The proliferation of affordable mobile devices with processing and sensing capabilities, together with the rapid growth in ubiquitous network connectivity, herald an era of Mobiscopes; networked sensing applications that rely on multiple mobile sensors to accomplish global tasks. These distributed sensing systems extend the model of traditional sensor networks, introducing challenges in data management, data integrity, privacy, and network system design. While several applications that fit the above description exist in prior literature, they provide tailored one-time solutions to what essentially is the same set of problems. It is time to work towards a general architecture that identifies common challenges and provides a generalizable methodology for the design of future Mobiscopes. Towards that end, this paper surveys a variety of current and emerging mobile, networked, sensing applications; articulates their common challenges; and provides architectural guidelines and design directions for this important

Abstract — This paper describes an on-line algorithm for multirobot simultaneous localization and mapping (SLAM). We take as our starting point the single-robot Rao-Blackwellized particle filter described in [1] and make two key generalizations. First, we extend the particle filter to handle multi-robot SLAM problems in which the initial pose of the robots is known (such as occurs when all robots start from the same location). Second, we introduce an approximation to solve the more general problem in which the initial pose of robots is not known a priori (such as occurs when the robots start from widely separated locations). In this latter case, we assume that pairs of robots will eventually ‘bump into’ one another, thereby determining their relative pose. We use this relative pose to initialize the filter, and combine the subsequent (and prior) observations from both robots into a common map. This algorithm has been experimentally validated using data from a team of four robots equipped with odometry and scanning laser range-finders. I.

Stage is a C++ software library that simulates multiple mobile robots. Stage version 2, as the simulation backend for the Player/Stage system, may be the most commonly used robot simulator in research and university teaching today. Development of Stage version 3 has focused on improving scalability, usability, and portability. This paper examines Stage’s scalability. We propose a simple benchmark for multi-robot simulator performance, and present results for Stage. Run time is shown to scale approximately linearly with population size up to 100,000 robots. For example, Stage simulates 1 simple robot at around 1,000 times faster than real time, and 1,000 simple robots at around real time. These results suggest that Stage may be useful for swarm robotics researchers who would otherwise use custom simulators, with their attendant disadvantages in terms of code reuse and transparency.

In this paper, we report on the integration challenges of the various component technologies developed towards the establishment of a framework for deploying an adaptive system of heterogeneous robots for urban surveillance. In our integrated experiment and demonstration, aerial robots generate maps that are used to design navigation controllers and plan missions for the team. A team of ground robots constructs a radio signal strength map that is used as an aid for planning missions. Multiple robots establish a mobile, ad-hoc communication network that is aware of the radio signal strength between nodes and can adapt to changing conditions to maintain connectivity. Finally, the team of aerial and ground robots is able to monitor a small village, and search for and localize human targets by the color of the uniform, while ensuring that the information from the team is available to a remotely located human operator. The key component technologies and contributions include (a) mission specification and planning software; (b) exploration and mapping of radio signal strengths in an urban environment; (c) programming abstractions and composition of controllers for multi-robot deployment; (d) cooperative control strategies for search, identification, and localization of targets; and (e) three-dimensional mapping in an urban setting. 1

Summary. In urban search and rescue scenarios, human first responders risk their lives as they routinely encounter hazardous environments. A team of robots, equipped with various sensors, deployed in such an environment can be used to track emergency personnel such as firefighters, reducing the risk to human life. This paper explores techniques for tracking a mobile target and coordinating a team of robots, equipped with range-only sensors, through smoke-filled, high-temperature environments. The particular strengths of our tracking and cooperative control algorithms are identified through a set of simulated examples. 1

Abstract — When an emergency occurs within a building it is critical to explore the area as fast as possible in order to find victims and identify hazards. We propose Brick&Mortar, an algorithm for the autonomous exploration of unknown terrains by a team of mobile agents. Because of the unreliability and short range of wireless communications in indoor environment we suggest that agents communicate indirectly with each other by tagging the environment. Agents have no prior knowledge of the map, but they are able to coordinate in order to explore a variety of terrains with different topological features. In our experimental evaluation, we show that Brick&Mortar significantly outperforms the competing algorithms, namely Ants and Multiple Depth First Search, in terms of exploration time. The observed performance benefits suggest that our algorithm is suitable for safety-critical applications that require rapid area coverage for real-time event detection and response. I.

