In this paper, we present an approach for coverage path planning for a team of an energy-constrained Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). Both the UAV and the UGV have predefined areas that they have to cover. The goal is to perform complete coverage by both robots while minimizing the coverage time. The UGV can also serve as a mobile recharging station. The UAV and UGV need to occasionally rendezvous for recharging. We propose a heuristic method to address this NP-Hard planning problem. Our approach involves initially determining coverage paths without factoring in energy constraints. Subsequently, we cluster segments of these paths and employ graph matching to assign UAV clusters to UGV clusters for efficient recharging management. We perform numerical analysis on real-world coverage applications and show that compared with a greedy approach our method reduces rendezvous overhead on average by 11.33\%. We demonstrate proof-of-concept with a team of a VOXL m500 drone and a Clearpath Jackal ground vehicle, providing a complete system from the offline algorithm to the field execution.

Legged robots, particularly quadrupeds, offer promising navigation capabilities, especially in scenarios requiring traversal over diverse terrains and obstacle avoidance. This paper addresses the challenge of enabling legged robots to navigate complex environments effectively through the integration of data-driven path-planning methods. We propose an approach that utilizes differentiable planners, allowing the learning of end-to-end global plans via a neural network for commanding quadruped robots. The approach leverages 2D maps and obstacle specifications as inputs to generate a global path. To enhance the functionality of the developed neural network-based path planner, we use Vision Transformers (ViT) for map pre-processing, to enable the effective handling of larger maps. Experimental evaluations on two real robotic quadrupeds (Boston Dynamics Spot and Unitree Go1) demonstrate the effectiveness and versatility of the proposed approach in generating reliable path plans.

Monte Carlo Tree Search (MCTS) algorithms such as AlphaGo and MuZero have achieved superhuman performance in many challenging tasks. However, the computational complexity of MCTS-based algorithms is influenced by the size of the search space. To address this issue, we propose a novel probability tree state abstraction (PTSA) algorithm to improve the search efficiency of MCTS. A general tree state abstraction with path transitivity is defined. In addition, the probability tree state abstraction is proposed for fewer mistakes during the aggregation step. Furthermore, the theoretical guarantees of the transitivity and aggregation error bound are justified. To evaluate the effectiveness of the PTSA algorithm, we integrate it with state-of-the-art MCTS-based algorithms, such as Sampled MuZero and Gumbel MuZero. Experimental results on different tasks demonstrate that our method can accelerate the training process of state-of-the-art algorithms with 10% 45% search space reduction.

Large environments are challenging for path planning algorithms as the size of the configuration space increases. Furthermore, if the environment is mainly unexplored, large amounts of the path are planned through unknown areas. Hence, a complete replanning of the entire path occurs whenever the path collides with newly discovered obstacles. We propose a novel method that stops the path planning algorithm after a certain distance. It is used to navigate the algorithm in large environments and is not prone to problems of existing navigation approaches. Furthermore, we developed a method to detect significant environment changes to allow a more efficient replanning. At last, we extend the path planner to be used in the U-Shift concept vehicle. It can switch to another system model and rotate around the center of its rear axis. The results show that the proposed methods generate nearly identical paths compared to the standard Hybrid A* while drastically reducing the execution time. Furthermore, we show that the extended path planning algorithm enables the efficient use of the maneuvering capabilities of the concept vehicle to plan concise paths in narrow environments.

We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a nominal policy versus switching to a safe backup policy at run-time. The decisions produced by our algorithms are safe in the sense that they come with provable statistical guarantees of having low risk without any assumptions on the world model whatsoever; the observations need not be I.I.D. and can even be adversarial. The theory extends results from conformal prediction to calibrate decisions directly, without requiring the construction of prediction sets. Experiments demonstrate the utility of our approach in robot motion planning around humans, automated stock trading, and robot manufacturing.

