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Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (1 April 2022) | Viewed by 30661

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Special Issue Editors

Prof. Dr. Reza Ghabcheloo

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Guest Editor

Faculty of Engineering and Natural Sciences, Tampere University, P.O. Box 1001, 33014, Finland
Interests: optimal control; motion control; estimation; robot learning

Dr. Antonio M. Pascoal

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Guest Editor

Institute for Systems and Robotics (ISR), Instituto Superior Tecnico (IST), Torre Norte, Piso 8 Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal
Interests: dynamical systems; control theory; marine robotics; marine technology

Special Issue Information

Dear Colleagues,

Fast paced developments in the fields of aerial, land, and marine robotics have given impetus to a wide spectrum of scientific and commercial applications with far reaching societal implications. Autonomous robots equipped with advanced sensors and manipulators afford humans the capability to operate seamlessly in remote and hazardous environments, as if they were extensions of our eyes and hands. Robots have become the tools par excellence for scientists and commercial operators to explore and monitor the state of heterogenous environments on Earth, inspect wave and energy offshore infrastructures, monitor the growth of crops, and transport goods, among a myriad of other activities. Groups of robots acting in cooperation have started to impact the development of multiple system platforms for adaptive environmental sampling, search and rescue operations in hard to access regions, and even coordinate image-taking in the movie and sports industries. The types of robots used are highly heterogeneous and cater to specific user-defined requirements for operations in air, on land, and at sea. Notwithstanding this diversity, they have a number of attributes in common that are key to their capability to explore or act upon the environment with great agility while exhibiting high levels of performance, resilience, adaptability, and safety.

It is against this backdrop of ideas that in this Special Issue we address fundamental problems that, in our opinion, are at the root of the development of a new breed of heterogenous robots that can act in isolation or cooperatively towards the execution of a wide spectrum of mission scenarios. Among such issues, we highlight the following: study of single and cooperative motion planning methods with a view to meeting temporal and energy constraints in the presence of robot dynamic constraints, while taking the topology of the underlying communication network and intervehicle and vehicle-obstacle avoidance requisites into account; creation of safe and emergent behaviours in a distributed manner, both at the motion planning and control levels, incorporating event-driven communication strategies to try and reduce the amount of information exchanged among the different agents; study of new methods to solve constrained optimal control problems efficiently in a receding horizon fashion; development of effective techniques for adaptive and robust control in the presence of plant model uncertainty of partially known models, especially for safety critical systems, using tools at the crossroads of reinforcement learning and feedback control; study of how advanced perception can be applied for the reformulation of the above problems in a sensor-based context, yielding challenging questions in the area of visual- and acoustic-based servoing, object tracking, and obstacle detection and avoidance.

Prof. Dr. Reza Ghabcheloo
Prof. Dr. Antonio M. Pascoal
Guest Editors

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Keywords

motion optimization, trajectory planning
trajectory tracking, path following
networked systems, distributed control
control and optimization under geometric and dynamical constraints
sensor-based control, visual servoing, tracking, collision avoidance
model predictive control
adaptive and robust control
control and barrier Lyapunov function methods
reinforcement learning and feedback control

Published Papers (14 papers)

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Editorial

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6 pages, 207 KiB

Open AccessEditorial

Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots

by Reza Ghabcheloo and António Pascoal

Sensors 2023, 23(1), 87; https://doi.org/10.3390/s23010087 - 22 Dec 2022

Cited by 1 | Viewed by 1066

Abstract

Fast-paced developments in the fields of aerial, land, and marine robotics are steadily paving the way for a wide spectrum of scientific and commercial applications of autonomous vehicles with far-reaching societal implications [...] Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

Research

Jump to: Editorial

18 pages, 1632 KiB

Open AccessArticle

Defense against Adversarial Swarms with Parameter Uncertainty

by Claire Walton, Isaac Kaminer, Qi Gong, Abram H. Clark and Theodoros Tsatsanifos

