In contrast, deep reinforcement learning (DRL) methods use flexible neural-network-based function approximators to discover policies that generalize naturally to unseen circumstances.
In this paper, we propose a general planning network, called Graph-based Motion Planning Networks (GrMPN), that will be able to i) learn and plan on general irregular graphs, hence ii) render existing planning network architectures special cases.
To alleviate this bottleneck, we present a fast neural network collision checking heuristic, ClearanceNet, and incorporate it within a planning algorithm, ClearanceNet-RRT (CN-RRT).
Critical PRMs are demonstrated to achieve up to three orders of magnitude improvement over uniform sampling, while preserving the guarantees and complexity of sampling-based motion planning.
The experimental results demonstrate that our proposed method can generate a bunch of human-like multi-vehicle interaction trajectories that can fit different road conditions remaining the key interaction patterns of agents in the provided scenarios, which is import to the development of autonomous vehicles.
Complex and skillful motions in actual assembly process are challenging for the robot to generate with existing motion planning approaches, because some key poses during the human assembly can be too skillful for the robot to realize automatically.
DRM proposes a divide-and-conquer approach that distributively partitions the problem among K cliques of robots.
This paper reviews machine learning methods for the motion planning of autonomous vehicles (AVs), with exclusive focus on the longitudinal behaviors and their impact on traffic congestion.
In this work, a novel, end-to-end motion planning method is proposed for quadrotor navigation in cluttered environments.