Abstract-- Current motion planning approaches rely on binary collision checking to evaluate the validity of a state and thereby dictate where the robot is allowed to move. This approach leaves little room for robots to engage in contact with an object, as is often necessary when operating in densely cluttered spaces. This allows a safety constraint of collision-free paths, as it ensures minimal robot to consider paths that would be discarded by traditional physical interaction with the environment that could lead to motion planning techniques while increasing success rate robot error states or damage. However, this principle is oftentimes and enabling the robot to explore the environment through too limiting, as environmental constraints (e.g. More specifically, in this paper we introduce a motion narrow field of view, sensor inaccuracies), and operational planner, Contact Admissible Transition-based Rapidly exploring constraints (e.g. As a result, a robot manipulator will robot through states of admissible contact, which we define likely fail to reach into a cluttered space due to the minimal as contact necessary to reach the goal configuration.

When facing a new motion-planning problem, most motion planners solve it from scratch, e.g., via sampling and exploration or starting optimization from a straight-line path. However, most motion planners have to experience a variety of planning problems throughout their lifetimes, which are yet to be leveraged for future planning. In this paper, we present a simple but efficient method called Motion Memory, which allows different motion planners to accelerate future planning using past experiences. Treating existing motion planners as either a closed or open box, we present a variety of ways that Motion Memory can contribute to reduce the planning time when facing a new planning problem. We provide extensive experiment results with three different motion planners on three classes of planning problems with over 30,000 problem instances and show that planning speed can be significantly reduced by up to 89% with the proposed Motion Memory technique and with increasing past planning experiences.

The objective is to find the optimal state vector that minimizes the measurement error between the estimated pose and environmental landmarks. Most SLAM algorithms are passive, i.e., the robot is controlled manually and the navigation or path planning algorithm does not actively take part in robot motion or trajectory. Active SLAM (A-SLAM), however, tries to solve the optimal exploration problem of the unknown environment by proposing a navigation strategy that generates future goal/target positions actions which decrease map and pose uncertainties, thus enabling a fully autonomous navigation and mapping SLAM system without the need of an external controller or human effort. In Active Collaborative SLAM (AC-SLAM) multiple robots interchange information to improve their localization estimation and map accuracy to achieve some high-level tasks such as exploration. The exchanged information can be localization information [1], entropy [2], visual features [3], and frontier points [4]. In this article, we present a multi-agent AC-SLAM system for efficient environment exploration using frontiers detected over an Occupancy Grid (OG) map. In particular, in this work, we aim at: 1. Extending the A-SLAM approach of [5] which uses a computationally inexpensive D-optimality criterion for utility computation to a multi-agent AC-SLAM framework.

This article presents a comprehensive review of control algorithms used in mobile robotics, a field in constant evolution. Mobile robotics has seen significant advances in recent years, driven by the demand for applications in various sectors, such as industrial automation, space exploration, and medical care. The review focuses on control algorithms that address specific challenges in navigation, localization, mapping, and path planning in changing and unknown environments. Classical approaches, such as PID control and methods based on classical control theory, as well as modern techniques, including deep learning and model-based planning, are discussed in detail. In addition, practical applications and remaining challenges in implementing these algorithms in real-world mobile robots are highlighted. Ultimately, this review provides a comprehensive overview of the diversity and complexity of control algorithms in mobile robotics, helping researchers and practitioners to better understand the options available to address specific problems in this exciting area of study.

The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is crucial for intelligent robots. In this work, we introduce $\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics simulation to efficiently transfer robotic skills. Specifically, $\textit{Diff-Transfer}$ discovers a feasible path within the task space that brings the source task to the target task. At each pair of adjacent points along this task path, which is two sub-tasks, $\textit{Diff-Transfer}$ adapts known actions from one sub-task to tackle the other sub-task successfully. The adaptation is guided by the gradient information from differentiable physics simulations. We propose a novel path-planning method to generate sub-tasks, leveraging $Q$-learning with a task-level state and reward. We implement our framework in simulation experiments and execute four challenging transfer tasks on robotic manipulation, demonstrating the efficacy of $\textit{Diff-Transfer}$ through comprehensive experiments. Supplementary and Videos are on the website https://sites.google.com/view/difftransfer