A fast heuristic Cartesian space motion planning algorithm for many-DoF robotic manipulators in dynamic environments

Abstract Real-time, safe, and stable motion planning in co-robot systems involving dynamic human robot interaction (HRI) remains challenging due to the time varying nature of the problem. One of the biggest challenges is to guarantee closed-loop stability of the planning algorithm in dynamic environments. Typically, this can be addressed if there exists a perfect predictor that precisely predicts the future motions of the obstacles. Unfortunately, a perfect predictor is not possible to achieve. In HRI environments in this paper, human workers and other robots are the obstacles to the ego robot. We discuss necessary conditions for the closed-loop stability of a planning problem using the framework of model predictive control (MPC). It is concluded that the predictor needs to be able to detect the obstacles’ movement mode change within a time delay allowance and the MPC needs to have a sufficient prediction horizon and a proper cost function. These allow MPC to have an uncertainty tolerance for closed-loop stability, and still avoid collision when the obstacles’ movement is not within the tolerance. Also, the closed-loop performance is investigated using a notion of M-convergence, which guarantees finite local convergence (at least M steps ahead) of the open-loop trajectories toward the closed-loop trajectory. With this notion, we verify the performance of the proposed MPC with stability enhanced prediction through simulations and experiments. With the proposed method, the robot can better deal with dynamic environments and the closed-loop cost is reduced.

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High-DOF Robots in Dynamic Environments Using Incremental Trajectory Optimization

International Journal of Humanoid Robotics ◽

10.1142/s0219843614410011 ◽

2014 ◽

Vol 11 (02) ◽

pp. 1441001 ◽

Cited By ~ 6

Author(s):

Chonhyon Park ◽

Jia Pan ◽

Dinesh Manocha

Keyword(s):

Motion Planning ◽

Trajectory Optimization ◽

A Priori ◽

Coupled System ◽

Dynamic Environments ◽

Planning Problem ◽

Simulated Environments ◽

Tightly Coupled ◽

Planning Algorithm ◽

Low Dimensional

We present a novel optimization-based motion planning algorithm for high degree-of-freedom (DOF) robots in dynamic environments. Our approach decomposes the high-dimensional motion planning problem into a sequence of low-dimensional sub-problems. We compute collision-free and smooth paths using optimization-based planning and trajectory perturbation for each sub-problem. The overall algorithm does not require a priori knowledge about global motion or trajectories of dynamic obstacles. Rather, we compute a conservative local bound on the position or trajectory of each obstacle over a short time and use the bound to incrementally compute a collision-free trajectory for the robot. The high-DOF robot is treated as a tightly coupled system, and we incrementally use constrained coordination to plan its motion. We highlight the performance of our planner in simulated environments on robots with tens of DOFs.

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Risk-Informed-RRT*: A Sampling-based Human-friendly Motion Planning Algorithm for Mobile Service Robots in Indoor Environments

2018 IEEE International Conference on Information and Automation (ICIA) ◽

10.1109/icinfa.2018.8812396 ◽

2018 ◽

Author(s):

Wenzheng Chi ◽

Jiankun Wang ◽

Max Qing-Hu Meng

Keyword(s):

Motion Planning ◽

Service Robots ◽

Mobile Service ◽

Indoor Environments ◽

Planning Algorithm ◽

Mobile Service Robots

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A new method on motion planning for mobile robots using jump point search and Bezier curves

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211019220 ◽

2021 ◽

Vol 18 (4) ◽

pp. 172988142110192

Author(s):

Ben Zhang ◽

Denglin Zhu

Keyword(s):

Motion Planning ◽

Initial Point ◽

Target Point ◽

Jump Point ◽

Bezier Curves ◽

Bézier Curves ◽

Front End ◽

Planning Algorithm ◽

Point Search ◽

Path Planning Algorithm

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.

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