Yasushi Kambayashi

Saving Energy Consumption of Multi-robots Using Higher-Order Mobile Agents

This paper presents a framework for controlling intelligent robots connected by communication networks. This framework provides novel methods to control coordinated systems using higher-order mobile agents. Higher-order mobile agents are hierarchically structured agents that can contain other mobile agents. The combination of the higher-order mobile agent and mobile multi-robots open a new horizon of efficient use of mobile robot resources. Instead of physical movement of multi-robots, mobile software agents can migrate from one robot to another so that they can find the most suitably equipped and/or the most suitably located robots to perform their task. Thus the framework presented here provides efficient use of robot resources. In this paper, we focus on the energy saving. We have demonstrated the efficiency by numerical experiments.

Multi-objective genetic algorithm for solving N-version program design problem

Abstract N -version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N -version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0–1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost.

Suppressing the Total Costs of Executing Tasks Using Mobile Agents

This paper presents a framework for controlling multiple robots connected by communication networks. Instead of making multiple robots pursue several tasks simultaneously, the framework makes mobile software agents migrate from one robot to another to perform the tasks. Since mobile software agents can migrate to arbitrary robots by wireless communication networks, they can find the most suitably equipped and/or the most suitably located robots to perform their task. In the previous papers, we have showed that this manner of controlling multiple robots can decrease the number of required robot resources, and therefore can suppress energy consumption in aggregation. In this paper, we focus on the efficiency aspect of the manner of controlling multiple robots. Our controlling manner for multiple robot is expected to achieve further contribution for suppressing the total costs of executing tasks. We demonstrate the effectiveness of our manner of controlling multiple robot by numerical experiments.

The separation principle: A programming paradigm

This paper introduces a programming paradigm known as the principle.The separation principle is a simple and natural way of constructing programs.Its intuitive nature and ease of use make it easy t...

Integrating Uncomfortable Intersection-Turns to Subjectively Optimal Route Selection Using Genetic Algorithm

Route selection is one of the most important problems for a car navigation system. Given a pair of origin and destination, there are many possible routes. Most current car navigation systems propose the shortest path from the origin to the destination. Selecting the shortest path is not a hard problem, but the shortest path is not always what the user wants; what the user really wants to have is the most comfortable route for him or her to drive. In other words, the driver wants to have a car navigation system to propose the subjectively optimal route for him or her. Finding such a route requires enumerating all the possible routes, and is known as a NP-hard problem. In order to reduce computational complexity, we have employed a GA to find a (subjectively) quasi optimal route for the driver. In this paper, we report our attempt to integrate uncomfortable-turns in to the conditions of our GA-based route selection algorithm. The numerical experiments demonstrate the feasibility of our GA-based route selection method.

A Resource Discovery Method Based on Multi-agents in P2P Systems

A peer-to-peer (P2P) system consists of a number of decentralized distributed network nodes that are capable of sharing resources without centralized supervision. Many applications such as IP-phone, contents delivery network (CDN), distributed computing adopt P2P technology into their base communication systems. One of the most important functions in P2P system is locating resources, and it is generally hard to achieve due to the intrinsic nature of P2P, i.e. dynamic re-configuration of the network. In this paper, we propose an efficient resource locating method in pure P2P system based on multi-agents. The model of our system is a DHT base P2P system that consists of nodes with DHT (high performance nodes) and nodes without DHT (regular nodes). All the resources as well as resource information are managed by cooperative multi-agents. Migrating multi-agents are expected to reduce communication traffic in the network. Efficient migration is achieved through the clustering of nodes that makes correlated nodes in a group by the logical similarity. The numerical experiments through simulation have shown a significant reduction of generated messages.

Ant colony clustering using mobile agents as ants and pheromone

This paper presents a new approach for controlling mobile multiple robots connected by communication networks. The control mechanism is based on a specific Ant Colony Clustering (ACC) algorithm. In traditional ACC, an ant convey an object, but in our approach, the ant implemented as a mobile software agent controls the robot corresponding to an objects, so that the object moves to the direction required by the ant agent. At this time, the process where an ant searches an object corresponds to some migrations of the ant agent, which are much more efficient than physically searching. Also, the ACC uses a pheromone for making a cluster grown and stabilized. However, it is difficult to implement such a pheromone as an physical entity, because it can diffuse, mutually intensify its strength, and restrict its effect in its scope. In our approach, the pheromone is implemented as a mobile software agent as well as an ant. The mobile software agents can migrate from one robot to another, so that they can diffuse over robots within their scopes. In addition, since they have their strength as vector values, they can represent mutually intensifying as synthesis of vectors. We have been developing elemental techniques for controlling multiple robots using mobile software agents, and showed effectiveness of applying them to the previous ACC approach which requires a host for centrally controlling robots. The new ACC approach decentralizes it, and makes a robot system free from special devices for checking locations.

Controlling biped walking robots using genetic algorithms in mobile agent environment

The design and implementation of a control program for biped walking robots using the genetic algorithms (GA) are presented. The most difficult problem with biped walking robots is that they have too many possible gaits. Generally it is impossible to find the optimal gait for a given route. In order to control biped walking robots, we have employed GA to determine the gaits of the robots. It is known that keeping the zero moment point (ZMP) in certain area is necessary for stable movement of a biped walking robot deriving ZMP is not theoretically difficult; it is just a matter of solving kinetic equations. The problem is that, for a certain series of ZMPs, the robot can have too many gaits and too hard to find the optimal one. We are using a control program using GA to produce approximately optimal gaits.

A Functional Language for Mobile Agents with Dynamic Extension

This paper presents a prototypical functional language for controlling intelligent robots. We have demonstrated that coordinated systems to control intelligent robots are effectively constructed by higherorder mobile agents. The system was built in Java language. We propose that such system can be described in a functional language. In this paper, we report the design and a key implementation technique of the functional language to describe higher-order mobile agents for intelligent robot control.

Distributed ant colony clustering using mobile agents and its effects

This paper presents a new approach for controlling mobile multiple robots connected by communication networks. The control mechanism is based on a specific Ant Colony Clustering (ACC) algorithm. In traditional ACC, an ant convey an object, but in our approach, the ant is implemented as a mobile software agent that controls the robot which is corresponding to an object, so that the object moves to the direction ordered by the ant agent. In this time, the process in which an ant searches an object corresponds to a sequence of migrations of the ant agent, which is much more efficient than the search by a mobile robot. In our approach, not only the ant but also the pheromone is implemented as a mobile software agent. The mobile software agents can migrate from one robot to another, so that they can diffuse over robots within their scopes. In addition, since they have their strengths as vector values, they can represent mutual intensification as synthesis of vectors. We have been developing elemental techniques for controlling multiple robots using mobile software agents, and showed effectiveness of applying them to the previous ACC approach which requires a host computer that centrally controls mobile robots. The new ACC approach decentralizes the mobile robot system, and makes the system free from special devices for checking locations.