Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups

Rösmann, Christoph; Makarow, Artemi; Bertram, Torsten

doi:10.23919/ecc54610.2021.9654872

Cited by 32 publications

(18 citation statements)

References 22 publications

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“…To better test the performance of our proposed method for static obstacle avoidance, we compare the proposed method with DWA (Fox et al , 1997), MPC (Rosmann et al , 2021) and the local model predictive contouring control (LMPCC) (Brito et al , 2019) algorithms in the same scene. As shown in Figure 8, we place three obstacles in the corridor and a waypoint at the corner.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The robot can also complete path planning and obstacle avoidance well through experimental tests in large scenarios or by increasing the distance between obstacles, as shown in Figure 7. To better test the performance of our proposed method for static obstacle avoidance, we compare the proposed method with DWA (Fox et al, 1997), MPC (Rosmann et al, 2021) and the local model predictive contouring control (LMPCC) (Brito et al, 2019) algorithms in the same scene. As shown in Figure 8, we place three obstacles in the corridor and a waypoint at the corner.…”

Section: Static Collision Avoidancementioning

confidence: 99%

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A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

Zhou

Feng²,

Chou³

2022

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Purpose Wheeled mobile robots (WMR) are the most widely used robots. Avoiding obstacles in unstructured environments, especially dynamic obstacles such as pedestrians, is a serious challenge for WMR. This paper aims to present a hybrid obstacle avoidance method that combines an informed-rapidly exploring random tree* algorithm with a three-dimensional (3D)-object detection approach and model prediction controller (MPC) to conduct obstacle perception, collision-free path planning and obstacle avoidance for WMR in unstructured environments. Design/methodology/approach Given a reference orientation and speed, the hybrid method uses parametric ellipses to represent obstacle expansion boundaries based on the 3D target detection results, and a collision-free reference path is planned. Then, the authors build on a model predictive control for tracking the collision-free reference path by incorporating the distance between the robot and obstacles. The proposed framework is a mapless method for WMR. Findings The authors present experimental results with a mobile robot for obstacle avoidance in indoor environments crowded with obstacles, such as chairs and pedestrians. The results show that the proposed hybrid obstacle avoidance method can satisfy the application requirements of mobile robots in unstructured environments. Originality/value In this study, the parameter ellipse is used to represent the area occupied by the obstacle, which takes the velocity as the parameter. Therefore, the motion direction and position of dynamic obstacles can be considered in the planning stage, which enhances the success rate of obstacle avoidance. In addition, the distance between the obstacle and robot is increased in the MPC optimization function to ensure a safe distance between the robot and the obstacle.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Static Collision Avoidancementioning

confidence: 99%

A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

Zhou

Feng²,

Chou³

2022

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“…In addition to the previous research paper, [15] represents a novel online motion planning approach based on nonlinear MPC. Using non-euclidean rotation groups, the authors have formulated an optimization problem that solves local planning via optimal control.…”

Section: B Model Predictive Controlmentioning

confidence: 99%

“…Due to its ability to tackle optimization problems on a finite time-horizon, MPC has been chosen as a method for trajectory generation. The approach was inspired by [15], q_nearest q_dead_end Fig. 4: If a parent-node is unable to spawn child-nodes (left), that parent is marked as a dead-end node (right).…”

Section: B Local Plannermentioning

confidence: 99%

Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw

Bojadžić¹,

Kunze²,

Osmanković³

et al. 2021

Preprint

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This paper presents a novel hierarchical motion planning approach based on Rapidly-Exploring Random Trees (RRT) for global planning and Model Predictive Control (MPC) for local planning. The approach targets a three-wheeled cycle rickshaw (trishaw) used for autonomous urban transportation in shared spaces. Due to the nature of the vehicle, the algorithms had to be adapted in order to adhere to non-holonomic kinematic constraints using the Kinematic Single-Track Model.The vehicle is designed to offer transportation for people and goods in shared environments such as roads, sidewalks, bicycle lanes but also open spaces that are often occupied by other traffic participants. Therefore, the algorithm presented in this paper needs to anticipate and avoid dynamic obstacles, such as pedestrians or bicycles, but also be fast enough in order to work in real-time so that it can adapt to changes in the environment. Our approach uses an RRT variant for global planning that has been modified for single-track kinematics and improved by exploiting dead-end nodes. This allows us to compute global paths in unstructured environments very fast. In a second step, our MPC-based local planner makes use of the global path to compute the vehicle's trajectory while incorporating dynamic obstacles such as pedestrians and other road users.Our approach has shown to work both in simulation as well as first real-life tests and can be easily extended for more sophisticated behaviors.

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“…To address these limitations, this paper proposes a distributed motion planning method that combines the kinematic constraints of robots, static environment constraints, and the predicted behaviors of neighboring robots to achieve collaborative motion planning for multiple robots while ensuring real-time performance. The distributed method is based on Model Predictive Contouring Control (MPCC), which is proposed for real-time motion planning of a single mobile robot [19,20] . The MPCC allows separating the tracking accuracy and productivity to reach a good trade-off between these two objectives.…”

Section: Introductionmentioning

confidence: 99%

Distributed Model Predictive Contouring Control for Real-Time Multi-Robot Motion Planning

Xin

Zhang

et al. 2022

Complex Syst. Model. Simul.

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Existing motion planning algorithms for multi-robot systems must be improved to address poor coordination and increase low real-time performance. This paper proposes a new distributed real-time motion planning method for a multi-robot system using Model Predictive Contouring Control (MPCC). MPCC allows separating the tracking accuracy and productivity, to improve productivity better than the traditional Model Predictive Control (MPC) which follows a time-dependent reference. In the proposed distributed MPCC, each robot exchanges the predicted paths of the other robots and generates the collision-free motion in a parallel manner. The proposed distributed MPCC method is tested in industrial operation scenarios in the robot simulation platform Gazebo. The simulation results show that the proposed distributed MPCC method realizes real-time multi-robot motion planning and performs better than three commonly-used planning methods (dynamic window approach, MPC, and prioritized planning).

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Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups

Cited by 32 publications

References 22 publications

A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw

Distributed Model Predictive Contouring Control for Real-Time Multi-Robot Motion Planning

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