通过优化机械臂工作空间利用率，提升无人机抓取的灵活性与效率。
Aerial Grasping via Maximizing Delta-Arm Workspace Utilization
- 用MLP将位置点映射为可行性概率，解决非凸工作空间约束问题。
- 采用RevNet近似德尔塔机械臂正向运动学，实现高效轨迹规划。
- 在仿真与真实实验中验证方法有效性，适用于复杂空中操作场景。
工作空间限制了带机械臂系统的操作能力与运动范围。最大化工作空间利用率有望为空中操作任务提供更优解，提升系统灵活性与作业效率。本文提出一种新型空中抓取规划框架，通过优化空中机械臂轨迹并融入任务约束，实现高效操作。针对德尔塔机械臂非凸工作空间难以纳入优化约束的问题，我们采用多层感知机（MLP）将位置点映射为可行性概率。此外，利用可逆残差网络（RevNet）近似德尔塔机械臂复杂的正向运动学模型，借助高效的模型梯度消除工作空间约束。我们在仿真与真实实验中验证了该方法的有效性。
原文摘要 · Abstract (English)
The workspace limits the operational capabilities and range of motion for the systems with robotic arms. Maximizing workspace utilization has the potential to provide more optimal 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 position points to feasibility probabilities.Furthermore, we employ Reversible Residual Networks (RevNet) to approximate the complex forward kinematics of the delta arm, utilizing efficient model gradients to eliminate workspace constraints. We validate our methods in simulations and real-world experiments to demonstrate their effectiveness.
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