Sensors 2022, 22(13), 4773; https://doi.org/10.3390/s22134773 - 24 Jun 2022

Cited by 6 | Viewed by 1380

Abstract

This paper addresses the problem of optimal defense of a high-value unit (HVU) against a large-scale swarm attack. We discuss multiple models for intra-swarm cooperation strategies and provide a framework for combining these cooperative models with HVU tracking and adversarial interaction forces. We show that the problem of defending against a swarm attack can be cast in the framework of uncertain parameter optimal control. We discuss numerical solution methods, then derive a consistency result for the dual problem of this framework, providing a tool for verifying computational results. We also show that the dual conditions can be computed numerically, providing further computational utility. Finally, we apply these numerical results to derive optimal defender strategies against a 100-agent swarm attack. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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19 pages, 1864 KiB

Open AccessArticle

Robust Nonlinear Tracking Control with Exponential Convergence Using Contraction Metrics and Disturbance Estimation

by Pan Zhao, Ziyao Guo and Naira Hovakimyan

Sensors 2022, 22(13), 4743; https://doi.org/10.3390/s22134743 - 23 Jun 2022

Cited by 4 | Viewed by 1473

Abstract

This paper presents a tracking controller for nonlinear systems with matched uncertainties based on contraction metrics and disturbance estimation that provides exponential convergence guarantees. Within the proposed approach, a disturbance estimator is proposed to estimate the pointwise value of the uncertainties, with a pre-computable estimation error bounds (EEB). The estimated disturbance and the EEB are then incorporated in a robust Riemannian energy condition to compute the control law that guarantees exponential convergence of actual state trajectories to desired ones. Simulation results on aircraft and planar quadrotor systems demonstrate the efficacy of the proposed controller, which yields better tracking performance than existing controllers for both systems. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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39 pages, 11290 KiB

Open AccessArticle

A Path-Following Controller for Marine Vehicles Using a Two-Scale Inner-Outer Loop Approach

by Pramod Maurya, Helio Mitio Morishita, Antonio Pascoal and A. Pedro Aguiar

Sensors 2022, 22(11), 4293; https://doi.org/10.3390/s22114293 - 05 Jun 2022

Cited by 6 | Viewed by 2382

Abstract

This article addresses the problem of path following of marine vehicles along straight lines in the presence of currents by resorting to an inner-outer control loop strategy, with due account for the presence of currents. The inner-outer loop control structures exhibit a fast-slow temporal scale separation that yields simple “rules of thumb” for controller tuning. Stated intuitively, the inner-loop dynamics should be much faster than those of the outer loop. Conceptually, the procedure described has three key advantages: (i) it decouples the design of the inner and outer control loops, (ii) the structure of the outer-loop controller does not require exact knowledge of the vehicle dynamics, and (iii) it provides practitioners a very convenient method to effectively implement path-following controllers on a wide range of vehicles. The path-following controller discussed in this article is designed at the kinematic outer loop that commands the inner loop with the desired heading angles while the vehicle moves at an approximately constant speed. The key underlying idea is to provide a seamless implementation of path-following control algorithms on heterogeneous vehicles, which are often equipped with heading autopilots. To this end, we assume that the heading control system is characterized in terms of an IOS-like relationship without detailed knowledge of vehicle dynamics parameters. This paper quantitatively evaluates the combined inner-outer loop to obtain a relationship for assessing the combined system’s stability. The methods used are based on nonlinear control theory, wherein the cascade and feedback systems of interest are characterized in terms of their IOS properties. We use the IOS small-gain theorem to obtain quantitative relationships for controller tuning that are applicable to a broad range of marine vehicles. Tests with AUVs and one ASV in real-life conditions have shown the efficacy of the path-following control structure developed. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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17 pages, 4774 KiB

