We present an algorithm to explore an orthogonal polygon using a team of p robots. This algorithm combines ideas from information-theoretic exploration algorithms and computational geometry based exploration algorithms. The algorithm is based on a single-robot polygon exploration algorithm and a tree exploration algorithm. We show that the exploration time of our algorithm is competitive (as a function of p) with respect to the offline optimal exploration algorithm. We discuss how this strategy can be adapted to real-world settings to deal with noisy sensors. In addition to theoretical analysis, we investigate the performance of our algorithm through simulations for multiple robots and experiments with a single robot. / Master of Science / In applications such as disaster recovery, the layout of the environment is generally unknown. Hence, there is a need to explore the environment in order to effectively perform search and rescue. Exploration of unknown environments using a single robot is a well studied problem. We present an algorithm to perform the task with a team of p robots for the specific case of orthogonal polygons, i.e. polygonal environments where each side is aligned with either the X or the Y axis. The algorithm is based on a single-robot polygon exploration algorithm and a tree exploration algorithm. We show that the exploration time of our algorithm is competitive (as a function of p) with respect to the optimal offline algorithm. We then optimize the information gain of the path followed by the robots by allowing local detours in order to decrease the entropy in the map.

The use of multi-robot teams in the Robot Operating System (ROS) has encountered difficulty in advancement because of a lack of effective ways for the robots to communicate. Several attempts towards solving this problem have been made, but these approaches have had trouble with either low fault tolerance or high network load. The Gray Transceiver is an interface and communication protocol for inter-robot communication using ROS. The Gray Transceiver leverages multicasting for reduced network load and increased fault tolerance. Results from simulations, high throughput testing, and live multi-robot evaluations are included. The live mult-robot and simulation evaluations show that it functions properly operating across multiple robots while tolerating faults. The high throughput test shows how the Gray Transceiver operates under high load across a several types of conditions.

<p>Multi-robot systems of autonomous mobile robots offer many benefits but also many challenges. This work addresses collision avoidance of robots solving continuous problems in known environments. The approach to handling collision avoidance is here to enhance a motion planning method for single-robot systems to account for auxiliary robots. A few assumptions are made to put the focus of the work on path planning, rather than on localization.</p><p>A method, based on exact cell decomposition and extended with a few rules, was developed and its consistency was proven. The method is divided into two steps: path planning, which is off-line, and path monitoring, which is on-line. This work also introduces the notion of<em>path obstacle</em>, an essential tool for this kind of path planning with many robots.</p><p>Furthermore, an implementation was performed on a system of omni-directional robots and tested in simulations and experiments. The implementation practices centralized control, by letting an additional computer handle the motion planning, to relieve the robots of strenuous computations.</p><p>A few drawbacks with the method are stressed, and the characteristics of problems that the method is suitable for are presented.</p> / QC 20100705

Inspired by the Rapidly-exploring Random Tree (RRT) data-structure and algorithm for path planning, we introduce an approach for spanning physical space with a group of simple mobile robots. Emphasizing minimalism and using only InfraRed and contact sensors for communication, our position unaware robots physically embody elements of the tree. Although robots are fundamentally constrained in the spatial operations they may perform, we show that the approach -implemented on physical robots- remains consistent with the original data-structure idea. In particular, we show that a generalized form of Voronoi bias is present in the construction of the tree, and that such trees have an approximate space-filling property. We present an analysis of the physical system via sets of coupled stochastic equations: the first being the rate-equation for the transitions made by the robot controllers, and the second to capture the spatial process describing tree formation. We also introduce a class of fixed edge length RRTs called lRRT and show that lRRT s have similar space-filling properties to that of RRTs. We are able to provide an understanding of the control parameters in terms of a process mixing-time and show the dependence of the Voronoi bias on an interference parameter which grows as O*sqrt(N).

