One of the main real-world applications of motion planning algorithms is for controlling mobile robots. Applications involve tasks such as mapping physic environments, path planning, and map coverage. These may involve single and multi-agent systems.

Navigation and Localization

We explore methods for navigation in known indoor environments, such as a home or office, that require only inexpensive range sensors such as sonar 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 sub-goals 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.

Multi-robot caravaning

Multi-robot caravanning is loosely defined as the problem of a heterogeneous team of robots visiting specific areas of an environment (waypoints) as a group. We propose a novel solution that requires minimal communication and scales with the number of waypoints and robots. Our approach restricts explicit communication and coordination to occur only when robots reach waypoints, and relies on implicit coordination when moving between a given pair of waypoints. At the heart of our algorithm is the use of leader election to efficiently exploit the unique environmental knowledge available to each robot in order to plan paths for the group, which makes it general enough to work with robots that have heterogeneous representations of the environment.

Battery constrained coverage

We present a behavioral modeling framework and coverage behavior that accounts for a battery constraint. This framework allows a user to model robot teams performing common robotic tasks such as exploration. It uses roadmap-based methods that identify the available paths in potentially complex environments. Also we present a coverage strategy that accounts for the available battery. It allows the agent to calculate a path through an environment that maximizes coverage and allows the agent to get back to a charging location. This eliminates the need to decide when to return to a charging location based on a threshold, as related methods do. It considers the actual path length as opposed to Euclidean distance which is generally used for estimating the energy spent in traversing a path. Different path scoring functions are used to score the path generated.

Publications

Updated: