Incremental, sensor-based motion generation for mobile manipulators in unknown, dynamic environments

Lehner, Peter; Sieverling, Arne; Brock, Oliver

doi:10.1109/icra.2015.7139861

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…Convex optimization (Alonso-Mora et al, 2015; Lehner et al, 2015) and quadratic programming (QP) (Bäuml et al, 2011; Escande et al, 2014; Giftthaler et al, 2017) have been studied to generate trajectory for high-DOF systems in dynamic environments. Joint velocity control-based manipulator trajectory generation approaches (Bodily et al, 2017b; Buss, 2004; Reiter et al, 2018) use Jacobian approximations for generating joint configurations to minimize the time to execute a path-constrained trajectory.…”

Section: Related Workmentioning

confidence: 99%

“…Dynamic environments require robots to make local adjustments to their global motion plans. Lehner et al (2015) presented an incremental motion generation method for mobile manipulators in unknown and dynamic environments. Alonso-Mora et al (2015) formulated a convex optimization problem for the task of multi-robot deformable object manipulation.…”

Section: Related Workmentioning

confidence: 99%

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Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots

Kabir

Thakar

Malhan

et al. 2021

The International Journal of Robotics Research

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We present an approach to generate path-constrained synchronous motion for the coupled ensemble of robots. In this article, we refer to serial-link manipulators and mobile bases as robots. We assume that the relative motion constraints among the objects in the environment are given. We represent the motion constraints as path constraints and pose the problem of path-constrained synchronous trajectory generation as a non-linear optimization problem. Our approach generates configuration space trajectories for the robots to manipulate the objects such that the given motion constraints among the objects are satisfied. We present a method that formulates the problem as a discrete parameter optimization problem and solves it using successive constraint refinement techniques. The method adaptively selects the parametric representation of the configuration variables for a given scenario. It also generates an approximate solution as the starting point for the successive constraint refinement stages to reduce the computation time. We discuss in detail why successive constraint refinement strategies are useful for solving this class of problems. We demonstrate the effectiveness of the proposed method on challenging test cases in simulation and physical environments with high-degree-of-freedom robotic systems.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots

Kabir

Thakar

Malhan

et al. 2021

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…The algorithm adapts the roadmap locally based on changes in the environment to preserve structure. The method was adapted in [24] to iteratively construct the roadmap based on the paths of a sampling-based planner. Prentice and Roy [25] construct a roadmap in belief space which is able to plan paths with expected low uncertainty.…”

Section: R Wmentioning

confidence: 99%

The Repetition Roadmap for Repetitive Constrained Motion Planning

Lehner

Albu‐Schäffer

2018

IEEE Robot. Autom. Lett.

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We present the Repetition Roadmap, a motion planner which effectively exploits the repetitiveness of a set of tasks with small variations to efficiently compute new motions. The method learns an abstract roadmap of probability distributions for the configuration space of a particular task set from previous solution paths. We show how to construct the Repetition Roadmap by learning a Gaussian Mixture Model and connecting the distribution components based on the connectivity information of the prior paths. We present an algorithm which exploits the information in the Repetition Roadmap to guide the search for solutions of similar tasks. We illustrate our method in a maze, which explains the construction of the Repetition Roadmap and how the method can generalize over different environments. We show how to apply the Repetition Roadmap to similar constrained manipulation tasks and present our results including significant speedup in computation time when compared to Uniform and Adaptive Sampling.

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“…Here we review the related work on integrated and autonomous systems that use a mobile manipulator for some application in an unknown environment. Note that we consider unknown regions of the environment as obstacles and not collision-free regions(which can clearly be detrimental) similarly to the assumption in Lehner et al (2015). Furthermore, it does not use view planning to explore the environment.…”

Section: Related Workmentioning

confidence: 99%

Mobile manipulator planning under uncertainty in unknown environments

Pilania

Gupta

2018

The International Journal of Robotics Research

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We present a sampling-based mobile manipulator planner that considers the base pose uncertainty and the effects of this uncertainty on manipulator motions. The overall planner has three distinct and novel features: (i) it uses the Hierarchical and Adaptive Mobile Manipulator Planner (HAMP) that plans for both the base and the arm in a judicious manner; (ii) it uses localization-aware sampling and connection strategies to consider only those nodes and edges which contribute toward better localization; (iii) it incorporates base pose uncertainty along the edges (where arm remains static) and the effects of this uncertainty are considered on arm motion. We call this overall planner HAMP-BUA, where BUA denotes "Base pose Uncertainty and its propagation to Arm motions. " First we evaluate our planner in known static environments and show that it finds a safer path as compared with other variants where uncertainty is not considered at different levels as mentioned above. Next, we incorporate our planner within an integrated and fully autonomous system for mobile pick-and-place tasks in unknown static environments. A key aspect of our integrated system is that the planner works in tandem with base and arm exploration modules that explore the unknown environment. Our system is implemented both in simulation and on the actual Simon Fraser University (SFU) mobile manipulator and we present the corresponding results. It demonstrates a level of competency in exploring unknown environments for carrying out pick-and-place tasks that has not been demonstrated previously.

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Incremental, sensor-based motion generation for mobile manipulators in unknown, dynamic environments

Cited by 13 publications

References 29 publications

Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots

Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots

The Repetition Roadmap for Repetitive Constrained Motion Planning

Mobile manipulator planning under uncertainty in unknown environments

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