This thesis investigates cooperative and intelligent control of autonomous multi-robot systems in a dynamic, unstructured and unknown environment and makes significant original contributions with regard to self-deterministic learning for robot cooperation, evolutionary optimization of robotic actions, improvement of system robustness, vision-based object tracking, and real-time performance. A distributed multi-robot architecture is developed which will facilitate operation of a cooperative multi-robot system in a dynamic and unknown environment in a self-improving, robust, and real-time manner. It is a fully distributed and hierarchical architecture with three levels. By combining several popular AI, soft computing, and control techniques such as learning, planning, reactive paradigm, optimization, and hybrid control, the developed architecture is expected to facilitate effective autonomous operation of cooperative multi-robot systems in a dynamically changing, unknown, and unstructured environment. A machine learning technique is incorporated into the developed multi-robot system for self-deterministic and self-improving cooperation and coping with uncertainties in the environment. A modified Q-learning algorithm termed Sequential Q-learning with Kalman Filtering (SQKF) is developed in the thesis, which can provide fast multi-robot learning. By arranging the robots to learn according to a predefined sequence, modeling the effect of the actions of other robots in the work environment as Gaussian white noise and estimating this noise online with a Kalman filter, the SQKF algorithm seeks to solve several key problems in multi-robot learning. As a part of low-level sensing and control in the proposed multi-robot architecture, a fast computer vision algorithm for color-blob tracking is developed to track multiple moving objects in the environment. By removing the brightness and saturation information in an image and filtering unrelated information based on statistical features and domain knowledge, the algorithm solves the problems of uneven illumination in the environment and improves real-time performance. In order to validate the developed approaches, a Java-based simulation system and a physical multi-robot experimental system are developed to successfully transport an object of interest to a goal location in a dynamic and unknown environment with complex obstacle distribution. The developed approaches in this thesis are implemented in the prototype system and rigorously tested and validated through computer simulation and experimentation.

Constructing and maintaining a formation is critical in applications of cooperative control of multi-agent systems. In this research we address the formation control problem of generating a formation for a group of nonholonomic mobile agents. The formation control scheme proposed in this work is based on a fusion of leader-follower and virtual reference approaches. This scheme gives a formation constraint representation that is independent of the number of agents in the formation and the resulting control algorithm is scalable. One of the important desired features in controller design is that the formation errors defined by formation constraints should be stabilized globally and exponentially by the controller. The proposed controller is based on feedback linearization, and formation errors are shown to be globally exponentially stable in the sense of Lyapunov. Since formation errors are stabilized globally, the proposed controller is applicable to both formation keeping and formation construction problems. As a possible application, the proposed algorithm is implemented in a cooperative ground moving target surveillance scenario. The proposed algorithm enables the determination of the minimal number of agents required for surveillance of a moving target. The number of agents returned by this scheme is not optimal and hence is a conservative solution. However, this is justified by the computational savings the scheme offers.

One of the applications where the use of robots can be beneficial is Wilderness Search and Rescue (WiSAR), which involves the search for a possibly mobile but non-trackable lost person (i.e., the target) in wilderness environments. A mobile target implies that the search area grows continuously and potentially without bound. This fact, combined with the presence of typically rugged, varying terrain and the possibility of inclement weather, poses a considerable challenge to human Search and Rescue (SAR) personnel with respect to the time and effort required to perform the search and the danger entailed to the searchers. Mobile robots can be advantageous in WiSAR due to their ability to provide consistent performance without getting tired and their lower susceptibility to harsh weather conditions compared to humans. Thus, a coordinated team of robots that can assist human SAR personnel by autonomously performing searches in WiSAR scenarios would be of great value. However, to date, a suitable multi-robot coordination methodology for autonomous search that can satisfactorily address the issues relevant to WiSAR is lacking. The objective of this Dissertation is, thus, to develop a methodology that can autonomously coordinate the search strategy of a multi-robot team in wilderness environments to locate a moving target that is neither continuously nor intermittently observed during the search process. Three issues in particular are addressed: (i) target-location prediction, (ii) robot deployment, and (iii) robot-path planning. The corresponding solution approaches devised to address these issues incorporate the influence of varying terrain that may contain a priori known and unknown obstacles, and deal with unique target physiology and psychology as well as found clues left behind by the target. The solution methods for these three tasks work seamlessly together resulting in a tractable MRC methodology for autonomous robotic WiSAR. Comprehensive simulations have been performed that validate the overall proposed methodology. Moreover, the tangible benefits provided by this methodology were further revealed through its comparison with an alternative search method.

