Model Predictive Formation Control Using Branch-and-Bound Compatible With Collision Avoidance Problems

Fukushima, Hiroaki; Kon, Kenichi; Matsuno, Fumitoshi

doi:10.1109/tro.2013.2262751

Cited by 63 publications

(33 citation statements)

References 33 publications

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“…Many reactive approaches are based on the concept of velocity obstacle (VO) [11], whereas, optimization based approaches avoid obstacles by embedding collision constraints (like VO) within cost function or as hard constraints in optimization. Recently, a mixed integer quadratic program (MIQP) in the form of a centralized non-linear model predictive control (NMPC) [12] has been proposed for dynamic obstacle avoidance, where feedback linearization is coupled with a variant of the branch and bound algorithm. However, this approach suffers with agent scale-up, since increase in binary variables of MIQP has an associated exponential complexity.…”

Section: State-of-the-artmentioning

confidence: 99%

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

Tallamraju

Rajappa

Black

et al. 2018

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these nonlinear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. Video of simulation studies: https://youtu.be/umkdm82Tt0M arXiv:1805.09633v1 [cs.RO]

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Section: State-of-the-artmentioning

confidence: 99%

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

Tallamraju

Rajappa

Black

et al. 2018

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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“…Examples of the use of decentralized MPC is given by Shin andKim (2009), Fukushima et al (2013), Turpin et al (2012) and Bemporad and Rocchi (2011). The considered scenario in Fukushima et al (2013) is formation control of a multi-vehicle system with collision avoidance and input constraints. For this purpose a feedback linearization controller is integrated with MPC.…”

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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“…The MPC pattern is of advantage in dealing with the navigation problems in dynamic environment. 27 Some of the MPC problems are solved by standard mathematical programming methods while some MPC problems are solved by tree search method. 28,29 As the unmodeled dynamics are difficult to describe in an analytical form, we propose to approximate the unmodeled dynamics by NNs.…”

Section: Related Workmentioning

confidence: 99%

Decentralized cooperative unmanned aerial vehicles conflict resolution by neural network-based tree search method

Yang

Yin

Cheng

et al. 2016

International Journal of Advanced Robotic Systems

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In this article, a tree search algorithm is proposed to find the near optimal conflict avoidance solutions for unmanned aerial vehicles. In the dynamic environment, the unmodeled elements, such as wind, would make UAVs deviate from nominal traces. It brings about difficulties for conflict detection and resolution. The back propagation neural networks are utilized to approximate the unmodeled dynamics of the environment. To satisfy the online planning requirement, the search length of the tree search algorithm would be limited. Therefore, the algorithm may not be able to reach the goal states in search process. The midterm reward function for assessing each node is devised, with consideration given to two factors, namely, the safe separation requirement and the mission of each unmanned aerial vehicle. The simulation examples and the comparisons with previous approaches are provided to illustrate the smooth and convincing behaviours of the proposed algorithm.

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Model Predictive Formation Control Using Branch-and-Bound Compatible With Collision Avoidance Problems

Cited by 63 publications

References 33 publications

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

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

Decentralized cooperative unmanned aerial vehicles conflict resolution by neural network-based tree search method

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