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.
Liu focused on energy efficiency by changing the foraging time for each robot @cite_10 . The time spent foraging for resources depended on various cues such as personal successful food retrievals, collisions with teammates for food, and success among other robots in food retrieval. So the method was able to find an adaptable and optimal time for foraging, but did not optimize the amount of energy allocated for it.
@cite_10
1909685943 2014403316 1526647122 1578969637 2774807875 1495441213 1601776805 2104064843 2736923367 2807731679 2333574198 2414980425 2132145840 2089077607 2168225348 2134813883 2895626374 2323122434 2027622574 2053831174
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. This article presents a simple adaptation mechanism to automatically adjust the ratio of foragers to resters (division of labor) in a swarm of foraging robots and hence maximize 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 team-mates while searching for food) and social cues (team-mate success in food retrieval) to dynamically vary the time spent foraging or resting. Simulation results show that the swarm demonstrates successful adaptive emergent division of labor and robustness to environmental change (in food source density), and we observe that robots need to cooperate more when food is scarce. Furthermore, the adaptation mechanism is able to guide the swarm towards energy optimization despite the limited sensing and communication abilities of the individual robots and the simple social interaction rules. The swarm also exhibits the capacity to collectively perceive environmental changes; a capacity that can only be observed at a group level and cannot be deduced from individual robots. 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. In multi-robot applications, such as foraging or collection tasks, interference, which results from competition for space between spatially extended robots, can significantly affect the performance of the group. We present a mathematical model of foraging in a homogeneous multi-robot system, with the goal of understanding quantitatively the effects of interference. We examine two foraging scenarios: a simplified collection task where the robots only collect objects, and a foraging task, where they find objects and deliver them to some pre-specified “home” location. In the first case we find that the overall group performance improves as the system size growss however, interference causes this improvement to be sublinear, and as a result, each robot's individual performance decreases as the group size increases. We also examine the full foraging task where robots collect objects and deliver them home. We find an optimal group size that maximizes group performance. For larger group sizes, the group performance declines. However, again due to the effects of interference, the individual robot's performance is a monotonically decreasing function of the group size. We validate both models by comparing their predictions to results of sensor-based simulations in a multi-robot system and find good agreement between theory and simulations data. Consider a setting where robots must visit sites represented as nodes in a graph, but each robot may fail when traversing an edge. The goal is to find a set of paths for a team of robots which maximizes the expected number of nodes collectively visited, while guaranteeing that the paths satisfy a notion of “independence” formalized by a matroid (e.g. limits on team size, number of visits to regions), and that the probabilities that each robot survives to its destination are above a given threshold. We call this problem the Matroid Team Surviving Orienteers (MTSO) problem, which has broad applications such as environmental monitoring in risky regions and search and rescue in dangerous conditions. We present the MTSO formally and detail numerous examples of matroids in a path planning context. We then propose an approximate greedy algorithm for selecting a feasible set of paths and prove that the value of the output is within a factor p s p s + λ of the optimum, where p s is the per-robot survival probability threshold and 1 λ ≤ 1 is the approximation factor of an oracle routine for the well known orienteering problem. We demonstrate the efficiency of our approach by applying it to a scenario where a team of robots must gather information while avoiding pirates in the Coral Triangle. Minimum time and minimum energy point-to-point trajectories for an industrial robot of the type Manutec r3 are computed subject to state constraints on the angular velocities. The numerical solutions of these optimal control problems are obtained in an efficient way by a combination of a direct collocation and an indirect multiple shooting method. This combination links the benefits of both approaches: A large domain of convergence and a highly accurate solution. The numerical results show that the constraints on the angular velocities become active during large parts of the time optimal motion. But the resulting stress on the links can be significantly reduced by a minimum energy trajectory that is only about ten percent slower than the minimum time trajectory. As a by-product, the reliability of the direct collocation method in estimating adjoint variables and the efficiency of the combination of direct collocation and multiple shooting is demonstrated. The highly accurate solutions reported in this paper may also serve as benchmark problems for other methods. We propose a multi-robot exploration algorithm that uses adaptive coordination to provide heterogeneous behavior. The key idea is to maximize the efficiency of exploring and mapping an unknown environment when a team is faced with unreliable communication and limited battery life (e.g., with aerial rotorcraft). The proposed algorithm utilizes four states: explore, meet, sacrifice, and relay. The explore state uses a frontier-based exploration algorithm, the meet state returns to the last known location of communication to share data, the sacrifice state sends the robot out to explore without consideration of remaining battery, and the relay state lands the robot until a meeting occurs. This approach allows robots to take on the role of a relay to improve communication between team members. In addition, the robots can “sacrifice” themselves by continuing to explore even when they do not have sufficient battery to return to the base station. We compare the performance of the proposed approach to state-of-the-art frontier-based exploration, and results show gains in explored area. The feasibility of components of the proposed approach is also demonstrated on a team of two custom-built quadcopters exploring an office environment. Xue Wang *, Jun-Jie Ma , Sheng Wang and Dao-Wei Bi State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instruments, Tsinghua