The Cost of Reality: Effects of Real-World Factors on Multi-Robot Search

Designing algorithms for multi-robot systems can be a complex and difficult process: the cost of such systems can be very high, collecting experimental data can be time consuming, and individual robots may malfunction, invalidating experiments. These constraints make it very tempting to work using high-level abstractions of the robots and their environment. While these high-level models can be useful for initial design, it is important to verify techniques in more realistic scenarios that include real-world effects that may have been ignored in the abstractions. In this paper, we take a simple, coordinated, multi-robot search algorithm and illustrate problems that it encounters in environments which incorporate real-world factors, such as probabilistic target detection and positional noise. We compare the performance to that of several simple randomized approaches, which are better able to deal with these constraints

Comparison of algorithms for distributed space exploration in a simulated environment

Space exploration algorithms aim to discover as much unknown space as possible as efficiently as possible in the shortest possible time. To achieve this goal, we use distributed algorithms, implemented on multi-agent systems. In this work, we explore, which of the algorithms can efficiently explore space in a simulated environment Gridland. Since Gridland, in it's original release, was not meant for simulating space exploration, we had to make some modifications and enable movement history and action tracking for a multi-agent system with the purpose of algorithm efficiency analysis. A random agent was implemented for reference and compared with an algorithm, that represents a group of so called "pseudo-random" algorithms, and a particle swarm based algorithm. We show that pseudo-random algorithms are much better than random algorithms, despite their simplicity. Algorithm RDPSO, based on particle swarm optimisation, proved to be efficient, despite not being the fastest

Performance analysis of a random search algorithm for distributed autonomous mobile robots

Master'sMASTER OF ENGINEERIN

Guidance and search algorithms for mobile robots: application and analysis within the context of urban search and rescue

Urban Search and Rescue is a dangerous task for rescue workers and for this reason the use of mobile robots to carry out the search of the environment is becoming common place. These robots are remotely operated and the search is carried out by the robot operator. This work proposes that common search algorithms can be used to guide a single autonomous mobile robot in a search of an environment and locate survivors within the environment. This work then goes on to propose that multiple robots, guided by the same search algorithms, will carry out this task in a quicker time. The work presented is split into three distinct parts. The first is the development of a nonlinear mathematical model for a mobile robot. The model developed is validated against a physical system. A suitable navigation and control system is required to direct the robot to a target point within an environment. This is the second part of this work. The final part of this work presents the search algorithms used. The search algorithms generate the target points which allow the robot to search the environment. These algorithms are based on traditional and modern search algorithms that will enable a single mobile robot to search an area autonomously. The best performing algorithms from the single robot case are then adapted to a multi robot case. The mathematical model presented in the thesis describes the dynamics and kinematics of a four wheeled mobile ground based robot. The model is developed to allow the design and testing of control algorithms offline. With the model and accompanying simulation the search algorithms can be quickly and repeatedly tested without practical installation. The mathematical model is used as the basis of design for the manoeuvring control algorithm and the search algorithms. This design process is based on simulation studies. In the first instance the control methods investigated are Proportional-Integral-Derivative, Pole Placement and Sliding Mode. Each method is compared using the tracking error, the steady state error, the rise time, the charge drawn from the battery and the ability to control the robot through a simple motion. Obstacle avoidance is also covered as part of the manoeuvring control algorithm. The final aspect investigated is the search algorithms. The following search algorithms are investigated, Lawnmower, Random, HillClimbing, Simulated Annealing and Genetic Algorithms. Variations on these algorithms are also investigated. The variations are based on Tabu Search. Each of the algorithms is investigated in a single robot case with the best performing investigated within a multi robot case. A comparison between the different methods is made based on the percentage of the area covered within the time available, the number of targets located and the time taken to locate targets. It is shown that in the single robot case the best performing algorithms have high random elements and some structure to selecting points. Within the multi robot case it is shown that some algorithms work well and others do not. It is also shown that the useable number of robots is dependent on the size of the environment. This thesis concludes with a discussion on the best control and search algorithms, as indicated by the results, for guiding single and multiple autonomous mobile robots. The advantages of the methods are presented, as are the issues with using the methods stated. Suggestions for further work are also presented

Self–organised multi agent system for search and rescue operations

Autonomous multi-agent systems perform inadequately in time critical missions, while they tend to explore exhaustively each location of the field in one phase with out selecting the pertinent strategy. This research aims to solve this problem by introducing a hierarchy of exploration strategies. Agents explore an unknown search terrain with complex topology in multiple predefined stages by performing pertinent strategies depending on their previous observations. Exploration inside unknown, cluttered, and confined environments is one of the main challenges for search and rescue robots inside collapsed buildings. In this regard we introduce our novel exploration algorithm for multi–agent system, that is able to perform a fast, fair, and thorough search as well as solving the multi–agent traffic congestion. Our simulations have been performed on different test environments in which the complexity of the search field has been defined by fractal dimension of Brownian movements. The exploration stages are depicted as defined arenas of National Institute of Standard and Technology (NIST). NIST introduced three scenarios of progressive difficulty: yellow, orange, and red. The main concentration of this research is on the red arena with the least structure and most challenging parts to robot nimbleness

Cooperative search algorithm for distributed autonomous robots

2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)1394-39