Waypoint-Based Path Tracking Approach For Self-Organized Swarm Robots (original) (raw)
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Swarm Intelligence, 2011
In this paper, we study the problem of exploration and navigation in an unknown environment from an evolutionary swarm robotics perspective. In other words, we search for an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to cope with the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. The collective strategy is synthesised through evolutionary robotics techniques, and is based on the emergence of a dynamic structure formed by the robots moving back and forth between the two target areas. Due to this structure, each robot is able to maintain the right heading and to efficiently navigate between the two areas. The evolved behaviour proved to be effective in finding the shortest path, adaptable to new environmental conditions, scalable to larger groups and larger environment size, and robust to individual failures.
Algorithm for Movement of Swarm Robots
International Journal of Computer Applications, 2012
Groups of robots can solve problems in fundamentally different ways than individuals while achieving higher levels of performance. This paper investigates the application of swarm intelligence principles for the cooperative behaviour of autonomous collective robots. Using swarm intelligence technique robots are able to get their optimized path during navigation. In the task of chain based path formation of swarm robots, a chain of multiple robots is formed between nest and prey for some specified work. Multiple robots randomly move to search the Nest and gather at Nest, after perceiving the Nest robots can self organizing into chain and again move randomly to search the Prey. In this paper I have proposed a method for the movement of swarm robots i.e. Spiral Move, which takes less time compare to random search.
This paper presents a path-planning approach to enable a swarm of robots move to a goal region while avoiding collisions with static and dynamic obstacles. To provide scalability and account for the complexity of the interactions in the swarm, the proposed approach combines probabilistic roadmaps with potential fields. The underlying idea is to provide the swarm with a series of intermediate goals which are obtained by constructing and searching a roadmap of likely collision-free guides. As the swarm moves from one intermediate goal to the next, it relies on potential fields to quickly react and avoid collisions with static and dynamic obstacles. Potential fields are also used to ensure that the swarm moves in cohesion. When the swarm deviates or is unable to reach the planned intermediate goals due to interference from the dynamic obstacles, the roadmap is searched again to provide alternative guides. Experiments conducted in simulation demonstrate the efficiency and scalability of the approach.
Chain based path formation in swarms of robots
2006
Abstract. In this paper we analyse a previously introduced swarm intelligence control mechanism used for solving problems of robot path formation. We determine the impact of two probabilistic control parameters. In particular, the problem we consider consists in forming a path between two objects which an individual robot cannot perceive simultaneously. Our experiments were conducted in simulation.
Navigation of multiple mobile robots using swarm intelligence
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
This research investigates the application of swarm intelligence principles for the cooperative behavior of autonomous collective robots. Using swarm intelligence technique robots are able to get their optimized path during navigation. A successful way of structuring the navigation task deals with the issue of cooperative behavior among multiple mobile robots. Using Ant Colony Optimization, robot path planning in two-dimension environment is studied. In this paper the current study introduces the intelligent finding optimum mechanism of ant colony. The theory has been tested both in simulation and experimental modes.
Evolution of Self-organised Path Formation in a Swarm of Robots
Lecture Notes in Computer Science, 2010
We present a set of experiments in which a robotic swarm manages to collectively explore the environment, forming a path to navigate between two target areas, which are too distant to be perceived by an agent at the same time. Robots within the path continuously move back and forth between the two locations, exploiting visual interactions with their neighbours. The global group behaviour is obtained through an evolutionary process and presents emergent properties like robustness, path optimisation and scalability, which recall ants trail formation.
Optimization of Autonomous Multi-Robot Path Planning and Navigation Using Swarm Intelligence
This paper explores the behavior and optimization of a group of mobile agents or robots to find the shortest path between the food and source, without any visible, central, active coordination mechanism. Feedback by the agent during traversal of the path causes more agent concentration on the path, thereby influencing the behavior of the other agents. Several obstacles are likely to be encountered in the course of this traversal. The objective of the agent is to find an appropriate solution to bring itself closer to the goal considering the cost, time and path availability. A typical case of Traveling Salesman Problem is incorporated to achieve a proper solution to this navigation problem wherein, an already visited node/location will not be visited again by the robot.
Path Planning for Swarms by Combining Probabilistic Roadmaps and Potential Fields
This paper combines probabilistic roadmaps with potential fields in order to enable a robotic swarm to e↵ectively move to a desired destination while avoiding collisions with obstacles and each other. Potential fields provide the robots with local, reactive, behaviors that seek to keep the swarm moving in cohesion and away from the obstacles. The probabilistic roadmap provides global path planning which guides the swarm through a series of intermediate goals in order to e↵ectively reach the desired destination. Random walks in combination with adjustments to the potential fields and intermediate goals are used to help stuck robots escape local minima. Experimental results provide promising validation on the e ciency and scalability of the proposed approach. Source code is made publicly available.
Method of Coordination of Motion of Swarm Robotic Systems
Scientific Journal of Astana IT University
Maintaining a specific geometric pattern is essential in various applications where groups of autonomous robots must follow a given path. Proper organization of the geometric pattern can lead to several benefits such as cost reduction, increased system reliability, and efficiency while providing a reconfigurable and flexible structure of the system. Military missions and traffic systems are examples where maintaining certain geometric patterns are widely used. However, little is known about how to develop an effective algorithm that guarantees collision avoidance and obstacle avoidance while maintaining the geometric pattern. This paper presents an algorithm for movement with a certain geometric structure of a group of autonomous mobile robots that maintains the required geometric pattern and ensures the avoidance of collisions and obstacles. The proposed algorithm is behavior-based and utilizes a set of rules that allow the robots to navigate around obstacles and avoid collisions. ...
Swarm Robotics with Circular Formation Motion: Including Obstacles Avoidance
2017
The robots science has been developed over the past few years, where robots have become used to accomplish difficult, repetitive or accurate tasks, which are very hard for humans to carry out. In this paper, we propose an algorithm to control the motion of a swarm of robots and make them able to avoid obstacles. The proposed solution is based on forming the robots in circular fashion. A group set of robots consists of multiple groups of robots, each group of robots consists of robots forming a circular shape and each group set is a circular form of robots. The proposed algorithm is concerned with first locating the randomly generated robots in groups and secondly with the swarm robot motion and finally with the swarm obstacle avoidance and swarm reorganization after crossing the obstacle. The proposed algorithm has been simulated with five different obstacles with various numbers of randomly generated robots. The results show that the swarm in the circular form can deal with the obs...