text: Miniaturization and cost, two of the main attractive factors of swarm robotics, have motivated its use as a solution in object collecting tasks, search & rescue missions, and other applications. However, in the current literature only a few papers consider energy allocation efficiency within a swarm. Generally, robots recharge to their maximum level every time unconditionally, and do not incorporate estimates of the energy needed for their next task. In this paper we present an energy efficiency maximization method that minimizes the overall energy cost within a swarm while simultaneously maximizing swarm performance on an object gathering task. The method utilizes dynamic thresholds for upper and lower battery limits. This method has also shown to improve the efficiency of existing energy management methods.
  @cite_0: Prey retrieval, also known as foraging, is a widely used test application in collective robotics. The task consists in searching for objects spread in the environment and in bringing them to a specific place called nest. Scientific issues usually concern efficient exploration, mapping, communication among agents, task coordination and allocation, and conflict resolution. In particular, interferences among robots reduce the efficiency of the group in performing the task. Several works in the literature investigate how the control system of each robot or some form of middle long range communication can reduce the interferences. In this work, we show that a simple adaptation mechanism, inspired by ants’ behaviour and based only on information locally available to each robot, is effective in increasing the group efficiency. The same adaptation mechanism is also responsible for self-organised task allocation in the group.
  @cite_10: This paper presents a simple adaptation mechanism to automatically adjust the ratio of foragers to resters (division of labour) in a swarm of foraging robots and hence maximise the net energy income to the swarm. Three adaptation rules are introduced based on local sensing and communications. Individual robots use internal cues (successful food retrieval), environmental cues (collisions with teammates while searching for food) and social cues (teammate success in food retrieval) to dynamically vary the time spent foraging or resting. The paper investigates the effectiveness of a number of strategies based upon different combinations of cues, and demonstrates successful adaptive emergent division of labour. Strategies which employ the social cues are shown to lead to the fastest adaptation to changes in food density and we see that social cues have most impact when food density is low: robots need to cooperate more when energy is scarce.
  @cite_4: 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.
summary: Labella took inspiration from ants. He created an adaptation method that controls the number of robots foraging in the environment @cite_0 . Each robot has a probability variable that increases and decreases based on the number of successes and failures the robot had when foraging. The probability variable dictates the probability that the specific robot would leave the nest and start foraging. This allows for robots who consistently find and retrieve prey to keep foraging, while also eventually reaching an equilibrium or state in which the necessary number of robots are active. Thus, it efficiently uses energy when needed, similarly to @cite_10 @cite_4 in that they measure the time it takes to finish foraging but not how much energy was already allocated.