Coordinated task manipulation by nonholonomic mobile robots

Coordinated task manipulation by a group of autonomous mobile robots has received signicant research effort in the last decade. Previous studies in the area revealed that one of the main problems in the area is to avoid the collisions of the robots with obstacles as well as with other members of the group. Another problem is to come up with a model for successful task manipulation. Signicant research effort has accumulated on the denition of forces to generate reference trajectories for each autonomous mobile robots engaged in coordinated behavior. If the mobile robots are nonholonomic, this approach fails to guarantee successful manipulation of the task since the so-generated reference trajectories might not satisfy the nonholonomic constraint. In this work, we introduce a novel coordinated task manipulation model inclusive of an online collision avoidance algorithm. The reference trajectory for each autonomous nonholonomic mobile robot is generated online in terms of linear and angular velocity references for the robot; hence these references automatically satisfy the nonholonomic constraint. The generated reference velocities inevitably depend on the nature of the specied coordinated task. Several coordinated task examples, on the basis of a generic task, have been presented and the proposed model is veried through simulations

Multi-robot team formation control in the GUARDIANS project

Purpose The GUARDIANS multi-robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots guide and accompany the firefighters on site whilst indicating possible obstacles and the locations of danger and maintaining communications links. Design/methodology/approach In order to fulfill the aforementioned tasks the robots need to exhibit certain behaviours. Among the basic behaviours are capabilities to stay together as a group, that is, generate a formation and navigate while keeping this formation. The control model used to generate these behaviours is based on the so-called social potential field framework, which we adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots. Findings The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus the application has forced us to redefine the concept of a formation. Using the graph-theoretic terminology, we can say that a formation may be stretched out as a path or be compact as a star or wheel. We have implemented the developed behaviours in simulation environments as well as on real ERA-MOBI robots commonly referred to as Erratics. We discuss advantages and shortcomings of our model, based on the simulations as well as on the implementation with a team of Erratics.</p

Bearing-only formation control with auxiliary distance measurements, leaders, and collision avoidance

We address the controller synthesis problem for distributed formation control. Our solution requires only relative bearing measurements (as opposed to full translations), and is based on the exact gradient of a Lyapunov function with only global minimizers (independently from the formation topology). These properties allow a simple proof of global asymptotic convergence, and extensions for including distance measurements, leaders and collision avoidance. We validate our approach through simulations and comparison with other stateof-the-art algorithms.ARL grant W911NF-08-2-0004, ARO grant W911NF-13-1-0350, ONR grants N00014-07-1-0829, N00014-14-1-0510, N00014-15-1-2115, NSF grant IIS-1426840, CNS-1521617 and United Technologies

Collision avoidance and dynamic modeling for wheeled mobile robots and industrial manipulators

Collision Avoidance and Dynamic Modeling are key topics for researchers dealing with mobile and industrial robotics. A wide variety of algorithms, approaches and methodologies have been exploited, designed or adapted to tackle the problems of finding safe trajectories for mobile robots and industrial manipulators, and of calculating reliable dynamics models able to capture expected and possible also unexpected behaviors of robots. The knowledge of these two aspects and their potential is important to ensure the efficient and correct functioning of Industry 4.0 plants such as automated warehouses, autonomous surveillance systems and assembly lines. Collision avoidance is a crucial aspect to improve automation and safety, and to solve the problem of planning collision-free trajectories in systems composed of multiple autonomous agents such as unmanned mobile robots and manipulators with several degrees of freedom. A rigorous and accurate model explaining the dynamics of robots, is necessary to tackle tasks such as simulation, torque estimation, reduction of mechanical vibrations and design of control law

Robust Distributed Formation Control of UAVs with Higher-Order Dynamics

In this thesis, we introduce distributed formation control strategies to reach an intended linear formation for agents with a diverse array of dynamics. The suggested technique is distributed entirely, does not include inter-agent cooperation or a barrier of orientation, and can be applied using relative location information gained by agents in their local cooperation frames. We illustrate how the control optimized for agents with the simpler dynamic model, i.e., the dynamics of the single integrator, can be expanded to holonomic agents with higher dynamics such as quadrotors and non-holonomic agents such as unicycles and cars. Our suggested approach makes feedback saturations, unmodelled dynamics, and switches stable in the sensing topology. We also indicate that the control is relaxed as agents will travel along with a rotated and scaled control direction without disrupting the convergence to the desired formation. We can implement this observation to design a distributed strategy for preventing collisions. In simulations, we explain the suggested solution and further introduce a distributed robotic framework to experimentally validate the technique. Our experimental platform is made up of off-the-shelf devices that can be used to evaluate other multi-agent algorithms and verify them