An Energy-Aware Algorithm for Large Scale Foraging Systems

International audienceThe foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of coordinated robots have to find and transport one or more objects to one or more specific storage points. Swarm robotics has been widely considered in such situations, due to its strengths such as robustness, simplicity and scalability. Typical multi-robot foraging systems currently consider tens to hundreds of agents. This paper presents a new algorithm called Energy-aware Cooperative Switching Algorithm for Foraging (EC-SAF) that manages thousands of robots. We investigate therefore the scalability of EC-SAF algorithm and the parameters that can affect energy efficiency overtime. Results indicate that EC-SAF is scalable and effective in reducing swarm energy consumption compared to an energy-aware version of the reference well-known c-marking algorithm (Ec-marking)

Multi‑Agent Foraging: state‑of‑the‑art and research challenges

International audienceThe foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth. First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems. Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments

Decentralized algorithm of dynamic task allocation for a swarm of homogeneous robots

The current trends in the robotics field have led to the development of large-scale swarm robot systems, which are deployed for complex missions. The robots in these systems must communicate and interact with each other and with their environment for complex task processing. A major problem for this trend is the poor task planning mechanism, which includes both task decomposition and task allocation. Task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize the cost while satisfying operational constraints. Task allocation mechanism must be run by each robot, which integrates the swarm whenever it senses a change in the environment to make sure the robot is assigned to the most appropriate task, if not, the robot should reassign itself to its nearest task. The main contribution in this thesis is to maximize the overall efficiency of the system by minimizing the total time needed to accomplish the dynamic task allocation problem. The near-optimal allocation schemes are found using a novel hybrid decentralized algorithm for a dynamic task allocation in a swarm of homogeneous robots, where the number of the tasks is more than the robots present in the system. This hybrid approach is based on both the Simulated Annealing (SA) optimization technique combined with the Discrete Particle Swarm Optimization (DPSO) technique. Also, another major contribution in this thesis is the formulation of the dynamic task allocation equations for the homogeneous swarm robotics using integer linear programming and the cost function and constraints are introduced for the given problem. Then, the DPSO and SA algorithms are developed to accomplish the task in a minimal time. Simulation is implemented using only two test cases via MATLAB. Simulation results show that PSO exhibits a smaller and more stable convergence characteristics and SA technique owns a better quality solution. Then, after developing the hybrid algorithm, which combines SA with PSO, simulation instances are extended to include fifteen more test cases with different swarm dimensions to ensure the robustness and scalability of the proposed algorithm over the traditional PSO and SA optimization techniques. Based on the simulation results, the hybrid DPSO/SA approach proves to have a higher efficiency in both small and large swarm sizes than the other traditional algorithms such as Particle Swarm Optimization technique and Simulated Annealing technique. The simulation results also demonstrate that the proposed approach can dislodge a state from a local minimum and guide it to the global minimum. Thus, the contributions of the proposed hybrid DPSO/SA algorithm involve possessing both the pros of high quality solution in SA and the fast convergence time capability in PSO. Also, a parameters\u27 selection process for the hybrid algorithm is proposed as a further contribution in an attempt to enhance the algorithm efficiency because the heuristic optimization techniques are very sensitive to any parameter changes. In addition, Verification is performed to ensure the effectiveness of the proposed algorithm by comparing it with results of an exact solver in terms of computational time, number of iterations and quality of solution. The exact solver that is used in this research is the Hungarian algorithm. This comparison shows that the proposed algorithm gives a superior performance in almost all swarm sizes with both stable and small execution time. However, it also shows that the proposed hybrid algorithm\u27s cost values which is the distance traveled by the robots to perform the tasks are larger than the cost values of the Hungarian algorithm but the execution time of the hybrid algorithm is much better. Finally, one last contribution in this thesis is that the proposed algorithm is implemented and extensively tested in a real experiment using a swarm of 4 robots. The robots that are used in the real experiment called Elisa-III robots

A Hormone Inspired System for On-line Adaptation in Swarm Robotic Systems

Individual robots, while providing the opportunity to develop a bespoke and specialised system, suffer in terms of performance when it comes to executing a large number of concurrent tasks. In some cases it is possible to drastically increase the speed of task execution by adding more agents to a system, however this comes at a cost. By mass producing relatively simple robots, costs can be kept low while still gaining the benefit of large scale multi-tasking. This approach sits at the core of swarm robotics. Robot swarms excel in tasks that rely heavily on their ability to multi-task, rather than applications that require bespoke actuation. Swarm suited tasks include: exploration, transportation or operation in dangerous environments. Swarms are particularly suited to hazardous environments due to the inherent expendability that comes with having multiple, decentralised agents. However, due to the variance in the environments a swarm may explore and their need to remain decentralised, a level of adaptability is required of them that can't be provided before a task begins. Methods of novel hormone-inspired robotic control are proposed in this thesis, offering solutions to these problems. These hormone inspired systems, or virtual hormones, provide an on-line method for adaptation that operates while a task is executed. These virtual hormones respond to environmental interactions. Then, through a mixture of decay and stimulant, provide values that grant contextually relevant information to individual robots. These values can then be used in decision making regarding parameters and behavioural changes. The hormone inspired systems presented in this thesis are found to be effective in mid-task adaptation, allowing robots to improve their effectiveness with minimal user interaction. It is also found that it is possible to deploy amalgamations of multiple hormone systems, controlling robots at multiple levels, enabling swarms to achieve strong, energy-efficient, performance

A robot swarm assisting a human fire-fighter

Emergencies in industrial warehouses are a major concern for fire-fighters. The large dimensions, together with the development of dense smoke that drastically reduces visibility, represent major challenges. The GUARDIANS robot swarm is designed to assist fire-fighters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting fire-fighters. We explain the swarming algorithms that provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus, the robot swarm is able to provide guidance information to the humans. Together with the fire-fighters we explored how the robot swarm should feed information back to the human fire-fighter. We have designed and experimented with interfaces for presenting swarm-based information to human beings

A swarm intelligence based approach to the mine detection problem

This research focuses on the application of swarm intelligence to the problem of mine detection. Swarm Intelligence concepts have captivated the interests of researchers mainly in collective robotics, optimization problems (traveling salesman problem (TSP), quadratic assignment problem, graph coloring etc.), and communication networks (routing) etc [1]. In the mine detection problem we are faced with sub problems such as searching for the mines over the minefield, defusing them effectively, and assuring that the field is clear of mines within the least possible time. In the problem, we assume that the mines can be diffused by the collective action of the robots for which a model based on ant colonies is given. In the first part of the project we study the ant colony system applied to the mine detection problem. The theoretical aspects such as the ant\u27s behavior (reaction of the ants to various circumstances that it faces), their motion over the minefield, and their process of defusing the mines are investigated. In the second section we highlight a certain formulation that the ants may be given for doing the task effectively. The ants do the task effectively when they are able to assure that the minefield is clear of the mines within the least possible time. A compilation of the results obtained by the various studies is tabulated. In the third and final section we talk about our emulations conducted on the Multi Agent Biorobotics Lab-built groundscout robots, which were used for the demonstration of our swarm intelligence-based algorithms at a practical basis. The various projects thus far conducted were a part of the Multi Agent Biorobotics Lab at Rochester Institute of Technology