One of the applications where the use of robots can be beneficial is Wilderness Search and Rescue (WiSAR), which involves the search for a possibly mobile but non-trackable lost person (i.e., the target) in wilderness environments. A mobile target implies that the search area grows continuously and potentially without bound. This fact, combined with the presence of typically rugged, varying terrain and the possibility of inclement weather, poses a considerable challenge to human Search and Rescue (SAR) personnel with respect to the time and effort required to perform the search and the danger entailed to the searchers. Mobile robots can be advantageous in WiSAR due to their ability to provide consistent performance without getting tired and their lower susceptibility to harsh weather conditions compared to humans. Thus, a coordinated team of robots that can assist human SAR personnel by autonomously performing searches in WiSAR scenarios would be of great value. However, to date, a suitable multi-robot coordination methodology for autonomous search that can satisfactorily address the issues relevant to WiSAR is lacking. The objective of this Dissertation is, thus, to develop a methodology that can autonomously coordinate the search strategy of a multi-robot team in wilderness environments to locate a moving target that is neither continuously nor intermittently observed during the search process. Three issues in particular are addressed: (i) target-location prediction, (ii) robot deployment, and (iii) robot-path planning. The corresponding solution approaches devised to address these issues incorporate the influence of varying terrain that may contain a priori known and unknown obstacles, and deal with unique target physiology and psychology as well as found clues left behind by the target. The solution methods for these three tasks work seamlessly together resulting in a tractable MRC methodology for autonomous robotic WiSAR. Comprehensive simulations have been performed that validate the overall proposed methodology. Moreover, the tangible benefits provided by this methodology were further revealed through its comparison with an alternative search method.

Studies of minimalist multi-robot systems consider multiple robotic agents, each with limited individual capabilities, but with the capacity for self-organization in order to collectively perform coordinated tasks. Object clustering is a widely studied task in which self-organized robots form piles from dispersed objects. Our work considers a variation of an object clustering derived from the influential ant-inspired work of Beckers, Holland and Deneubourg which proposed stigmergy as a design principle for such multi-robot systems. Since puck mechanics contribute to cluster accrual dynamics, we studied a new scenario with square objects because these pucks into clusters differently from cylindrical ones. Although central clusters are usually desired, workspace boundaries can cause perimeter cluster formation to dominate. This research demonstrates successful clustering of square boxes - an especially challenging instance since flat edges exacerbate adhesion to boundaries - using simpler robots than previous published research. Our solution consists of two novel behaviours, Twisting and Digging, which exploit the objects’ geometry to pry boxes free from boundaries. Physical robot experiments illustrate that cooperation between twisters and diggers can succeed in forming a single central cluster. We empirically explored the significance of different divisions of labor by measuring the spatial distribution of robots and the system performance. Data from over 40 hours of physical robot experiments show that different divisions of labor have distinct features, e.g., one is reliable while another is especially efficient.

Multi-robot systems of autonomous mobile robots offer many benefits but also many challenges. This work addresses collision avoidance of robots solving continuous problems in known environments. The approach to handling collision avoidance is here to enhance a motion planning method for single-robot systems to account for auxiliary robots. A few assumptions are made to put the focus of the work on path planning, rather than on localization. A method, based on exact cell decomposition and extended with a few rules, was developed and its consistency was proven. The method is divided into two steps: path planning, which is off-line, and path monitoring, which is on-line. This work also introduces the notion ofpath obstacle, an essential tool for this kind of path planning with many robots. Furthermore, an implementation was performed on a system of omni-directional robots and tested in simulations and experiments. The implementation practices centralized control, by letting an additional computer handle the motion planning, to relieve the robots of strenuous computations. A few drawbacks with the method are stressed, and the characteristics of problems that the method is suitable for are presented. / QC 20100705