Autonomous navigation of ground robots has been widely used in indoor structured 2D environments, but there are still many challenges in outdoor 3D unstructured environments, especially in rough, uneven terrains. This paper proposed a plane-fitting based uneven terrain navigation framework (PUTN) to solve this problem. The implementation of PUTN is divided into three steps. First, based on Rapidly-exploring Random Trees (RRT), an improved sample-based algorithm called Plane Fitting RRT* (PF-RRT*) is proposed to obtain a sparse trajectory. Each sampling point corresponds to a custom traversability index and a fitted plane on the point cloud. These planes are connected in series to form a traversable strip. Second, Gaussian Process Regression is used to generate traversability of the dense trajectory interpolated from the sparse trajectory, and the sampling tree is used as the training set. Finally, local planning is performed using nonlinear model predictive control (NMPC). By adding the traversability index and uncertainty to the cost function, and adding obstacles generated by the real-time point cloud to the constraint function, a safe motion planning algorithm with smooth speed and strong robustness is available. Experiments in real scenarios are conducted to verify the effectiveness of the method. The source code is released for the reference of the community.

Implicit neural representations have shown promising potential for the 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that using volumetric representations, collision checking using the implicit representation for a 3D point is much slower. In the paper, we propose to 1) leverage a neural network as an implicit function approximator for the information gain field and 2) combine the implicit fine-grained representation with coarse volumetric representations to improve efficiency. Further with the improved efficiency, we propose a novel informative path planning based on a graph-based planner. Our method demonstrates significant improvements in the reconstruction quality and planning efficiency compared with autonomous reconstructions with implicit and explicit representations. We deploy the method on a real UAV and the results show that our method can plan informative views and reconstruct a scene with high quality.

Real-time interception of a fast-moving object by a robotic arm in cluttered environments filled with static or dynamic obstacles permits only tens of milliseconds for reaction times, hence quite challenging and arduous for state-of-the-art robotic planning algorithms to perform multiple robotic skills, for instance, catching the dynamic object and avoiding obstacles, in parallel. This paper proposes an unified framework of robotic path planning through embedding the high-dimensional temporal information contained in the event stream to distinguish between safe and colliding trajectories into a low-dimension space manifested with a pre-constructed 2D densely connected graph. We then leverage a fast graph-traversing strategy to generate the motor commands necessary to effectively avoid the approaching obstacles while simultaneously intercepting a fast-moving objects. The most distinctive feature of our methodology is to conduct both object interception and obstacle avoidance within the same algorithm framework based on deep manifold learning. By leveraging a highly efficient diffusion-map based variational autoencoding and Extended Kalman Filter(EKF), we demonstrate the effectiveness of our approach on an autonomous 7-DoF robotic arm using only onboard sensing and computation. Our robotic manipulator was capable of avoiding multiple obstacles of different sizes and shapes while successfully capturing a fast-moving soft ball thrown by hand at normal speed in different angles. Complete video demonstrations of our experiments can be found in https://sites.google.com/view/multirobotskill/home.

This thesis studies the interference free workspace (IFW) of arbitrary cable-driven robots (CDRs) for both cables and obstacles using the ray-based method. Continuing from this, the point to point path planning and verification technique to ensure that the resulting motion is within the workspace is also studied. CDRs are a type of parallel mechanism where cables, actuated by motorized winches, are connected from a base platform to either a single end-effector (also called cable-driven parallel robots, or CD-PRs) or multiple links that are connected in series through passive joints (also called multi-link cable-driven Robots or MCDRs). CDRs have attracted much attention over years due to their promising advantages, such as high payload capacity, large potential workspace, and ease of reconfiguration.

