In manufacturing and automation settings, robots often have to perform highly-repetitive manipulation tasks in structured environments. In this work we are interested in settings where tasks are similar, yet not identical (e.g., due to uncertain orientation of objects) and motion planning needs to be extremely fast. Preprocessing-based approaches prove to be very beneficial in these settings; they analyze the configuration-space offline to generate some auxiliary information which can then be used in the query phase to speedup planning times. Typically, the tighter the requirement is on query times the larger the memory footprint will be. In particular, for high-dimensional spaces, providing real-time planning capabilities is extremely challenging. While there are planners that guarantee real-time performance by limiting the planning horizon, we are not aware of general-purpose planners capable of doing it for infinite horizon (i.e., planning to the goal). To this end, we propose a preprocessing-based method that provides provable bounds on the query time while incurring only a small amount of memory overhead in the query phase. We evaluate our method on a 7-DOF robot arm and show a speedup of over tenfold in query time when compared to the PRM algorithm, while provably guaranteeing a maximum query time of less than 3 milliseconds.
Finally, our notion of attractor states is similar to control-based methods that ensure safe operation over local regions of the free configuration space @cite_22 @cite_2 . These regions are then used within a high-level motion planner to compute collision-free paths.
@cite_22 @cite_2
2104906962 2014776084 2591921113 2772321183
This paper presents an alternative to the Potential Field Method of computing local collision-free motions for general manipulators. The main distinction is that we separate the description of the task from constraints of anti-collision. This enables to control accurately all relevant measures of the problem. A task is expressed by the minimization of a function plus eventually some geometric constraints, whereas anti-collision is translated into very simple linear constraints through the methods of the velocity dampers and the tangent separating planes. This approach is applied to the control of manipulators with a high number of degrees of freedom, using hierarchical descriptions of the environment and the robots. This is illustrated by two realizations, path planning for a ten link manipulator in the cluttered environment of a nuclear plant reactor, and cooperative tasks between two six degree of freedom robots. In the end we discuss how to incorporate the action of a global planner in this general framework. We designed a distributed collision-free formation flight control law in the framework of nonlinear model predictive control. Formation configuration is determined in the virtual reference point coordinate system. Obstacle avoidance is guaranteed by cost penalty, and intervehicle collision avoidance is guaranteed by cost penalty combined with a new priority strategy. Safety Barrier Certificates that ensure collision-free maneuvers for teams of differential flatness-based quadrotors are presented in this paper. Synthesized with control barrier functions, the certificates are used to modify the nominal trajectory in a minimally invasive way to avoid collisions. The proposed collision avoidance strategy complements existing flight control and planning algorithms by providing trajectory modifications with provable safety guarantees. The effectiveness of this strategy is supported both by the theoretical results and experimental validation on a team of five quadrotors. Quadrotors that navigate through unknown, cluttered environments have only recently begun to emerge following the development of small form-factor sensing and computing hardware and computationally efficient collision avoidance algorithms. Computation time of planning algorithms has significantly decreased in part to using local information as opposed to using a global map for collision avoidance. Safe planning with local information, however, restricts the direction of travel to remain within the perception system's field-of-view (FOV). The vehicle's motion becomes more constrained with body-mounted narrow FOV sensors, reducing vehicle maneuverability and speed. This work presents a relaxed-constraint Model Predictive Control framework that allows motions outside the perception FOV with guaranteed safety. The key aspect of this approach is the ability to safely choose a motion primitive generated in the past. Simulation and hardware results shows the new framework improves time to goal and flight path efficiency in environments with varying levels of clutter.