Open AccessArticle

Visual Servoed Autonomous Landing of an UAV on a Catamaran in a Marine Environment

by Andrea Delbene, Marco Baglietto and Enrico Simetti

Sensors 2022, 22(9), 3544; https://doi.org/10.3390/s22093544 - 06 May 2022

Cited by 4 | Viewed by 1752

Abstract

This paper introduces a procedure for autonomous landing of a quadrotor on an unmanned surface vehicle in a marine environment. The relative pose and velocity of the vehicle with respect to the quadrotor are estimated using a combination of data coming from a vision system, which recognizes a set of AprilTags located on the vehicle itself, and an ultrasonic sensor, to achieve further robustness during the final landing phase. The considered software and hardware architecture is provided, and the details about the landing procedure are presented. Software-in-the-loop tests were performed as a validation step for the proposed algorithms; to recreate realistic conditions, the movements of the landing platform have been replicated from data of a test in a real marine environment. In order to provide further proof of the reliability of the vision system, a video sequence from a manual landing of a quadrotor on the surface vehicle in a real marine environment has been processed, and the results are presented. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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13 pages, 17868 KiB

Open AccessArticle

Fast Adaptation of Manipulator Trajectories to Task Perturbation by Differentiating through the Optimal Solution

by Shashank Srikanth, Mithun Babu, Houman Masnavi, Arun Kumar Singh, Karl Kruusamäe and Krishnan Madhava Krishna

Sensors 2022, 22(8), 2995; https://doi.org/10.3390/s22082995 - 13 Apr 2022

Cited by 1 | Viewed by 1155

Abstract

Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations, such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. Most importantly, however, our algorithm achieves a worst-case speed-up of 160x over the latter approach. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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29 pages, 2984 KiB

Open AccessArticle

A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

by Henrik Andreasson, Jonas Larsson and Stephanie Lowry

Sensors 2022, 22(7), 2588; https://doi.org/10.3390/s22072588 - 28 Mar 2022

Cited by 4 | Viewed by 2705

Abstract

This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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31 pages, 16402 KiB

Open AccessArticle

Chemical Spill Encircling Using a Quadrotor and Autonomous Surface Vehicles: A Distributed Cooperative Approach

by Marcelo Jacinto, Rita Cunha and António Pascoal

Sensors 2022, 22(6), 2178; https://doi.org/10.3390/s22062178 - 10 Mar 2022

Cited by 1 | Viewed by 2103

Abstract

This article addresses the problem of formation control of a quadrotor and one (or more) marine vehicles operating at the surface of the water with the end goal of encircling the boundary of a chemical spill, enabling such vehicles to carry and release chemical dispersants used during ocean cleanup missions to break up oil molecules. Firstly, the mathematical models of the Medusa class of marine robots and quadrotor aircrafts are introduced, followed by the design of single vehicle motion controllers that allow these vehicles to follow a parameterised path individually using Lyapunov-based techniques. At a second stage, a distributed controller using event-triggered communications is introduced, enabling the vehicles to perform cooperative path following missions according to a pre-defined geometric formation. In the next step, a real-time path planning algorithm is developed that makes use of a camera sensor, installed on-board the quadrotor. This sensor enables the detection in the image of which pixels encode parts of a chemical spill boundary and use them to generate and update, in real time, a set of smooth B-spline-based paths for all the vehicles to follow cooperatively. The performance of the complete system is evaluated by resorting to 3-D simulation software, making it possible to visually simulate a chemical spill. Results from real water trials are also provided for parts of the system, where two Medusa vehicles are required to perform a static lawn-mowing path following mission cooperatively at the surface of the water. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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41 pages, 30147 KiB

Open AccessArticle

Bernstein Polynomial-Based Method for Solving Optimal Trajectory Generation Problems

by Calvin Kielas-Jensen, Venanzio Cichella, Thomas Berry, Isaac Kaminer, Claire Walton and Antonio Pascoal

