Dynamic Obstacles Avoidance Using Nonlinear Model Predictive Control

Sani, Mukhtar; Robu, Bogdan; Hably, Ahmad

doi:10.1109/iecon48115.2021.9589658

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…The problem setup is for the vehicles to move from the initial state to the final state without colliding with each other, while simultaneously avoiding other stationary and moving obstacles. A method to avoid obstacles by giving constraints to the MPC has also been proposed (12)∼ (14) . However, these methods incorporate the prohibited area into the constraint, and if the plant moves into the prohibited area, it may become unstable.…”

Section: Figmentioning

confidence: 99%

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Obstacle Avoidance during Teleoperation by Model Predictive Control with Time-varying Delay

Hatori

Uchimura

2023

IEEJ Journal IA

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This paper proposes a method for autonomously replanning the optimal trajectory of a teleoperated mobile robot to avoid obstacles during teleoperation with time-varying delay. Model predictive control (MPC) is applied to compensate for the time-varying delay to ensure the mobile robot autonomously avoids obstacles. An operator operates the robot while viewing the predicted model. The state of the predicted model is sequentially sent to the teleoperation system by the local system including the predicted model. This earlier received predicted state is used as a reference trajectory for MPC, and linear interpolation is performed on the lost data of the reference trajectory. When an obstacle is encountered, the path to avoid it is replanned. The constraints for obstacle avoidance, including the cost of the distance between the obstacle and the mobile robot are relaxed, making obstacle avoidance control robust to modeling errors. The mobile robot then follows a reference trajectory while maintaining the optimal distance to the obstacle. Simulations and experiments are conducted to evaluate the performance of the proposed method in comparison with conventional methods.

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Section: Figmentioning

confidence: 99%

“…Each weight matrices of the cost function were given as follows: (10,10), Q ob3 = 200, P ob1 = 50Q ob1 , P ob3 = 50Q ob3 . In addition, d desired = 0.7 m, d rep = 1.0 m. In the conventional method based on the reference (12) , ( 16) was modified as (27).…”

Section: Simulation Setupmentioning

confidence: 99%

Obstacle Avoidance during Teleoperation by Model Predictive Control with Time-varying Delay

Hatori

Uchimura

2023

IEEJ Journal IA

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“…Different from the work of [16], our MPC algorithm does not require additional computation for predicting the obstacle's speed, which could lead to higher computational time and wrong prediction in the case of "tricky" obstacles. This method, initially developed in our previous [17], incorporates obstacle avoidance as a constraint while solving the optimal control problem. The performance of the discretization methods as well as the effect of prediction horizon on the computational time is also studied.…”

Section: Introductionmentioning

confidence: 99%

Real-time Dynamic Obstacle Avoidance For A Non-holonomic Mobile Robot

Sani,

Hably,

Robu

et al. 2023

2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)

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This paper presents the experimental validation of a real-time nonlinear model predictive control algorithm developed to deal with dynamic and static obstacle avoidance for a non-holonomic wheeled mobile robot. Unlike state-of-theart techniques, the speed of the dynamic obstacle is unknown to the controller. The developed controller autonomously drives the robot away from the obstacle by calculating the minimal distance from which the avoidance maneuver starts. Several real-time experimental results for stabilizing a mobile robot in presence of dynamic and static obstacles are presented. This work has been partially supported by ROBOTEX 2.0 (Grants ROBO-TEX ANR-10-EQPX-44-01 and TIRREX ANR-21-ESRE-0015) funded by the French program Investissements d'avenir.

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Experimental Assessment of a Vision-Based Obstacle Avoidance Strategy for Robot Manipulators: Off-line Trajectory Planning and On-line Motion Control

Scoccia,

Ubezio,

Palmieri

et al. 2024

J Intell Robot Syst

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Human-Robot Interaction is an increasingly important topic in both research and industry fields. Since human safety must be always guaranteed and accidental contact with the operator avoided, it is necessary to investigate real-time obstacle avoidance strategies. The transfer from simulation environments, where algorithms are tested, to the real world is challenging from different points of view, e.g., the continuous tracking of the obstacle and the configuration of different manipulators. In this paper, the authors describe the implementation of a collision avoidance strategy based on the potential field method for off-line trajectory planning and on-line motion control, paired with the Motion Capture system Optitrack PrimeX 22 for obstacle tracking. Several experiments show the performance of the proposed strategy in the case of a fixed and dynamic obstacle, disturbing the robot’s trajectory from multiple directions. Two different avoidance modalities are adapted and tested for both standard and redundant robot manipulators. The results show the possibility of safely implementing the proposed avoidance strategy on real systems.

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Dynamic Obstacles Avoidance Using Nonlinear Model Predictive Control

Cited by 6 publications

References 33 publications

Obstacle Avoidance during Teleoperation by Model Predictive Control with Time-varying Delay

Obstacle Avoidance during Teleoperation by Model Predictive Control with Time-varying Delay

Real-time Dynamic Obstacle Avoidance For A Non-holonomic Mobile Robot

Experimental Assessment of a Vision-Based Obstacle Avoidance Strategy for Robot Manipulators: Off-line Trajectory Planning and On-line Motion Control

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