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We devise a cooperative planning framework to generate optimal trajectories for a tethered robot duo, who is tasked to gather scattered objects spread in a large area using a flexible net. Specifically, the proposed planning framework first produces a set of dense waypoints for each robot, serving as the initialization for optimization. Next, we formulate an iterative optimization scheme to generate smooth and collision-free trajectories while ensuring cooperation within the robot duo to efficiently gather objects and properly avoid obstacles. We validate the generated trajectories in simulation and implement them in physical robots using Model Reference Adaptive Controller (MRAC) to handle unknown dynamics of carried payloads. In a series of studies, we find that: (i) a U-shape cost function is effective in planning cooperative robot duo, and (ii) the task efficiency is not always proportional to the tethered net's length. Given an environment configuration, our framework can gauge the optimal net length. To our best knowledge, ours is the first that provides such estimation for tethered robot duo.

The roll-out phase of the next generation of mobile networks (5G) has started and operators are required to devise deployment solutions while pursuing localization accuracy maximization. Enabling location-based services is expected to be a unique selling point for service providers now able to deliver critical mobile services, e.g., autonomous driving, public safety, remote operations. In this paper, we propose a novel roll-out base station placement solution that, given a Throughput-Positioning Ratio (TPR) target, selects the location of new-generation base stations (among available candidate sites) such that the throughput and localization accuracy are jointly maximized. Moving away from the canonical position error bound (PEB) analysis, we develop a realistic framework in which each positioning measurement is affected by errors depending upon the actual wireless channel between the measuring base station and the target device. Our solution, referred to as LOKO, is a fast-converging algorithm that can be readily applied to current 5G (or future) roll-out processes. LOKO is validated by means of an exhaustive simulation campaign considering real existing deployments of a major European network operator as well as synthetic scenarios.

This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. Furthermore, we account for the uncertainty of the robot's partial state by utilizing the Unscented Transform and the uncertainty of the neural network model by using Monte Carlo dropout. The uncertainty-aware collision cost is then combined with the goal direction given by a global planner in order to determine the best action sequence to execute in a receding horizon manner. To demonstrate the method, we develop a resilient small flying robot integrating lightweight sensing and computing resources. A set of simulation and experimental studies, including a field deployment, in both cluttered and perceptually-challenging environments is conducted to evaluate the quality of the prediction network and the performance of the proposed planner.

Rapidly-exploring random tree (RRT) has been applied for autonomous parking due to quickly solving high-dimensional motion planning and easily reflecting constraints. However, planning time increases by the low probability of extending toward narrow parking spots without collisions. To reduce the planning time, the target tree algorithm was proposed, substituting a parking goal in RRT with a set (target tree) of backward parking paths. However, it consists of circular and straight paths, and an autonomous vehicle cannot park accurately because of curvature-discontinuity. Moreover, the planning time increases in complex environments; backward paths can be blocked by obstacles. Therefore, this paper introduces the continuous-curvature target tree algorithm for complex parking environments. First, a target tree includes clothoid paths to address such curvature-discontinuity. Second, to reduce the planning time further, a cost function is defined to construct a target tree that considers obstacles. Integrated with optimal-variant RRT and searching for the shortest path among the reached backward paths, the proposed algorithm obtains a near-optimal path as the sampling time increases. Experiment results in real environments show that the vehicle more accurately parks, and continuous-curvature paths are obtained more quickly and with higher success rates than those acquired with other sampling-based algorithms.

The guiding task of a mobile robot requires not only human-aware navigation, but also appropriate yet timely interaction for active instruction. State-of-the-art tour-guide models limit their socially-aware consideration to adapting to users' motion, ignoring the interactive behavior planning to fulfill the communicative demands. We propose a multi-behavior planning framework based on Monte Carlo Tree Search to better assist users to understand confusing scene contexts, select proper paths and timely arrive at the destination. To provide proactive guidance, we construct a sampling-based probability model of human motion to consider the interrelated effects between robots and humans. We validate our method both in simulation and real-world experiments along with performance comparison with state-of-the-art models.

Multi-robot bounding overwatch requires timely coordination of robot team members. Symbolic motion planning (SMP) can provide provably correct solutions for robot motion planning with high-level temporal logic task requirements. This paper aims to develop a framework for safe and reliable SMP of multi-robot systems (MRS) to satisfy complex bounding overwatch tasks constrained by temporal logics. A decentralized SMP framework is first presented, which guarantees both correctness and parallel execution of the complex bounding overwatch tasks by the MRS. A computational trust model is then constructed by referring to the traversability and line of sight of robots in the terrain. The trust model predicts the trustworthiness of each robot team's potential behavior in executing a task plan. The most trustworthy task and motion plan is explored with a Dijkstra searching strategy to guarantee the reliability of MRS bounding overwatch. A robot simulation is implemented in ROS Gazebo to demonstrate the effectiveness of the proposed framework.

Several solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDC-UAV. The primary goal of EDC-UAV is to build inflight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collision-free mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.

This paper focuses on a research problem of robotic controlled laser orientation to minimize errant overcutting of healthy tissue during the course of pathological tissue resection. Laser scalpels have been widely used in surgery to remove pathological tissue targets such as tumors or other lesions. However, different laser orientations can create various tissue ablation cavities, and incorrect incident angles can cause over-irradiation of healthy tissue that should not be ablated. This work aims to formulate an optimization problem to find the optimal laser orientation in order to minimize the possibility of excessive laser-induced tissue ablation. We first develop a 3D data-driven geometric model to predict the shape of the tissue cavity after a single laser ablation. Modelling the target and non-target tissue region by an obstacle boundary, the determination of an optimal orientation is converted to a collision-minimization problem. The goal of this optimization formulation is maintaining the ablated contour distance from the obstacle boundary, which is solved by Projected gradient descent. Simulation experiments were conducted and the results validated the proposed method with conditions of various obstacle shapes and different initial incident angles.

We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, a machine learning approach to search-based planning is still challenging due to the discrete nature of search algorithms. In this work, we reformulate a canonical A* search algorithm to be differentiable and couple it with a convolutional encoder to form an end-to-end trainable neural network planner. Neural A* solves a path planning problem by encoding a problem instance to a guidance map and then performing the differentiable A* search with the guidance map. By learning to match the search results with ground-truth paths provided by experts, Neural A* can produce a path consistent with the ground truth accurately and efficiently. Our extensive experiments confirmed that Neural A* outperformed state-of-the-art data-driven planners in terms of the search optimality and efficiency trade-off, and furthermore, successfully predicted realistic human trajectories by directly performing search-based planning on natural image inputs.

Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing - Query Planning - is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries.We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including DBpedia, YAGO, and Freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.