Aerial Grasping via Maximizing Delta-Arm Workspace Utilization

Abstract

Workspace limitations restrict the operational capabilities and range of motion for systems with robotic arms. Maximizing workspace utilization has the potential to provide better solutions for aerial manipulation tasks, increasing the system’s flexibility and operational efficiency. In this paper, we introduce a novel planning framework for aerial grasping that maximizes workspace utilization. We formulate an optimization problem to optimize the aerial manipulator’s trajectory, incorporating task constraints to achieve efficient manipulation. To address the challenge of incorporating the delta arm’s non-convex workspace into optimization constraints, we leverage a Multilayer Perceptron (MLP) to map the point positions to feasibility probabilities. Furthermore, we employ Reversible Residual Networks (RevNet) to approximate the complex forward kinematics of the delta arm, utilizing its efficient model gradients to further eliminate workspace constraints. We validate our methods in simulations and real-world experiments to demonstrate their effectiveness.