Sensors 2022, 22(5), 1869; https://doi.org/10.3390/s22051869 - 27 Feb 2022

Cited by 9 | Viewed by 3376

Abstract

This paper presents a method for the generation of trajectories for autonomous system operations. The proposed method is based on the use of Bernstein polynomial approximations to transcribe infinite dimensional optimization problems into nonlinear programming problems. These, in turn, can be solved using off-the-shelf optimization solvers. The main motivation for this approach is that Bernstein polynomials possess favorable geometric properties and yield computationally efficient algorithms that enable a trajectory planner to efficiently evaluate and enforce constraints along the vehicles’ trajectories, including maximum speed and angular rates as well as minimum distance between trajectories and between the vehicles and obstacles. By virtue of these properties and algorithms, feasibility and safety constraints typically imposed on autonomous vehicle operations can be enforced and guaranteed independently of the order of the polynomials. To support the use of the proposed method we introduce BeBOT (Bernstein/Bézier Optimal Trajectories), an open-source toolbox that implements the operations and algorithms for Bernstein polynomials. We show that BeBOT can be used to efficiently generate feasible and collision-free trajectories for single and multiple vehicles, and can be deployed for real-time safety critical applications in complex environments. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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21 pages, 1489 KiB

Open AccessArticle

A Distributed Algorithm for Real-Time Multi-Drone Collision-Free Trajectory Replanning

by Bahareh Sabetghadam, Rita Cunha and António Pascoal

Sensors 2022, 22(5), 1855; https://doi.org/10.3390/s22051855 - 26 Feb 2022

Cited by 9 | Viewed by 2358

Abstract

In this paper, we present a distributed algorithm to generate collision-free trajectories for a group of quadrotors flying through a common workspace. In the setup adopted, each vehicle replans its trajectory, in a receding horizon manner, by solving a small-scale optimization problem that only involves its own individual variables. We adopt the Voronoi partitioning of space to derive local constraints that guarantee collision avoidance with all neighbors for a certain time horizon. The obtained set of collision avoidance constraints explicitly takes into account the vehicle’s orientation to avoid infeasiblity issues caused by ignoring the quadrotor’s rotational motion. Moreover, the resulting constraints can be expressed as Bézier curves, and thus can be evaluated efficiently, without discretization, to ensure that collision avoidance requirements are satisfied at any time instant, even for an extended planning horizon. The proposed approach is validated through extensive simulations with up to 100 drones. The results show that the proposed method has a higher success rate at finding collision-free trajectories for large groups of drones compared to other Voronoi diagram-based methods. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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31 pages, 7176 KiB

Open AccessArticle

Over-Actuated Underwater Robots: Configuration Matrix Design and Perspectives

by Tho Dang, Lionel Lapierre, Rene Zapata, Benoit Ropars and Pascal Lepinay

Sensors 2021, 21(22), 7729; https://doi.org/10.3390/s21227729 - 20 Nov 2021

Cited by 5 | Viewed by 1675

Abstract

In general, for the configuration designs of underwater robots, the positions and directions of actuators (i.e., thrusters) are given and installed in conventional ways (known points, vertically, horizontally). This yields limitations for the capability of robots and does not optimize the robot’s resources such as energy, reactivity, and versatility, especially when the robots operate in confined environments. In order to optimize the configuration designs in the underwater robot field focusing on over-actuated systems, in the paper, performance indices (manipulability, energetic, reactive, and robustness indices) are introduced. The multi-objective optimization problem was formulated and analyzed. To deal with different objectives with different units, the goal-attainment method, which can avoid the difficulty of choosing a weighting vector to obtain a good balance among these objectives, was selected to solve the problem. A solution design procedure is proposed and discussed. The efficiency of the proposed method was proven by simulations and experimental results. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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27 pages, 3477 KiB

