Solar-powered aquaponics prototype as sustainable approach for food production

This paper presents the establishment of a solar-powered aquaponics prototype as a sustainable, cost effective and environmentally sound approach for food production. In this study, a prototype bench top aquaponics rig with an integrated 20 W solar panel were fabricated for the cultivation of red Hybrid Tilapia (Oreochromis spp.) and leaf mustard (Brassica juncea). The size of the fish tank is about 29.5L and serves as the base for the setup. Additionally, the hydroponic grower compartment (0.45 m (L) � 0.32 m (W) � 0.13 m (H)) was stacked on top of the fish tank and was filled with LECA media bed for the plant growth. Two important operating parameters were studied. First, the amount of energy produced by the solar panel and the energy consumption by the water pump used in the setup. Secondly, the resultant effects from fish cultivation and plants growth on the water qualities and nitrification effi�ciency of the aquaponics unit. The aquaponics unit was operated for a month and the values of pH, tem�perature, and ammonia level were measured to be within the range of 6.4–7.2, 27.1–31.7 �C, and 1 mg�L�1 , respectively. Survival rate for fish was about 75% with specific growth rate (SGR) of 3.75% per day and food conversion ratio of about 1.15. A slight nutrient deficiency was evident and plants showed a healthy growth with height gain as high as 5 cm was achieved. Despite raining season, our data shows that the energy produced via 20 W solar panel enabled the unit to run at night without depending on local electricity for nearly two hours. Clearly, a larger solar panel is needed for longer operation. Nevertheless, the study has proven the potential of operating a low cost aquaponics setup using renew�able energy for a sustainable food production method

Negotiation with reaction functions for solving complex task allocation problems

Abstract-We study task-allocation problems where cooperative robots need to perform tasks simultaneously. We develop a distributed negotiation procedure that allows robots to find all task exchanges that reduce the team cost of a given task allocation, without robots having to know how other robots compute their robot costs. Finally, we demonstrate empirically that our negotiation procedure can substantially reduce the team costs of task allocations resulting from existing taskallocation procedures, including sequential single-item auctions

Negotiation with reaction functions for solving complex task allocation problems

Abstract-We study task-allocation problems where cooperative robots need to perform tasks simultaneously. We develop a distributed negotiation procedure that allows robots to find all task exchanges that reduce the team cost of a given task allocation, without robots having to know how other robots compute their robot costs. Finally, we demonstrate empirically that our negotiation procedure can substantially reduce the team costs of task allocations resulting from existing taskallocation procedures, including sequential single-item auctions

Upper-bound cost analysis of a market-based algorithm applied to the initial formation problem

©2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA, Oct 29-Nov 2, 2007.DOI: 10.1109/IROS.2007.4399100In this paper, an analysis of a market-based approach applied to the Initial Formation Problem is presented. This problem tries to determine which mobile sensor should go to each position of a desired formation in order to minimize an objective. In our case, this objective is the global distance traveled by all the mobile sensors. In this analysis, a bound on the efficiency for the market-based algorithm is calculated and it is shown that the relative difference as compared with the optimal solution increases with the logarithm of the total number of mobile sensors. The theoretical results are validated with numerous simulations

Incremental task selection and allocation for the real-world multiple travelling robot problem