University, Beijing 100084, P. R. China; E-mails: mjj, wang_sheng00, bdw02 @mails.tsinghua.edu.cn. * Author to whom correspondence should be addressed; E-mail: wangxue@mail.tsinghua.edu.cn. Received: 25 April 2007 Accepted: 8 May 2007 Published: 10 May 2007 Abstract: The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and Dijkstra’s algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications. Keywords: Wireless sensor network, deployment optimization, energy efficiency, particle swarm optimization, simulated annealing. Fetching items is an important problem for a social robot. It requires a robot to interpret a person's language and gesture and use these noisy observations to infer what item to deliver. If the robot could ask questions, it would help the robot be faster and more accurate in its task. Existing approaches either do not ask questions, or rely on fixed question-asking policies. To address this problem, we propose a model that makes assumptions about cooperation between agents to perform richer signal extraction from observations. This work defines a mathematical framework for an item-fetching domain that allows a robot to increase the speed and accuracy of its ability to interpret a person's requests by reasoning about its own uncertainty as well as processing implicit information (implicatures). We formalize the item-delivery domain as a Partially Observable Markov Decision Process (POMDP), and approximately solve this POMDP in real time. Our model improves speed and accuracy of fetching tasks by asking relevant clarifying questions only when necessary. To measure our model's improvements, we conducted a real world user study with 16 participants. Our method achieved greater accuracy and a faster interaction time compared to state-of-the-art baselines. Our model is 2.17 seconds faster (25 faster) than a state-of-the-art baseline, while being 2.1 more accurate. Swarm robotics (SR) offers promising solutions to real-world problems that can be modeled as foraging tasks, e.g. disaster trash cleanup or object gathering for construction. Yet current SR foraging approaches make limiting assumptions that restrict their applicability to selected real-world environments. We propose an improved self-organized task allocation method based on task partitioning that removes restrictions such as: (1) a priori knowledge of foraging environment, and (2) strict limitations on intermediate drop pickup site behavior. With experiments in simulation, we show that under the proposed constraint relaxation, our approach still provides performance increases when compared to an unpartitioned strategy within some combinations of swarm sizes, robot capabilities, and environmental conditions. This work broadens the applicability of SR foraging approaches, showing that they can be effective under ideal conditions while continuing to perform robustly in more volatile challenging environments. We present information theoretic search strategies for single and multi-robot teams to find and localize the source of a biochemical or radiological materials in turbulent flows. In our work, robots rely on sporadic and intermittent sensor readings to synthesize information maximizing exploration strategies to find and localize the position of the source. By reasoning about the spatial distribution of these sensory cues, the robots are able to construct a belief distribution over the possible positions of the source. The belief distribution is then employed to synthesize motion strategies that drives the robots to regions in the workspace that results in the largest decrease in the entropy of the belief distribution for the source position. We validate the proposed strategies in 2D and 3D environments and consider the performance of the strategies when robots have limited access to global pose information. In particular, the proposed strategies are validated using a three dimensional (3D) time-varying computational fluid model of the 2010 Deep Water Horizon oil spill. In this paper, we propose an algorithm that enables robots to improve their spatial-semantic representation of the environment by engaging users in dialog. The algorithm aims to reduce the entropy in maps formulated based upon user-provided natural language descriptions (e.g., "The kitchen is down the hallway"). The robot's available information-gathering actions take the form of targeted questions intended to reduce the entropy over the grounding of the user's descriptions. These questions include those that query the robot's local surround (e.g., "Are we in the kitchen?") as well as areas distant from the robot (e.g., "Is the lab near the kitchen?"). Our algorithm treats dialog as an optimization problem that seeks to balance the information-theoretic value of candidate questions with a measure of cost associated with dialog. In this manner, the method determines the best questions to ask based upon expected entropy reduction while accounting for the burden on the user. We evaluate the entropy reduction based upon a joint distribution over a hybrid metric, topological, and semantic representation of the environment learned from user-provided descriptions and the robot's sensor data. We demonstrate that, by asking deliberate questions of the user, the method results in significant improvements in the accuracy of the resulting map. 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. In this article, we analyze the behavior of a group of robots involved in an object retrieval task. The robots' control system is inspired by a model of ants' foraging. This model emphasizes the role of learning in the individual. Individuals adapt to the environment using only locally available information. We show that a simple parameter adaptation is an effective way to improve the efficiency of the group and that it brings forth division of labor between the members of the group. Moreover, robots that are best at retrieving have a higher probability of becoming active retrievers. This selection of the best members does not use any explicit representation of individual capabilities. We analyze this system and point out its strengths and its weaknesses. We consider the problem of periodic task scheduling in sensor nodes powered with energy harvesters. In particular, we focus on systems with stochastic energy sources such as solar panels, and we present two energy-aware scheduling algorithms that reduce the likelihood of task violations. Our algorithms, called Smooth to Average Method (STAM) and Smooth to Full Utilization (STFU), are static schedulers that do not require prescience of the incoming energy to operate effectively. Dependence on a team of robots to carry out the exploration missions has many benefits such as speeding up the missions; provided that the coordination among robots is maintained. Many circumstances