Autonomous Underwater Vehicles (AUVs) encounter significant challenges in confined spaces like ports and testing tanks, where vehicle-environment interactions, such as wave reflections and unsteady flows, introduce complex, time-varying disturbances. Model-based state estimation methods can struggle to handle these dynamics, leading to localization errors. To address this, we propose a data-driven velocity estimation approach using Inertial Measurement Units (IMUs) and a Gated Recurrent Unit (GRU) neural network, capturing temporal dependencies and rejecting external disturbances. This velocity estimator is then integrated into a sensor fusion framework using an asynchronous Kalman filter to improve localization by fusing on-board and off-board sensor information. Experimental validation on miniature AUVs demonstrates the effectiveness of the proposed method in enhancing accuracy for velocity and position estimation in environments with significant disturbances due to interactions between the vehicle and the environment.

Existing indoor SLAM datasets primarily focus on robot sensing, often lacking building architectures. To address this gap, we design and construct the first dataset to couple the SLAM and BIM, named SLABIM. This dataset provides BIM and SLAM -oriented sensor data, both modeling a university building at HKUST. The as-designed BIM is decomposed and converted for ease of use. We employ a multi-sensor suite for multi-session data collection and mapping to obtain the as-built model. All the related data are timestamped and organized, enabling users to deploy and test effectively. Furthermore, we deploy advanced methods and report the experimental results on three tasks: registration, localization and semantic mapping, demonstrating the effectiveness and practicality of SLAB 1M. We make our dataset open-source at https://github.com/HKUST-Aerial-Robotics/SLABIM.

Image-goal navigation enables a robot to reach the location where a target image was captured, using visual cues for guidance. However, current methods either rely heavily on data and computationally expensive learning-based approaches or lack efficiency in complex environments due to insufficient exploration strategies. To address these limitations, we propose Bayesian Embodied Image-goal Navigation Using Gaussian Splatting, a novel method that formulates ImageNav as an optimal control problem within a model predictive control framework. BEINGS leverages 3D Gaussian Splatting as a scene prior to predict future observations, enabling efficient, real-time navigation decisions grounded in the robot's sensory experiences. By integrating Bayesian updates, our method dynamically refines the robot's strategy without requiring extensive prior experience or data. Our algorithm is validated through extensive simulations and physical experiments, showcasing its potential for embodied robot systems in visually complex scenarios. Project Page: www.mwg.ink/BEINGS-web.

Formation control is essential for swarm robotics, enabling coordinated behavior in complex environments. In this paper, we introduce a novel formation control system for an indoor blimp swarm using a specialized leader-follower approach enhanced with a dynamic leader-switching mechanism. This strategy allows any blimp to take on the leader role, distributing maneuvering demands across the swarm and enhancing overall formation stability. Only the leader blimp is manually controlled by a human operator, while follower blimps use onboard monocular cameras and a laser altimeter for relative position and altitude estimation. A leader-switching scheme is proposed to assist the human operator to maintain stability of the swarm, especially when a sharp turn is performed. Experimental results confirm that the leader-switching mechanism effectively maintains stable formations and adapts to dynamic indoor environments while assisting human operator.

In this study, we present a novel simultaneous localization and mapping (SLAM) system, VIMS, designed for underwater navigation. Conventional visual-inertial state estimators encounter significant practical challenges in perceptually degraded underwater environments, particularly in scale estimation and loop closing. To address these issues, we first propose leveraging a low-cost single-beam sonar to improve scale estimation. Then, VIMS integrates a high-sampling-rate magnetometer for place recognition by utilizing magnetic signatures generated by an economical magnetic field coil. Building on this, a hierarchical scheme is developed for visual-magnetic place recognition, enabling robust loop closure. Furthermore, VIMS achieves a balance between local feature tracking and descriptor-based loop closing, avoiding additional computational burden on the front end. Experimental results highlight the efficacy of the proposed VIMS, demonstrating significant improvements in both the robustness and accuracy of state estimation within underwater environments.

This letter presents the design of a single-input-single-output (SISO) underwater acoustic (UWA) short video communication system for quasi-real-time marine observation, which employing 64-QAM (Quadrature Amplitude Modulation) to yield a peak data rate of 15 kbps and an adaptive decision feedback equalizer (DA-DFE) with embedded phase-locked loop (PLL) to mitigate multipath interference. Meanwhile, Polar channel encoding and MPEG-4 video compression encoding guarantee the communication performance. Two field tests conducted in different UWA channels demonstrated that the proposed system enables single-element UWA communication of 3.5-second short video clip over a distance of 150 meters with an end-to-end delay on the order of minute. This capability has the potential to be utilized for Internet of Underwater Things (IoUT)-driven quasi-real-time marine observation.

