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

Abstract

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.

Area-Coverage Planning for Spray-based Surface Disinfection with a Mobile Manipulator

“…Finding an appropriate parametric representation and a good optimization seed can significantly accelerate the optimization process as demonstrated by Kabir et al. [92] , [93] , [94] . Here an initial seed is determined by computing inverse kinematic (IK) solutions at a high resolution.…”

“…Each constraint has one or several attraction basins in the optimization parameter space. Having all constraints together may result in opposing attraction canceling out, causing the solution to get stuck in local minima or at an infeasible location [93] . Hence, it is necessary to solve a problem with a few constraints first and successively add constraints to the previous solutions.…”

Multi-stage warm started optimal motion planning for over-actuated mobile platforms

“…This method keeps introducing problem constraints (grasping pose, gripper speed, collisions) sequentially to the optimization problem, continuously warm-starting and refining the solution, which improves the performance of the motion planner in comparison with the cold-started planner. These results were extended by the authors in [17], where a multistaged warm started motion planner for a group of robots (up to three manipulators or a mobile manipulator with two robotic arms) was presented, including a deep analysis on the best sequence of introduction of the problem constraints (position, velocity, orientation of the end effector, collisions). The same multi-staged warm started motion planner was also used in [18] for surface disinfection with mobile manipulators, generating first a path to cover the goal area by means of a branch and bound-based tree search.…”

Optimized Dynamic Collision Avoidance Algorithm for USV Path Planning

“…Avoiding unpredictable obstacles can better solve the DWA [28]. DWA is widely used in dynamic obstacle avoidance path optimization of UAVs, robots, and USVs [29][30][31]. Dobrevski reported local path planning based on DWA and deep reinforcement learning to improve path optimization [32].…”