8,214 research outputs found
Robotic swarm control from spatio-temporal specifications
In this paper, we study the problem of controlling a two-dimensional robotic swarm with the purpose of achieving high level and complex spatio-temporal patterns. We use a rich spatio-temporal logic that is capable of describing a wide range of time varying and complex spatial configurations, and develop a method to encode such formal specifications as a set of mixed integer linear constraints, which are incorporated into a mixed integer linear programming problem. We plan trajectories for each individual robot such that the whole swarm satisfies the spatio-temporal requirements, while optimizing total robot movement and/or a metric that shows how strongly the swarm trajectory resembles given spatio-temporal behaviors. An illustrative case study is included.This work was partially supported by the National Science Foundation under grants NRI-1426907 and CMMI-1400167. (NRI-1426907 - National Science Foundation; CMMI-1400167 - National Science Foundation
On the Minimal Revision Problem of Specification Automata
As robots are being integrated into our daily lives, it becomes necessary to
provide guarantees on the safe and provably correct operation. Such guarantees
can be provided using automata theoretic task and mission planning where the
requirements are expressed as temporal logic specifications. However, in
real-life scenarios, it is to be expected that not all user task requirements
can be realized by the robot. In such cases, the robot must provide feedback to
the user on why it cannot accomplish a given task. Moreover, the robot should
indicate what tasks it can accomplish which are as "close" as possible to the
initial user intent. This paper establishes that the latter problem, which is
referred to as the minimal specification revision problem, is NP complete. A
heuristic algorithm is presented that can compute good approximations to the
Minimal Revision Problem (MRP) in polynomial time. The experimental study of
the algorithm demonstrates that in most problem instances the heuristic
algorithm actually returns the optimal solution. Finally, some cases where the
algorithm does not return the optimal solution are presented.Comment: 23 pages, 16 figures, 2 tables, International Joural of Robotics
Research 2014 Major Revision (submitted
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