Open AccessArticle

Universal Path-Following of Wheeled Mobile Robots: A Closed-Form Bounded Velocity Solution

by Reza Oftadeh, Reza Ghabcheloo and Jouni Mattila

Sensors 2021, 21(22), 7642; https://doi.org/10.3390/s21227642 - 17 Nov 2021

Cited by 2 | Viewed by 2697

Abstract

This paper presents a nonlinear, universal, path-following controller for Wheeled Mobile Robots (WMRs). This approach, unlike previous algorithms, solves the path-following problem for all common categories of holonomic and nonholonomic WMRs, such as omnidirectional, unicycle, car-like, and all steerable wheels. This generality is the consequence of a two-stage solution that tackles separately the platform path-following and wheels’ kinematic constraints. In the first stage, for a mobile platform divested of the wheels’ constraints, we develop a general paradigm of a path-following controller that plans asymptotic paths from the WMR to the desired path and, accordingly, we derive a realization of the presented paradigm. The second stage accounts for the kinematic constraints imposed by the wheels. In this stage, we demonstrate that the designed controller simplifies the otherwise impenetrable wheels’ kinematic and nonholonomic constraints into explicit proportional functions between the velocity of the platform and that of the wheels. This result enables us to derive a closed-form trajectory generation scheme for the asymptotic path that constantly keeps the wheels’ steering and driving velocities within their corresponding, pre-specified bounds. Extensive experimental results on several types of WMRs, along with simulation results for the other types, are provided to demonstrate the performance and the efficacy of the method. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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23 pages, 3751 KiB

Open AccessArticle

Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions

by Myron Papadimitrakis, Marios Stogiannos, Haralambos Sarimveis and Alex Alexandridis

Sensors 2021, 21(21), 6959; https://doi.org/10.3390/s21216959 - 20 Oct 2021

Cited by 11 | Viewed by 2579

Abstract

The field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed using a model predictive controller (MPC) that makes use of obstacle ship trajectory prediction models built on the RBF framework and is trained on real AIS data sourced from an open-source database. The usage of such sophisticated trajectory prediction models enables the controller to correctly infer the existence of a collision risk and apply evasive control actions in a timely manner, thus accounting for the slow dynamics of a large vessel, such as container ships, and enhancing the cooperation between controlled vessels. The proposed method is evaluated on a real-life case from the Miami port area, and its generated trajectories are assessed in terms of safety, economy, and COLREG compliance by comparison with an identical MPC controller utilizing straight-line predictions for the obstacle vessel. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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12 pages, 4091 KiB

Open AccessArticle

An Improved Rapidly-Exploring Random Trees Algorithm Combining Parent Point Priority Determination Strategy and Real-Time Optimization Strategy for Path Planning

by Lijing Tian, Zhizhuo Zhang, Change Zheng, Ye Tian, Yuchen Zhao, Zhongyu Wang and Yihan Qin

Sensors 2021, 21(20), 6907; https://doi.org/10.3390/s21206907 - 18 Oct 2021

Cited by 7 | Viewed by 2005

Abstract

In order to solve the problems of long path planning time and large number of redundant points in the rapidly-exploring random trees algorithm, this paper proposed an improved algorithm based on the parent point priority determination strategy and the real-time optimization strategy to optimize the rapidly-exploring random trees algorithm. First, in order to shorten the path-planning time, the parent point is determined before generating a new point, which eliminates the complicated process of traversing the random tree to search the parent point when generating a new point. Second, a real-time optimization strategy is combined, whose core idea is to compare the distance of a new point, its parent point, and two ancestor points to the target point when a new point is generated, choosing the new point that is helpful for the growth of the random tree to reduce the number of redundant points. Simulation results of 3-dimensional path planning showed that the success rate of the proposed algorithm, which combines the strategy of parent point priority determination and the strategy of real-time optimization, was close to 100%. Compared with the rapidly-exploring random trees algorithm, the number of points was reduced by more than 93.25%, the path planning time was reduced by more than 91.49%, and the path length was reduced by more than 7.88%. The IRB1410 manipulator was used to build a test platform in a laboratory environment. The path obtained by the proposed algorithm enables the manipulator to safely avoid obstacles to reach the target point. The conclusion can be made that the proposed strategy has a better performance on optimizing the success rate, the number of points, the planning time, and the path length. Full article

(This article belongs to the Special Issue Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots)

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