We consider the localization problem for a system of mobile robots using inexpensive range sensors. Among many issues for multi-robot systems, two problems are identified and formally defined. The first problem is sensing ranges from all robots as quickly as possible while avoiding sensor cross-talk, and the second problem is to localize a multi-robot system using a minimal number of range sensings. We show that both these problems are NP-complete, and we propose an approximate method for the multi-robot localization problem that takes advantage of the robots pose uncertainty information. Simulation results show the effectiveness of our method for localizing multiple robots.

We present methods for extracting optimal paths from motion planning roadmaps. Our system enables any combination of optimization criteria, such as collision detection, kinematic/dynamic constraints, or minimum clearance, and relaxed definitions of the goal state, to be used when selecting paths from roadmaps. Our algorithm is an augmented version of Dijkstra's shortest path algorithm which allows edge weights to be defined relative to the current path. We present simulation results maximizing minimum path clearance, minimizing localization effort, and enforcing kinematic/dynamic constraints.

This paper presents methods for navigating and localizing mobile robots in a known indoor environment. We introduce a restricted visibility concept called a scannable sector that can aid many existing navigation and localization algorithms. The scannable sectors are based on the physical characteristics of the environment and the limitations of the localization sensors used. We describe a complete navigation system that includes a scannable sector based localizer, sonar sensors, and a probabilistic roadmap path planner. Simulation and hardware results using a real robot with sonar sensors show the potential of our approach.

In this paper, we describe a method for navigation and localization of a mobile robot using sonar sensors in an indoor environment. This is an enhanced version of our previous method (2001) which assumed a perfectly known environment and perfect sensor data. We remove these assumptions by computing a roadmap and selecting geometric features of the environment for localization that are robust in terms of known sensor limitations and uncertainty. In particular, our roadmap-based navigator and localizer have been redesigned to work cooperatively. To identify geometric features, a simple sensor data filter is designed. We present simulation and hardware experiments for a robot equipped with inexpensive sonar sensors in a real environment.

We describe a method for navigation in a known indoor environment, such as a home or office, that requires only inexpensive range sensors. Our framework includes a high-level planner which integrates and coordinates path planning and localization modules with the aid of a module for computing regions which are expected, with high probability, to contain the robot at any given time. The localization method is based on simple geometric properties of the environment which are computed during a preprocessing stage. The roadmap-based path planner enables one to select routes, and subgoals along those routes, that will facilitate localization and other optimization criteria. In addition, our framework enables one to quickly plan new routes, dynamically, based on the current position as computed by intermediate localization operations. We present simulation and hardware experimental results that illustrate the practicality and potential of our approach.