Fast and efficient motion planning algorithms are crucial for many
state-of-the-art robotics applications such as self-driving cars. Existing
motion planning methods become ineffective as their computational complexity
increases exponentially with the dimensionality of the motion planning problem.
To address this issue, we present Motion Planning Networks (MPNet), a neural
network-based novel planning algorithm. The proposed method encodes the given
workspaces directly from a point cloud measurement and generates the end-to-end
collision-free paths for the given start and goal configurations. We evaluate
MPNet on various 2D and 3D environments including the planning of a 7 DOF
Baxter robot manipulator. The results show that MPNet is not only consistently
computationally efficient in all environments but also generalizes to
completely unseen environments. The results also show that the computation time
of MPNet consistently remains less than 1 second in all presented experiments,
which is significantly lower than existing state-of-the-art motion planning
algorithms.

Motion Planning Networks (MPNet) is a novel neural network-based algorithm that efficiently generates collision-free paths for robotics applications in various environments, and consistently remains computationally efficient in all presented experiments.

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Motion Planning Networks

We consider partially observable Markov decision processes (POMDPs), that are
a standard framework for robotics applications to model uncertainties present
in the real world, with temporal logic specifications. All temporal logic
specifications in linear-time temporal logic (LTL) can be expressed as parity
objectives. We study the qualitative analysis problem for POMDPs with parity
objectives that asks whether there is a controller (policy) to ensure that the
objective holds with probability 1 (almost-surely). While the qualitative
analysis of POMDPs with parity objectives is undecidable, recent results show
that when restricted to finite-memory policies the problem is EXPTIME-complete.
While the problem is intractable in theory, we present a practical approach to
solve the qualitative analysis problem. We designed several heuristics to deal
with the exponential complexity, and have used our implementation on a number
of well-known POMDP examples for robotics applications. Our results provide the
first practical approach to solve the qualitative analysis of robot motion
planning with LTL properties in the presence of uncertainty.

本文探讨了具有标准框架的 POMDPs，以模拟现实世界中存在的不确定性，以及时间逻辑规范。我们研究了帕里目标下的 POMDP 定性分析问题，该问题在理论上难以计算，但我们提出了解决该问题的实用方法，并在许多机器人应用的已知示例中使用了我们的实现。