Tracking-error model-based predictive control for mobile robots in real time

Klančar, Gregor; Škrjanc, Igor

doi:10.1016/j.robot.2007.01.002

Cited by 342 publications

(186 citation statements)

References 20 publications

(38 reference statements)

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“…Another approach on the control level is model predictive control [7], which iteratively optimizes a cost function for a finite horizon into the future. It has been used in different areas of robotics like tracking mobile robots [8] or generating walking gaits for humanoid robots [9]. Using online replanning methods has many advantages since it extends well-known offline planning methods to dynamic scenarios.…”

Section: A Online Replanningmentioning

confidence: 99%

“…The while loop in Algorithm 1 contains the steps that are executed with a frequency of 1 ∆t. In step 1 of Algorithm 1 the current system state and goal state are measured and fed as input into the update of the phase variable in step 2, which is computed with Equation (8). Afterwards, the trajectory is transformed with Equation (7) such that the current robot and goal state are on the plan.…”

Section: Adaptive Motion Execution Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Reactive phase and task space adaptation for robust motion execution

Englert

Toussaint

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-An essential aspect for making robots succeed in real-world environments is to give them the ability to robustly perform motions in continuously changing situations. Classical motion planning methods usually create plans for static environments. The direct execution of such plans in dynamic environments often becomes problematic. We present an approach that adapts motion plans by feeding changes of the environment into a transformation of the plan in task space. Furthermore, the progress in the plan is defined with a phase variable that is updated adaptively according to the actual task progress. This phase variable releases the strict time compliance that many motion planning methods bring along. The main benefit of our approach is the ability to do this adaptation in a computational efficient manner during the execution of the motion. Thus, the gap between the motion planning and motion execution stage is bridged by continuously transforming geometric and dynamic features of a reference plan to the current situation. We evaluate the performance of our approach by comparing it to alternative methods such as dynamic motion primitives and continuous replanning on several simulated benchmark tasks. Moreover, we demonstrate the real robot applicability on a PR2 robot platform.

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Section: A Online Replanningmentioning

confidence: 99%

Section: Adaptive Motion Execution Algorithmmentioning

confidence: 99%

Reactive phase and task space adaptation for robust motion execution

Englert

Toussaint

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…In (Lages and Alves, 2006;Klančar andŠkrjanc, 2007), model-predictive control based on a linear, time-varying description of the system was used for trajectory tracking control. Generalized predictive control was used to solve path following control in (Ollero and Amidi, 1991).…”

Section: The Path Following Problemmentioning

confidence: 99%

Path Following with an Optimal Forward Velocity for a Mobile Robot

Kanjanawanishkul

Hofmeister

Zell

2010

IFAC Proceedings Volumes

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Abstract:In this paper, we present a novel solution for a path following problem in partially-known static environments. Given linearized error dynamic equations, model predictive control (MPC) is employed to produce a sequence of angular velocities. Since the forward velocity of the robot has to be adapted to environmental constraints and robot dynamics while the robot is following a path, we propose an optimal solution to generate the velocity profile. Furthermore, we integrate an obstacle-avoidance behavior using local sensor information with a path-following behavior based on global knowledge. To achieve this, we introduce new waypoints in order to move the robot away from obstacles while the robot still keeps following the desired path. Extensive simulations and experiments with a physical unicycle mobile robot have been conducted to illustrate the effectiveness of our path following control framework.

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“…On the other hand, the tracking performance can be easily tuned by assigning the poles to the required locations through properly choosing control parameters k1, k2 and k3. This greatly simplifies the control parameter tuning process, as compared to other local controllers [10,14] .…”

Section: Low-level Controller Designmentioning

confidence: 99%

“…This is the main obstacle of applying MPC into plants with fast dynamics such as vehicles. Although few MPC algorithms in this field have been implemented in real-time, they are based on linear settings [9,10] . The linearized model is only valid when the vehicle is close to the reference trajectory.…”

Section: Introductionmentioning

confidence: 99%

Experimental tests of autonomous ground vehicles with preview

Liu

Chen

Andrews

2010

Int. J. Autom. Comput.

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This paper describes the design and experimental tests of a path planning and reference tracking algorithm for autonomous ground vehicles. The ground vehicles under consideration are equipped with forward looking sensors that provide a preview capability over a certain horizon. A two-level control framework is proposed for real-time implementation of the Model Predictive Control (MPC) algorithm, where the high-level performs on-line optimization to generat the best possible local reference respect to various constraints and the low-level commands the vehicle to follow realistic trajectories generated by the high-level controller. The proposed control scheme is implemented on an indoor testbed through networks with satisfactory performance.

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Tracking-error model-based predictive control for mobile robots in real time

Cited by 342 publications

References 20 publications

Reactive phase and task space adaptation for robust motion execution

Reactive phase and task space adaptation for robust motion execution

Path Following with an Optimal Forward Velocity for a Mobile Robot

Experimental tests of autonomous ground vehicles with preview

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