NEPTUNE: Non-Entangling Planning for Multiple Tethered Unmanned Vehicles

Despite recent progress on trajectory planning of multiple robots and path planning of a single tethered robot, planning of multiple tethered robots to reach their individual targets without entanglements remains a challenging problem. In this paper, we present a complete approach to address this problem. Firstly, we propose a multi-robot tether-aware representation of homotopy, using which we can efficiently evaluate the feasibility and safety of a potential path in terms of (1) the cable length required to reach a target following the path, and (2) the risk of entanglements with the cables of other robots. Then, the proposed representation is applied in a decentralized and online planning framework that includes a graph-based kinodynamic trajectory finder and an optimization-based trajectory refinement, to generate entanglement-free, collision-free and dynamically feasible trajectories. The efficiency of the proposed homotopy representation is compared against existing single and multiple tethered robot planning approaches. Simulations with up to 8 UAVs show the effectiveness of the approach in entanglement prevention and its real-time capabilities. Flight experiments using 3 tethered UAVs verify the practicality of the presented approach.Comment: Accepted for publication in IEEE Transaction on Robotic

An integrated localization-navigation scheme for distance-based docking of UAVs

In this paper we study the distance-based docking problem of unmanned aerial vehicles (UAVs) by using a single landmark placed at an arbitrarily unknown position. To solve the problem, we propose an integrated estimation-control scheme to simultaneously achieve the relative localization and navigation tasks for discrete-time integrators under bounded velocity: a nonlinear adaptive estimation scheme to estimate the relative position to the landmark, and a delicate control scheme to ensure both the convergence of the estimation and the asymptotic docking at the given landmark. A rigorous proof of convergence is provided by invoking the discrete-time LaSalle's invariance principle, and we also validate our theoretical findings on quadcopters equipped with ultra-wideband ranging sensors and optical flow sensors in a GPS-less environment

SPINS: Structure Priors aided Inertial Navigation System

Although Simultaneous Localization and Mapping (SLAM) has been an active research topic for decades, current state-of-the-art methods still suffer from instability or inaccuracy due to feature insufficiency or its inherent estimation drift, in many civilian environments. To resolve these issues, we propose a navigation system combing the SLAM and prior-map-based localization. Specifically, we consider additional integration of line and plane features, which are ubiquitous and more structurally salient in civilian environments, into the SLAM to ensure feature sufficiency and localization robustness. More importantly, we incorporate general prior map information into the SLAM to restrain its drift and improve the accuracy. To avoid rigorous association between prior information and local observations, we parameterize the prior knowledge as low dimensional structural priors defined as relative distances/angles between different geometric primitives. The localization is formulated as a graph-based optimization problem that contains sliding-window-based variables and factors, including IMU, heterogeneous features, and structure priors. We also derive the analytical expressions of Jacobians of different factors to avoid the automatic differentiation overhead. To further alleviate the computation burden of incorporating structural prior factors, a selection mechanism is adopted based on the so-called information gain to incorporate only the most effective structure priors in the graph optimization. Finally, the proposed framework is extensively tested on synthetic data, public datasets, and, more importantly, on the real UAV flight data obtained from a building inspection task. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications.Comment: 14 pages, 14 figure

CapsuleBot: A Novel Compact Hybrid Aerial-Ground Robot with Two Actuated-wheel-rotors

This paper presents the design, modeling, and experimental validation of CapsuleBot, a compact hybrid aerial-ground vehicle designed for long-term covert reconnaissance. CapsuleBot combines the manoeuvrability of bicopter in the air with the energy efficiency and noise reduction of ground vehicles on the ground. To accomplish this, a structure named actuated-wheel-rotor has been designed, utilizing a sole motor for both the unilateral rotor tilting in the bicopter configuration and the wheel movement in ground mode. CapsuleBot comes equipped with two of these structures, enabling it to attain hybrid aerial-ground propulsion with just four motors. Importantly, the decoupling of motion modes is achieved without the need for additional drivers, enhancing the versatility and robustness of the system. Furthermore, we have designed the full dynamics and control for aerial and ground locomotion based on the bicopter model and the two-wheeled self-balancing vehicle model. The performance of CapsuleBot has been validated through experiments. The results demonstrate that CapsuleBot produces 40.53% less noise in ground mode and consumes 99.35% less energy, highlighting its potential for long-term covert reconnaissance applications.Comment: 7 pages, 10 figures, submitted to 2024 IEEE International Conference on Robotics and Automation (ICRA). This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Multi-robot sweep coverage and motion planning

