Actuator Networks for Navigating an Unmonitored Mobile Robot
Jeremy Ryan Schiff, Anand Kulkarni1, Danny Bazo2, Vincent Duindam3, Ron Alterovitz4, Dezhen Song5 and Ken Goldberg
National Science Foundation CCF-0424422, National Science Foundation 0534848, National Science Foundation 0535218 and AFOSR Human Centric Design Environments for Command and Control Systems: The C2 Wind Tunnel
Building on recent work in sensor-actuator networks and distributed manipulation, we consider the use of pure actuator networks for localization-free robotic navigation. We show how an actuator network can be used to guide an unobserved robot to a desired location in space and introduce an algorithm to calculate optimal actuation patterns for such a network. Sets of actuators are sequentially activated to induce a series of static potential fields that robustly drive the robot from a start to an end location under movement uncertainty. Our algorithm constructs a roadmap with probability-weighted edges based on motion uncertainty models and identifies an actuation pattern that maximizes the probability of successfully guiding the robot to its goal.
Simulations of the algorithm show that an actuator network can robustly guide robots with various uncertainty models through a two-dimensional space. We experiment with additive Gaussian Cartesian motion uncertainty models and additive Gaussian polar models. Motion randomly chosen destinations within the convex hull of a 10-actuator network succeeds with up to 93.4% probability. For n actuators, and m samples per transition edge in our roadmap, our runtime is O(mn6).
Figure 1: An actuator network with sequentially activated actuator triplets (shown as squares) driving a mobile robot toward an end location. The robot is guided by creating locally convex potential fields with minima at waypoints (marked by xs).
Figure 2: Example of a simulation of the actuator-networks algorithm with 8 actuators (denoted by circles) and incenters formed by sets of three actuators (denoted by *s). The actuators are placed randomly on the border of a square workspace, and work in sets of three to exert forces on the robot to move it from one incenter to another. Therefore, the incenters of all possible triangles between them form vertices in a roadmap with edges containing the probability of successful transition by activation of an actuator triplet. The left hand figure illustrates the placement of actuators, triangles of three actuators, and incenters. The right hand figure shows the roadmap and an example path (thick line) through the network.
- J. Schiff, A. Kulkarni, D. Bazo, V. Duindam, R. Alterovitz, D. Song, and K. Goldberg, "Actuator Networks for Navigating an Unmonitored Mobile Robot," IEEE Conference on Automation Science and Engineering (CASE), Washington, DC, August 2008.
1IEOR PhD Student
5Texas A&M Professor