Bu çalışmada, Çoklu-Robot Çoklu-Hedef Ataması problemi için bir gerçek zamanlı görev seçme ve atama yöntemi önerilmektedir. Çoklu-Robot Çoklu-Hedef Ataması problemi, literatürde iyi bilinen ve tüm veri kümeleri için en iyi çözümlerin polinomsal algoritmalarla bulunamadığı MTSP (Multiple Traveling Salesman Problem) probleminin her bir hedefin en az bir robot tarafından ziyaret edilmesini gerektiren değişik bir uyarlamasıdır. Bu problem için farklı eniyileme amaç fonksiyonları tanımlanabilir (Örn. yürütme zamanını iyileme, robotların toplam yollarını iyileme, vb.). Bu makalede, bu problemin gerçek dünya versiyonu ele alınıp, yürütme zamanında ortaya çıkabilecek problemler irdelenmiştir. Çoklu-robot sistemlerinin ortaklaşa çalışmalarında karşılaşılan en büyük güçlükler, görevlerin yürütme zamanının önceden kesin olarak tahmin edilememesi, yürütme süresince değişebilen maliyet değerleri ile belirsizlik ve tutarsızlıklardan kaynaklanır. Bu çalışmada, yürütme zamanı kısıtlamalarını da aşmak üzere etkin bir dinamik görev seçim ve atama yöntemi önerilmekte ve önerilen yöntemin, hem benzetim ortamlarında hem de gerçek robotlar üzerinde yapılan testlerle başarım analizi yapılmaktadır. Önerilen yöntem, yürütme zamanında oluşabilecek birçok hataya karşı dayanıklı olarak sistemin güncel durumuna göre artımlı ve dağıtılmış bir görev ataması gerçekler. Gerçek zamanlı yürütmeye uygun şekilde iletişim gereksinimleri minimumda tutulmaya çalışılmıştır. Gerçeklenen testler yöntemin bilgi-işlemsel açıdan etkin ve düşük maliyetli, sistemin hatalara karşı dayanıklı olmasını sağlayacak şekilde çalıştığını gösterir niteliktedir. Anahtar Kelimeler: Çoklu-robot sistemleri, gerçek zamanlı çoklu-robot çoklu-hedef atama, artımlı görev seçimi, dinamik görev atama, hataya dayanıklılık.In this study, the real-world Multiple Travelling Robot Problem (MTRP) is analyzed and an integrated approach is proposed to solve this problem in real time. MTRP is a generalization of the well-known Multiple Traveling Salesman Problems and is solved by a multi-robot team. In MTRP, a different version of the well known NP-hard MTSP (Multiple Traveling Salesman Problem), each target must be visited by at least one robot in its open tour. Depending on the selected application domain, various objectives may be defined for this problem (e.g. minimization of total path length, time, makespan etc.). Although this problem has emerged from Operations Research, it has become one of the main problems in multi-robot research for different missions (e.g., search and rescue, space, or surveillance operations, etc.). In this article, besides the target allocation issue, the real challenges of this problem are presented. Unpredictability of the exact processing times of tasks, unstable cost values during runtime and inconsistencies due to uncertain information form the main difficulties of the task allocation problem for robot systems. Since the real world is beyond the control of robots, in most cases, the Operations Research solutions are not directly applicable due to either robot hardware/software limitations or environmental dynamics. These approaches may become impractical when the size of the mission is even moderate or the cost values change frequently because of the uncertain knowledge, changes in the environment (including failures) or the changing structure of the mission (e.g. online tasks). Furthermore, robots have continuous path planning burdens for target sets in dynamic environments. Expensive computational efforts for initial allocations may become redundant. We propose a solution as a generalized framework " DEMiR-CF (Distributed and Efficient Multi-Robot Cooperation Framework) " which includes dynamic task selection, distributed task allocation and contingency handling mechanisms. These mechanisms and lower level robot controllers, the motor and sensory modules are integrated together to solve the real-world MTRP. DEMiR-CF, while capable of handling diverse contingencies, performs an incremental task allocation method based on the current information about the environment. Globally efficient solutions are obtained by the proposed mechanisms that form priority based rough schedules and select the most suitable tasks from these schedules. Rough schedules are formed by using information regarding the beliefs about the other robots. Since DEMiR-CF is for real-world task execution, communication requirements are kept at minimum as much as possible. The approach is distributed and computationally efficient. Target allocation and route construction is integrated into each other by an incremental assignment approach. Real time conditions and contingencies that change the problem instance are handled at the same time by the designed contingency handling mechanisms. Robots keep system models to correct models of their own or warn other robots to maintain system consistency. Empirical evaluations of the system performed on the Webots simulator and on Khepera II robots reveal the efficiency of the integrated components of the approach. Experiments are designed in three sets. In the first set of the experiments, evaluations of the proposed cost functions to be used with DEMiR-CF are performed. Comparisons are made both with the optimal results taken from the IP solver CPLEX and that of the Prim Allocation approach, one of the efficient methods to solve this problem. As the results illustrate, allocating all targets from scratch and generating routes of robots may result in suboptimal solutions. Therefore the target allocation and the route construction should be integrated for efficient heuristic approaches. This integration and incremental allocation is also useful for eliminating redundant calculations especially in highly dynamic or unknown environments. In the second set of experiments, scalability of the framework and the response efficiency of the contingency handling mechanism integrated into the framework are evaluated. The scalability of the approach is validated and the efficiency of using the contingency handling mechanisms is observed in the results. The third set of experiments is performed on real robots. As results illustrate, both incremental task selection and allocation approaches produce efficient results and the contingency handling mechanisms make the system robust and allow the handling of real-time online situations. Keywords: Multi-robot systems, multi-robot multi-target assignment in real-time, incremental task selection, dynamic task allocation, robustness.

Solving the Task Variant Allocation Problem in Distributed Robotics

We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a task variant, which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. We formalise the problem of assigning task variants to processors as a mathematical model that incorporates typical constraints found in robotics applications; the model is a constrained form of a multi-objective, multi-dimensional, multiple-choice knapsack problem. We propose and evaluate three different solution methods to the problem: constraint programming, a constructive greedy heuristic and a local search metaheuristic. Furthermore, we demonstrate the use of task variants in a real instance of a distributed interactive multi-agent navigation system, showing that our best solution method (constraint programming) improves the system’s quality of service, as compared to the local search metaheuristic, the greedy heuristic and a randomised solution, by an average of 16, 31 and 56% respectively

An Integrated Approach for Achieving Multi-Robot Task Formations

©2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/TMECH.2009.2014056In this paper, a problem, called the initial formation problem, within the multirobot task allocation domain is addressed. This problem consists in deciding which robot should go to each of the positions of the formation in order to minimize an objective. Two different distributed algorithms that solve this problem are explained. The second algorithm presents a novel approach that uses cost means to model the cost distribution and improves the performance of the task allocation algorithm. Also, we present an approach that integrates distributed task allocation algorithms with a behavior-based architecture to control formations of robot teams. Finally, simulations and real experiments are used to analyze the formation behavior and provide performance metrics associated with implementation in realistic scenarios