MPFA with transportation networks in physical robot swarms. Full implementation of the MPFA in an outdoor environment is the next step to demonstrate truly scalable and robust foraging robot swarms

Stigmergy-based, Dual-Layer Coverage of Unknown Indoor Regions

We present algorithms for uniformly covering an unknown indoor region with a swarm of simple, anonymous and autonomous mobile agents. The exploration of such regions is made difficult by the lack of a common global reference frame, severe degradation of radio-frequency communication, and numerous ground obstacles. We propose addressing these challenges by using airborne agents, such as Micro Air Vehicles, in dual capacity, both as mobile explorers and (once they land) as beacons that help other agents navigate the region. The algorithms we propose are designed for a swarm of simple, identical, ant-like agents with local sensing capabilities. The agents enter the region, which is discretized as a graph, over time from one or more entry points and are tasked with occupying all of its vertices. Unlike many works in this area, we consider the requirement of informing an outside operator with limited information that the coverage mission is complete. Even with this additional requirement we show, both through simulations and mathematical proofs, that the dual role concept results in linear-time termination, while also besting many well-known algorithms in the literature in terms of energy use

Robotic equipment carrying RN detectors: requirements and capabilities for testing

77 pags., 32 figs., 5 tabs.-- ERNCIP Radiological and Nuclear Threats to Critical Infrastructure Thematic Group . -- This publication is a Technical report by the Joint Research Centre (JRC) . -- JRC128728 . -- EUR 31044 ENThe research leading to these results has received funding from the European Union as part of the European Reference Network for Critical Infrastructure Protection (ERNCIP) projec

Design, Developement, Analysis and Control of a Bio-Inspired Robotic Samara Rotorcraft

THIS body of work details the development of the first at-scale (>15 cm) robotic samara, or winged seed. The design of prototypes inspired by autorotating plant seed geometries is presented along with a detailed experimental process that elucidates similarities between mechanical and robotic samara flight dynamics. The iterative development process and the implementation of working prototypes are discussed for robotic samara Micro-Air-Vehicles (MAV) that range in size from 7.5 cm to 27 cm. Vehicle design issues are explored as they relate to autorotation efficiency, stability, flight dynamics and control of single winged rotorcraft. In recent years a new paradigm of highly maneuverable aircraft has emerged that are ideally suited for operation in a confined environment. Different from conven- tional aircraft, viscous forces play a large role in the physics of flight at this scale. This results in relatively poor aerodynamic performance of conventional airfoil and rotorcraft configurations. This deficiency has led to the consideration of naturally occurring geometries and configurations, the simplest of which is the samara. To study the influence of geometric variation on autorotation efficiency, a high speed camera system was used to track the flight path and orientation of the mechan- ical samaras. The wing geometry is planar symmetric and resembles a scaled version of Acer diabolicum Blume. The airfoil resembles a scaled version of the maple seed with a blunt leading edge followed by a thin section without camber. Four mechan- ical samara geometries with equal wing loading were designed and fabricated using a high precision rapid prototyping machine that ensured similarity between models. It was found that in order to reduce the descent velocity of an autorotating samara the area centroid or maximum chords should be as far from the center of rotation as possible. Flight data revealed large oscillations in feathering and coning angles, and the resultant flight path was found to be dependent on the mean feathering angle. The different flight modalities provided the basis for the design of a control sys- tem for a powered robotic samara that does not require high frequency sensing and actuation typical of micro-scaled rotorcraft. A prototype mechanical samara with a variable wing pitch (feathering) angle was constructed and it was found that active control of the feathering angle allowed the variation of the radius of the helix carved by the samara upon descent. This knowledge was used to design a hovering robotic samara capable of lateral motion through a series of different size circles specified by precise actuation of the feathering angle. To mathematically characterize the flight dynamics of the aircraft, System identi- fication techniques were used. Using flight data, a linear model describing the heave dynamics of two robotic samara vehicles was verified. A visual positioning system was used to collect flight data while the vehicles were piloted in an indoor laboratory. Closed-loop implementation of the derived PID controller was demonstrated using the visual tracking system for position and velocity feedback. An approach to directional control that does not require the once-per-revolution actuation or high-frequency measurement of vehicle orientation has been demon- strated for the first time. Lateral flight is attained through the vehicles differing responses to impulsive and step inputs that are leveraged to create a control strategy that provides full controllability. Flight testing revealed several linear relationships, including turn rate, turn radius and forward speed. The steady turn discussed here has been observed in scaled versions of the robotic samara, therefore the open-loop control demonstrated and analyzed is considered to be appropriate for similar vehicles of reduced size with limited sensing and actuation capabilities