Centralized non-convex model predictive control for cooperative collision avoidance of networked vehicles

Alrifaee, Bassam; Mamaghani, Masoumeh Ghanbarpour; Abel, Dirk

doi:10.1109/isic.2014.6967623

Cited by 36 publications

(29 citation statements)

References 6 publications

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“…Compared to joint-space models, representing the robot by vectors leads to simpler inequality constraints for collision avoidance. Due to a nonconvex nature [21], however, these constraints further complicate convergence properties in both joint-space and vectorbased optimizations. Since the goal is limited to investigate model properties in this paper, we consider including them in future work.…”

Section: Discussionmentioning

confidence: 99%

Modeling Robot Geometries Like Molecules, Application to Fast Multicontact Posture Planning for Humanoids

Faraji

Ijspeert

2018

IEEE Robot. Autom. Lett.

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Abstract-Traditional joint-space models used to describe equations of motion for humanoid robots offer nice properties linked directly to the way these robots are built. However, from a computational point of view and convergence properties, these models are not the fastest when used in planning optimizations. In this paper, inspired by Cartesian coordinates used to model molecular structures, we propose a new modeling technique for humanoid robots. We represent robot segments by vectors and derive equations of motion for the full body. Using this methodology in a complex task of multi-contact posture planning with minimal joint torques, we set up optimization problems and analyze the performance. We demonstrate that compared to joint-space models that get trapped in local minima, the proposed vector-based model offers much faster computational speed and a suboptimal but unique final solution. The underlying principle lies in reducing the nonlinearity and exploiting the sparsity in the problem structure. Apart from the specific case study of posture optimization, these principles can make the proposed technique a promising candidate for many other optimization-based complex tasks in robotics.

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Section: Discussionmentioning

confidence: 99%

Modeling Robot Geometries Like Molecules, Application to Fast Multicontact Posture Planning for Humanoids

Faraji

Ijspeert

2018

IEEE Robot. Autom. Lett.

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“…This allows the separation of general convex sets. Some methods formulate the collision avoidance requirement by mixed-integer constraints [7]. These types of problems are, however, cumbersome to solve in real-time.…”

Section: A Background and Contributionsmentioning

confidence: 99%

Embedded nonlinear model predictive control for obstacle avoidance using PANOC

Sathya

Sopasakis

Parys

et al. 2018

2018 European Control Conference (ECC)

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We employ the proximal averaged Newton-type method for optimal control (PANOC) to solve obstacle avoidance problems in real time. We introduce a novel modeling framework for obstacle avoidance which allows us to easily account for generic, possibly nonconvex, obstacles involving polytopes, ellipsoids, semialgebraic sets and generic sets described by a set of nonlinear inequalities. PANOC is particularly well-suited for embedded applications as it involves simple steps, its implementation comes with a low memory footprint and its fast convergence meets the tight runtime requirements of fast dynamical systems one encounters in modern mechatronics and robotics. The proposed obstacle avoidance scheme is tested on a lab-scale autonomous vehicle.

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“…One typical example of MPC for UAVs is trajectory tracking in formation flight while considering collision avoidance constraints. Examples of a centralized MPC of cooperative control scenarios is given in Shin and Kim (2009) and Alrifaee et al (2014). In Shin and Kim (2009) a leaderfollower mode is used to perform airplane formation flight with collision avoidance using nonlinear MPC.…”

Section: Related Workmentioning

confidence: 99%

“…For this purpose Shin and Kim (2009) compares centralized, sequential decentralized and fully decentralized methods of nonlinear MPC. Alrifaee et al (2014) is presenting a non-convex MPC for cooperative control. Here, the first objective is to tackle collision avoidance while the secondary performance objective is to deal with the quality of the collision-free trajectory.…”

Section: Related Workmentioning

confidence: 99%

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

et al. 2018

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The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver.Keywords Localization and navigation in multi-robot systems · Distributed robotic systems operating on land, sea and air · Multi-robot and multi-vehicle motion coordination · Model predictive control · Sensor constrained control · Unmanned aerial vehicle · Quadrotor

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Centralized non-convex model predictive control for cooperative collision avoidance of networked vehicles

Cited by 36 publications

References 6 publications

Modeling Robot Geometries Like Molecules, Application to Fast Multicontact Posture Planning for Humanoids

Modeling Robot Geometries Like Molecules, Application to Fast Multicontact Posture Planning for Humanoids

Embedded nonlinear model predictive control for obstacle avoidance using PANOC

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

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