Scalability is a significant challenge for robot swarms. Generally, larger groups of cooperating robots produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel costs. This paper demonstrates a scale-invariant swarm foraging algorithm that ensures that each robot finds and delivers targets 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 collected targets and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as tree branches and animal cardiovascular networks that deliver resources to cells and determine the scale and pace of life. 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 to predict the capacity of depot robots that overcome scaling constraints to produce scale-invariant robot swarms. We verify the claims for large swarms in simulation and implement a simple depot design in hardware.

Designing resource-collection algorithms for relatively simple robots that are effective given the noise and uncertainty of the real world is a challenge in swarm robotics. This paper describes the performance of two algorithms for collective robot foraging: the stochastic central-place foraging algorithm (CPFA) and the distributed deterministic spiral algorithm (DDSA). With the CPFA, robots mimic the foraging behaviors of ants; they stochastically search for targets and share information to recruit other robots to locations where they detect multiple targets. With the DDSA, robots travel along pre-planned spiral paths; robots detect the nearest targets first and, in theory, guarantee eventual complete coverage of the arena with minimal overlap. We implemented both algorithms and compared their performance in a Gazebo simulation and in physical robots in a large outdoor arena. In a realistic Gazebo simulation, the DDSA outperforms the CPFA. However, in real-world experiments with obstacles, collisions, and errors, the movement patterns of robots implementing the DDSA become visually indistinguishable from the CPFA. The CPFA is less affected by noise and error, and it performs as well as, or better than, the DDSA. 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.

Pavement surface distress conditions are critical inputs for quantifying roadway infrastructure serviceability. Numerous computer-aided automatic examination techniques have been deployed for pavement distress condition assessments, such as digital image processing methods. However, their effectiveness and applicability are impeded due to information losses in 2-D image combination processes or extremely high costs in 3-D geo-referenced data set. In this paper, a cost-effective Kinect-based approach is proposed for 3-D pavement surface reconstruction and cracking recognition. We propose a comprehensive computational solution for the detection and recognition of pavement distress feature identification. Various cracking measurements such as alligator cracking, traverse cracking, longitudinal cracking, and so on. are identified and recognized for their severity examinations based on associated geometrical features. The experimental results indicate that this method is effective in reducing data collection costs and extracting analytical information on pavement cracking measurements. The research findings confirm that the proposed approach provides a viable, applicable solution to an automatic pavement surface condition detection and evaluation. The proposed methodology is transferable for pavement surface reconstruction and distress condition detection based on the other 3-D cloud point data. It provides an alternative inexpensive complement to existing pavement examination methodologies.

Finding and retrieving resources in unmapped environments is an important and difficult challenge for robot swarms. Central-place foraging algorithms can be tuned to produce efficient collective strategies for different resource distributions. However, efficiency decreases as swarm size scales up: larger swarms produce more inter-robot collisions and increase competition for resources. We propose a novel extension to central-place foraging in which multiple nests are distributed in the environment. In this multiple-place foraging algorithm, robots depart from a home nest but always return to the nest closest to them. We simulate robot swarms that mimic foraging ants using the multiple-place strategy, employing a genetic algorithm to optimize their behavior in the robot simulator ARGoS. Experiments show that multiple nests produce higher foraging rates and lower average travel time compared to central-place foraging for three different resource distributions. Time spent avoiding robot-robot collisions is not always reduced as was expected, primarily because the use of pheromone-like waypoints leads to more collisions when robots forage for clustered resources. These results demonstrate the importance of careful design in order to create efficient multiple collection points to mitigate the central-place bottleneck for foraging robot swarms.