Abstract — This paper presents an adaptive causal model method (adaptive CMM) for fault diagnosis and recovery in complex multi-robot teams. We claim that a causal model approach is effective for anticipating and recovering from many types of robot team errors, presenting extensive experimental results to support this claim. To our knowledge, these results show the first, full implementation of a CMM on a large multi-robot team. However, because of the significant number of possible failure modes in a complex multi-robot application, and the difficulty in anticipating all possible failures in advance, our empirical results show that one cannot guarantee the generation of a complete a priori causal model that identifies and specifies all faults that may occur in the system. Instead, an adaptive method is needed to enable the robot team to use its experience to update and extend its causal model to enable the team, over time, to better recover from faults when they occur. We present our case-based learning approach, called LeaF (for Learning-based Fault diagnosis), that enables robot team members to adapt their causal models, thereby improving their ability to diagnose and recover from these faults over time. I.

Abstract—Mobile sensors are useful in many environments because they can move to increase the sensing coverage. In this paper, we present a mobile sensor prototype in which the Mica2 sensor node is used to control the movement of the robot built with commercial off-the-shelf (COTS) components. We use a sensor relocation application to demonstrate the feasibility of our design. In the sensor relocation application, after a sensor node failure creates a coverage hole, a mobile sensor node is relocated to cover the hole in a timely and energy-efficient way. We present a distributed sensor relocation algorithm and provide novel solutions to implement this algorithm in our mobile sensor platform. Experimental results show that our relocation algorithm can reduce the sensor relocation time and balance the energy consumption of the mobile nodes.

Abstract: In this paper, we present a novel approach to the area coverage problem by using a team of heterogeneous mobile robots. In our method, a parent robot is assumed to possess state-of-the-art sensors and sufficient computation power to establish robust localization and navigation. A large number of inexpensive and small child robots possess only restricted sensing and computation capabilities. They can only fulfill a certain task, e.g., floor-cleaning, but can be teleoperated in line-of-sight of the parent robot. To exploit the advantages of both types of robots, the team cooperatively covers the area in an efficient way. In contrast to other approaches and due to the cooperation of the robots, we can relax the requirement that every robot must be able to self-localize and robustly navigate to take part in efficient multi-robot coverage. Simulation results are presented in which our approach was tested intensively.

The Automated Transport and Retrieval System (ATRS) represents a technology-based alternative to van conversions for automobile drivers in wheelchairs. Rather than requiring dramatic, permanent, and expensive modifications to the host vehicle, ATRS employs robotics and automation technologies and can be integrated noninvasively into a standard minivan or sport utility vehicle. At the core of ATRS is a “smart ” wheelchair system that autonomously navigates between the driver’s position and a powered lift at the rear of the vehicle, eliminating the need for an attendant. From an automation perspective, autonomously docking the wheelchair onto the lift platform presented the most significant technical challenge due to limited clearance between the chair wheels and the lift platform rails. To solve the docking task, we employed a light detection and ranging (LIDAR)–based approach for wheelchair localization coupled with a hybrid motion controller design. Extensive testing from the localization subsystem to the complete ATRS was conducted under representative usage conditions. This included 3 days of public demonstrations indoors at the World Congress on Disabilities, where potential end users were able to evaluate the system. In this environment, ATRS performed more than 300 consecutive cycles without failure. During 2 days of outdoor reliability testing, 97.5 % docking reliability was observed. The system is scheduled to enter the commercial market in 2008.