21,000 research outputs found
Visibility maintenance via controlled invariance for leader-follower Dubins-like vehicles
The paper studies the visibility maintenance problem (VMP) for a
leader-follower pair of Dubins-like vehicles with input constraints, and
proposes an original solution based on the notion of controlled invariance. The
nonlinear model describing the relative dynamics of the vehicles is interpreted
as linear uncertain system, with the leader robot acting as an external
disturbance. The VMP is then reformulated as a linear constrained regulation
problem with additive disturbances (DLCRP). Positive D-invariance conditions
for linear uncertain systems with parametric disturbance matrix are introduced
and used to solve the VMP when box bounds on the state, control input and
disturbance are considered. The proposed design procedure is shown to be easily
adaptable to more general working scenarios. Extensive simulation results are
provided to illustrate the theory and show the effectiveness of our approachComment: 17 pages, 24 figures, extended version of the journal paper of the
authors submitted to Automatic
Mobile robotic network deployment for intruder detection and tracking
This thesis investigates the problem of intruder detection and tracking using mobile robotic networks. In the first part of the thesis, we consider the problem of seeking an electromagnetic source using a team of robots that measure the local intensity of the emitted signal. We propose a planner for a team of robots based on Particle Swarm Optimization (PSO) which is a population based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this thesis. Our results are also validated on an experimental testbed.
In the second part, we present a game theoretic framework for visibility-based target tracking in multi-robot teams. A team of observers (pursuers) and a team of targets (evaders) are present in an environment with obstacles. The objective of the team of observers is to track the team of targets for the maximum possible time. While the objective of the team of targets is to escape (break line-of-sight) in the minimum time. We decompose the problem into two layers. At the upper level, each pursuer is allocated to an evader through a minimum cost allocation strategy based on the risk of each evader, thereby, decomposing the agents into multiple single pursuer-single evader pairs. Two decentralized allocation strategies are proposed and implemented in this thesis. At the lower level, each pursuer computes its strategy based on the results of the single pursuer-single evader target-tracking problem. We initially address this problem in an environment containing a semi-infinite obstacle with one corner. The pursuer\u27s optimal tracking strategy is obtained regardless of the evader\u27s strategy using techniques from optimal control theory and differential games. Next, we extend the result to an environment containing multiple polygonal obstacles. We construct a pursuit field to provide a guiding vector for the pursuer which is a weighted sum of several component vectors. The performance of different combinations of component vectors is investigated. Finally, we extend our work to address the case when the obstacles are not polygonal, and the observers have constraints in motion
AI2-THOR: An Interactive 3D Environment for Visual AI
We introduce The House Of inteRactions (THOR), a framework for visual AI
research, available at http://ai2thor.allenai.org. AI2-THOR consists of near
photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes
and interact with objects to perform tasks. AI2-THOR enables research in many
different domains including but not limited to deep reinforcement learning,
imitation learning, learning by interaction, planning, visual question
answering, unsupervised representation learning, object detection and
segmentation, and learning models of cognition. The goal of AI2-THOR is to
facilitate building visually intelligent models and push the research forward
in this domain
- …