One of the most complex tasks of decision making and planning is to gather information. This task becomes even more complex when the state is high-dimensional and its belief cannot be expressed with a parametric distribution. Although the state is high-dimensional, in many problems only a small fraction of it might be involved in transitioning the state and generating observations. We exploit this fact to calculate an information-theoretic expected reward, mutual information (MI), over a much lower-dimensional subset of the state, to improve efficiency and without sacrificing accuracy. A similar approach was used in previous works, yet specifically for Gaussian distributions, and we here extend it for general distributions. Moreover, we apply the dimensionality reduction for cases in which the new states are augmented to the previous, yet again without sacrificing accuracy. We then continue by developing an estimator for the MI which works in a Sequential Monte Carlo (SMC) manner, and avoids the reconstruction of future belief's surfaces. Finally, we show how this work is applied to the informative planning optimization problem. This work is then evaluated in a simulation of an active SLAM problem, where the improvement in both accuracy and timing is demonstrated.

Everyday household tasks require both commonsense understanding of the world and situated knowledge about the words, which then need to be mapped to actions and world current environment. To create a task plan for "Make dinner," objects available to the agent. For example, if the LLM an agent needs common sense: object affordances, such as produced "reach in and pick up the jar of pickles," that that the stove and microwave can be used for heating; logical string would have to neatly map to an executable action like sequences of actions, such as an oven must be preheated before "pick up jar." A key component missing in LLM-based task food is added; and task relevance of objects and actions, planning is state feedback from the environment. The fridge such as heating and food are actions related to "dinner" in the in the house might not contain chicken, soda, or pickles, first place. However, this reasoning is infeasible without state but a high-level instruction "Make dinner" doesn't give us feedback. The agent needs to know what food is available in that world state information. Our work introduces situatedawareness the current environment, such as whether the freezer contains in LLM-based robot task planning.

In the timeline-based approach to planning, originally born in the space sector, the evolution over time of a set of state variables (the timelines) is governed by a set of temporal constraints. Traditional timeline-based planning systems excel at the integration of planning with execution by handling temporal uncertainty. In order to handle general nondeterminism as well, the concept of timeline-based games has been recently introduced. It has been proved that finding whether a winning strategy exists for such games is 2EXPTIME-complete. However, a concrete approach to synthesize controllers implementing such strategies is missing. This paper fills this gap, outlining an approach to controller synthesis for timeline-based games.

The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perform tasks such as diagnosis in case of fault. A promising approach to deal with this complexity is the concept of causality. However, most research on causality has focused on inferring causal relations between parts of an unknown system. Engineering uses causality in a fundamentally different way: complex systems are constructed by combining components with known, controllable behavior. As CPS are constructed by the second approach, most data-based causality models are not suited for industrial automation. To bridge this gap, a Uniform Causality Model for various application areas of industrial automation is proposed, which will allow better communication and better data usage across disciplines. The resulting model describes the behavior of CPS mathematically and, as the model is evaluated on the unique requirements of the application areas, it is shown that the Uniform Causality Model can work as a basis for the application of new approaches in industrial automation that focus on machine learning.

Quadruped platforms have become an active topic of research due to their high mobility and traversability in rough terrain. However, it is highly challenging to determine whether the clattered environment could be passed by the robot and how exactly its path should be calculated. Moreover, the calculated path may pass through areas with dynamic objects or environments that are dangerous for the robot or people around. Therefore, we propose a novel conceptual approach of teaching quadruped robots navigation through user-guided path planning in virtual reality (VR). Our system contains both global and local path planners, allowing robot to generate path through iterations of learning. The VR interface allows user to interact with environment and to assist quadruped robot in challenging scenarios. The results of comparison experiments show that cooperation between human and path planning algorithms can increase the computational speed of the algorithm by 35.58% in average, and non-critically increasing of the path length (average of 6.66%) in test scenario. Additionally, users described VR interface as not requiring physical demand (2.3 out of 10) and highly evaluated their performance (7.1 out of 10). The ability to find a less optimal but safer path remains in demand for the task of navigating in a cluttered and unstructured environment.

AlphaZero.jl is making two significant decisions to overcome the computing challenge in those algorithms. The first is the selection of Julia as the unique programming language used for this project. The second is the main focus of this Google Summer of Code (GSoC): two fully-batched implementations of the Monte Carlo Tree Search (MCTS) algorithms running on GPU. The MCTS algorithm is a crucial bottleneck for MuZero. It could account for up to 80% of the execution time of the previous (non-batched) implementation.