7 research outputs found
3D multi-robot patrolling with a two-level coordination strategy
Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks
Optimal Control of Two-Wheeled Mobile Robots for Patrolling Operations
Optimal Control of Two-Wheeled Mobile Robots for Patrolling Operations
Walaaeldin Ahmed Ghadiry,
Concordia University, 2015
This work studies the use of the two-wheeled mobile robots in patrolling operations, and provides the most distance-e�cient as well as time-e�cient trajectories to patrol a given area. Novel formulations in the context of constrained optimization are introduced which can be solved using existing software. The main concept of the
problem is directly related to the well-known Traveling Salesman Problem (TSP) and its variants, where a salesman starts from a base city and visits a number of
other cities with minimum travel distance while satisfying the constraint that each city has to be visited only once. Finally, the salesman returns back to the starting base city after completing the mission. Two di�erent patrolling con�gurations that are related to the TSP and its variants, namely the Single Depot multiple Traveling Salesman Problem (mTSP) and the Multidepot multiple Traveling Salesman Problem (MmTSP) are investigated. Novel algorithms are introduced for the trajectory planning of multiple two-wheeled mobile robots, either with two di�erential motors (which can turn on the spot) or with Dubins-like vehicles. The output trajectories for both types of wheeled robots are investigated by using a model predictive control scheme to ensure their kinematic feasibility for the best monitoring performance. The proposed formulations and algorithms are veri�ed by a series of simulations using e�cient programming and optimization software as well as experimental tests in the lab environment
Trust Modeling in Multi-Robot Patrolling
©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2014 IEEE International Conference on Robotics and Automation (ICRA), 31 May-7 June 2014, Hong Kong, China.On typical multi-robot teams, there is an implicit
assumption that robots can be trusted to effectively perform
assigned tasks. The multi-robot patrolling task is an example of a domain that is particularly sensitive to reliability and
performance of robots. Yet reliable performance of team members may not always be a valid assumption even within
homogeneous teams. For instance, a robot’s performance may
deteriorate over time or a robot may not estimate tasks correctly. Robots that can identify poorly performing team members as performance deteriorates, can dynamically adjust
the task assignment strategy. This paper investigates the use
of an observation based trust model for detecting unreliable robot team members. Robots can reason over this model to
perform dynamic task reassignment to trusted team members.
Experiments were performed in simulation and using a team of indoor robots in a patrolling task to demonstrate both
centralized and decentralized approaches to task reassignment.
The results clearly demonstrate that the use of a trust model
can improve performance in the multi-robot patrolling task