Multi-robot systems have received increasing attention from both research community and engineering practitioners due to their broad application scenarios. In particular, multiple robots can be deployed to cover a target region to conduct quality inspection, surveillance, search and rescue. This thesis focuses on two important aspects of multi-robot coverage problem: high-level coverage planning, which aims to partition the target region or allocate coverage workloads among the robots; and low-level motion planning, which aims to generate smooth trajectory for each robot that avoids the static and dynamic obstacles. For high-level coverage planning, we focus on the problem of sweep coverage over a region with uneven and unknown workload distribution. Uneven workload distribution means that a robot has to spend different amounts of time covering a unit area at different locations in the region. Unknown workload distribution necessitates the online allocation of workload among the robots. To tackle this problem, we adopt the formulation in which the entire region is separated into multiple stripes, and a discrete-time distributed workload allocation algorithm is used to allocate workload on the stripe to each robot. The convergence of the distributed workload allocation algorithm to the optimal workload assignment is established, error bounds between the actual completion time and the optimal completion time are derived. Furthermore, we consider the scenario when the robots have limited workload sensing ranges and propose a new algorithm to address this scenario with proven stability. Realistic simulations and actual flight experiments using UAVs are carried out to demonstrate the practicality and validate the theoretical results. The research described above aims to achieve optimal workload assignments among the robots, which do not guarantee optimal coverage completion time. As a further step towards the time-optimal coverage planning problem, we propose an improved workload partition algorithm and prove that the operation time on each stripe converges to the minimum under the discrete-time update law. We conduct comprehensive simulation studies and compare our method with the existing methods to verify the theoretical results and the advantage of the proposed method. Flight experiments on mini drones are also conducted to demonstrate the practicality of the proposed algorithm. While the high-level planning module generates target positions or moving directions for the robots, the low-level motion planning module generates dynamically feasible and collision-free trajectories leading to the target position. To build a reliable motion planning module for multiple robots, we propose a differential dynamic programming (DDP) based method for polynomial trajectory generation for differentially flat systems. In particular, we take a new perspective from state-space representation and convert the constrained trajectory generation problem (both with and without time optimization) to a discrete-time finite-horizon optimal control problem with inequality constraints. The proposed method is combined with a decentralized multi-robot planning framework to generate safe and dynamically feasible trajectories for the robots to follow. Both numerical comparisons with state-of-the-art methods and physical experiments are presented to verify and validate the effectiveness of our theoretical findings. Since a coverage mission may last for an extended period of time, tethered systems are commonly employed to extend the working duration of mobile robots. Hence, based on the above research, we further investigate the motion planning problem of multiple tethered robots and present a complete approach to address this problem. Firstly, we present a procedure to set up a multi-robot tether-aware representation of homotopy, using which we can efficiently evaluate the feasibility and safety of a potential path in terms of (1) the cable length required to reach a target following the path, and (2) the risk of entanglements with the cables of other robots. Then, the proposed representation is applied in the decentralized planning framework to generate entanglement-free, collision-free and dynamically feasible trajectories. The efficiency of the proposed homotopy representation is shown in comparison with existing single and multiple tethered robot planning approaches. Simulation and flight experiments demonstrate the effectiveness of the approach in entanglement prevention and real-time implementation.Doctor of Philosoph

A Novel RPL Algorithm Based on Chaotic Genetic Algorithm

RPL (routing protocol for low-power and lossy networks) is an important candidate routing algorithm for low-power and lossy network (LLN) scenarios. To solve the problems of using a single routing metric or no clearly weighting distribution theory of additive composition routing metric in existing RPL algorithms, this paper creates a novel RPL algorithm according to a chaotic genetic algorithm (RPL-CGA). First of all, we propose a composition metric which simultaneously evaluates packet queue length in a buffer, end-to-end delay, residual energy ratio of node, number of hops, and expected transmission count (ETX). Meanwhile, we propose using a chaotic genetic algorithm to determine the weighting distribution of every routing metric in the composition metric to fully evaluate candidate parents (neighbors). Then, according to the evaluation results of candidate parents, we put forward a new holistic objective function and a new method for calculating the rank values of nodes which are used to select the optimized node as the preferred parent (the next hop). Finally, theoretical analysis and a series of experimental consequences indicate that RPL-CGA is significantly superior to the typical existing relevant routing algorithms in the aspect of average end-to-end delay, average success rate, etc

DIRECT: a differential dynamic programming based framework for trajectory generation

This letter introduces a differential dynamic programming (DDP) based framework for polynomial trajectory generation for differentially flat systems. In particular, instead of using a linear equation with increasing size to represent multiple polynomial segments as in literature, we take a new perspective from state-space representation such that the linear equation reduces to a finite horizon control system with a fixed state dimension and the required continuity conditions for consecutive polynomials are automatically satisfied. Consequently, the constrained trajectory generation problem (both with and without time optimization) can be converted to a discrete-time finite-horizon optimal control problem with inequality constraints, which can be approached by a recently developed interior-point DDP (IPDDP) algorithm. Furthermore, for unconstrained trajectory generation with preallocated time, we show that this problem is indeed a linear-quadratic tracking (LQT) problem (DDP algorithm with exact one iteration). All these algorithms enjoy linear complexity with respect to the number of segments. Both numerical comparisons with state-of-the-art methods and physical experiments are presented to verify and validate the effectiveness of our theoretical findings. The implementation code will be open-sourced. [Online] Available: https://github.com/ntu-caokun/DIRECTNational Research Foundation (NRF)This work was supported by the National Research Foundation, Singapore under its Medium Sized Center for Advanced Robotics Technology Innovation