can limit the communication, which is crucial for the coordination among robots (e.g. impenetrable barriers, high temperature etc.). A periodic rendezvous strategy is considered in this paper as a work around in order to overlap communication ranges of the robots. Attending these periodic rendezvous sessions requires that the robots interrupt their current exploration progress periodically and traverse back to the rendezvous points (i.e. Interruptibility). During their trips to these points, they do not gain new knowledge since they cross already explored parts of the area. Therefore, using rendezvous strategies improves the team behavior but has a negative impact on the time efficiency. The contribution of this paper is to mitigate this negative impact of Interruptibility on explorations while maintaining the coordination among robots. The mitigation algorithm is evaluated on several graphs and its performance is compared with other rendezvous approaches where the results are promising. In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in previous work, the experience was replayed at random, without considering which episode might be the most valuable for learning. In this paper, we develop an energy-based framework for prioritizing hindsight experience in robotic manipulation tasks. Our approach is inspired by the work-energy principle in physics. We define a trajectory energy function as the sum of the transition energy of the target object over the trajectory. We hypothesize that replaying episodes that have high trajectory energy is more effective for reinforcement learning in robotics. To verify our hypothesis, we designed a framework for hindsight experience prioritization based on the trajectory energy of goal states. The trajectory energy function takes the potential, kinetic, and rotational energy into consideration. We evaluate our Energy-Based Prioritization (EBP) approach on four challenging robotic manipulation tasks in simulation. Our empirical results show that our proposed method surpasses state-of-the-art approaches in terms of both performance and sample-efficiency on all four tasks, without increasing computational time. A video showing experimental results is available at this https URL Efficient collaboration requires collaborators to monitor the behaviors of their partners, make inferences about their task intent, and plan their own actions accordingly. To work seamlessly and efficiently with their human counterparts, robots must similarly rely on predictions of their users' intent in planning their actions. In this paper, we present an anticipatory control method that enables robots to proactively perform task actions based on anticipated actions of their human partners. We implemented this method into a robot system that monitored its user's gaze, predicted his or her task intent based on observed gaze patterns, and performed anticipatory task actions accord- ing to its predictions. Results from a human-robot interaction experiment showed that anticipatory control enabled the robot to respond to user requests and complete the task faster-2.5 seconds on average and up to 3.4 seconds-compared to a robot using a reactive control method that did not anticipate user intent. Our findings highlight the promise of performing anticipatory actions for achieving efficient human-robot teamwork. This paper considers the allocation of time slots in a frame, as well as power and rate to multiple receivers on an energy harvesting downlink. Energy arrival times that will occur within the frame are known at the beginning of the frame. The goal is to optimize throughput in a proportionally fair way, taking into account the inherent differences of channel quality among users. Analysis of structural characteristics of the problem reveals that it can be formulated as a biconvex optimization problem, and that it has multiple optima. Due to the biconvex nature of the problem, a Block Coordinate Descent (BCD) based optimization algorithm that converges to an optimal solution is presented. However, finding the optimal allocation with BCD entails a computational complexity that increases sharply in terms of the number of users or slots. Therefore, certain structural characteristics of the optimal power-time allocation policy are derived. Building on those, two simple and computationally scalable heuristics, PTF and ProNTO are proposed. Simulation results suggest that PTF and ProNTO can closely track the performance of BCD which achieves a good balance between total throughput and fairness. We use distributed computing tools to provide a new perspective on the behavior of cooperative biological ensembles. We introduce the Ants Nearby Treasure Search (ANTS) problem, a generalization of the classical cow-path problem [10, 20, 41, 42], which is relevant for collective foraging in animal groups. In the ANTS problem, k identical (probabilistic) agents, initially placed at some central location, collectively search for a treasure in the two-dimensional plane. The treasure is placed at a target location by an adversary and the goal is to find it as fast as possible as a function of both k and D, where D is the distance between the central location and the target. This is biologically motivated by cooperative, central place foraging, such as performed by ants around their nest. In this type of search there is a strong preference to locate nearby food sources before those that are further away. We focus on trying to find what can be achieved if communication is limited or altogether absent. Indeed, to avoid overlaps agents must be highly dispersed making communication difficult. Furthermore, if the agents do not commence the search in synchrony, then even initial communication is problematic. This holds, in particular, with respect to the question of whether the agents can communicate and conclude their total number, k. It turns out that the knowledge of k by the individual agents is crucial for performance. Indeed, it is a straightforward observation that the time required for finding the treasure is Ω(D + D2 k), and we show in this paper that this bound can be matched if the agents have knowledge of k up to some constant approximation. We present a tight bound for the competitive penalty that must be paid, in the running time, if the agents have no information about k. Specifically, this bound is slightly more than logarithmic in the number of agents. In addition, we give a lower bound for the setting in which the agents are given some estimation of k. Informally, our results imply that the agents can potentially perform well without any knowledge of their total number k, however, to further improve, they must use some information regarding k. Finally, we propose a uniform algorithm that is both efficient and extremely simple, suggesting its relevance for actual biological scenarios.