Timed-Elastic Bands for Manipulation Motion Planning

Magyar, Bence; Tsiogkas, Nikolaos; Deray, Jérémie; Pfeiffer, Sammy; Lane, David M.

doi:10.1109/lra.2019.2927956

Cited by 25 publications

(15 citation statements)

References 19 publications

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“…In [ 9 ], a set of possible local trajectories were used blending the difference between global and local planning. TEB-based formulations have also been utilized for motion planning of manipulators [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

Andreasson

Larsson

Lowry

2022

Sensors

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This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.

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Section: Related Workmentioning

confidence: 99%

A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

Andreasson

Larsson

Lowry

2022

Sensors

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“…Besides optimizing the number of states K it is also possible to choose a sufficiently high but fixed K or optimize ∆t [22]. Solving (5) to achieve a global solution by optimization will be called Global Trajectory Optimization (GTO).…”

Section: A Global Planning Problem (Offline)mentioning

confidence: 99%

Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

Krämer

Bertram

2022

IEEE Access

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Trajectory optimization is a promising method for planning trajectories of robotic manipulators. With the increasing success of collaborative robots in dynamic environments, the demand for online planning methods grows and offers new opportunities as well as challenges for trajectory optimization. Special requirements in terms of real-time capabilities are one of the greatest difficulties. Optimizing a short planning horizon instead of an entire trajectory is one approach to reduce computation time, which nonetheless separates the optimality of local and global solutions. This contribution introduces, on the one hand, Extended Initialization as a new approach that reduces the risk of local minima and aims at improving the quality of the global trajectory. On the other hand, the particularly critical cases in which local solutions lead to standstills are mitigated by globally guiding local solutions. The evaluation performs four experiments with comparisons to established methods like STOMP or PRM* and demonstrates the effectiveness of both approaches.

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“…Additionally, TEB-based motion planning has been used for robotic arms [21], so future work could include extending the multi-modal trajectory optimization approach presented in this paper to problems where multi-modality arises from contact and interaction with objects.…”

Section: Future Workmentioning

confidence: 99%

Multi-Modal Motion Planning Using Composite Pose Graph Optimization

Beyer

Balabanska

Tal

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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This work presents a motion planning framework for multi-modal vehicle dynamics. An approach for transcribing cost function, vehicle dynamics, and state and control constraints into a sparse factor graph is introduced. By formulating the motion planning problem in pose graph form, the motion planning problem can be addressed using efficient optimization techniques, similar to those already widely applied in dual estimation problems, e.g., pose graph optimization for simultaneous localization and mapping (SLAM). Optimization of trajectories for vehicles under various dynamics models is demonstrated. The motion planner is able to optimize the location of mode transitions, and is guided by the pose graph optimization process to eliminate unnecessary mode transitions, enabling efficient discovery of optimized mode sequences from rough initial guesses. This functionality is demonstrated by using our planner to optimize multi-modal trajectories for vehicles such as an airplane which can both taxi on the ground or fly. Extensive experiments validate the use of the proposed motion planning framework in both simulation and real-life flight experiments using a vertical take-off and landing (VTOL) fixed-wing aircraft that can transition between hover and horizontal flight modes.

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Timed-Elastic Bands for Manipulation Motion Planning

Cited by 25 publications

References 19 publications

A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

Multi-Modal Motion Planning Using Composite Pose Graph Optimization

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