Light detection and ranging (LiDAR) point clouds and building information modeling (BIM) represent two distinct data modalities in the fields of robot perception and construction. These modalities originate from different sources and are associated with unique reference frames. The primary goal of this study is to align these modalities within a shared reference frame using a global registration approach, effectively enabling them to “speak the same language”. To achieve this, we propose a cross-modality registration method, spanning from the front end to the back end. At the front end, we extract triangle descriptors by identifying walls and intersected corners, enabling the matching of corner triplets with a complexity independent of the BIM’s size. For the back-end transformation estimation, we utilize the Hough transform to map the matched triplets to the transformation space and introduce a hierarchical voting mechanism to hypothesize multiple pose candidates. The final transformation is then verified using our designed occupancy-aware scoring method. To assess the effectiveness of our approach, we conducted real-world multi-session experiments in a large-scale university building, employing two different types of LiDAR sensors. We make the collected datasets and codes publicly available to benefit the community. Note to Practitioners—Our proposed registration method leverages walls and corners as shared features between LiDAR and BIM data, making it particularly well-suited for scenarios with well-defined structural layouts. Accumulating a larger LiDAR submap provides richer structural information, which further aids in achieving accurate alignment. To optimize computational efficiency, we recommend constructing the descriptor database offline and loading it during runtime, enabling a theoretical retrieval complexity of$O(1)$. Despite its advantages, our approach has certain limitations. First, it primarily focuses on planar structures, which limits its effectiveness in utilizing non-planar features. Second, the method may underperform in cases where significant deviations exist between the as-designed BIM and as-is LiDAR data. Lastly, in ambiguous scenarios, such as long corridors or similar layouts within the same or across different floors, our method may struggle to verify the correct transformation among candidates. To address these challenges, incorporating additional information, particularly semantic cues such as floor numbers, room numbers, and room types, could enhance its robustness and reliability.

The challenging problem of single-hydrophone underwater acoustic direction of arrival (DOA) estimation draws significant attention from diverse underwater drone applications due to its small size requirement and low hardware overhead, addressing which from the perspective of acoustic metamaterial retains a frontier. In this paper, an acoustic metamaterial shell with multiple pore-cavity structure is randomly designed to generate anisotropic direction-dependent frequency modulation (DDFM) effect, which enables single-hydrophone collaborative DOA estimation by exploring the sparsity of spatial target distribution and the pre-known DDFM pattern. To mitigate the deterioration of DDFM effect caused by multipath underwater acoustic channel, a metamaterial-assisted multipath decoupling (MAMD) sparse recovery method is further developed. Specifically, contribution of multipath channel response is decomposed into time delay and amplitude components, which are then recombined with pre-know DDFM pattern matrix and sparse direction vector, respectively, to achieve DOA estimation via sparse recovery algorithm. Numerical simulations and lake experiments onboard autonomous underwater vehicle (AUV) demonstrate that the proposed method reduces the root mean square error (RMSE) by approximately 7.8296° and improves the estimated success rate (SR) by about 7.4% compared to traditional array-based techniques.

Opinions of individuals in large groups evolve through interactions with neighbors and the environment, which can be modeled with opinion dynamics. In this paper, we propose a distributed opinion-based strategy for large-scale multi-robot task allocation utilizing the convergence behaviors of opinion dynamics. The strategy relies on the specialized opinion dynamics on the unit sphere for robot task selection. We investigate the convergence behaviors of opinion dynamics in the context of regions of attraction. Simulation results with a swarm of 200 homogeneous robots validate the effectiveness of our proposed strategy.

This article addresses cooperative target enclosing of underactuated autonomous surface vehicles (ASVs) subject to obstacles. Each ASV suffers from input constraints in addition to unknown kinetics induced by model nonlinearities, unknown input gains, and external disturbances. A safety-critical cooperative target enclosing control method is proposed for surrounding a maneuvering target vehicle. Specifically, a finite-time fuzzy predictor is presented to learn the unknown kinetics with the integral of historical vehicle data. By using a distributed target estimator to recover the target position, a nominal distributed target enclosing control law is developed to achieve a circumnavigation formation. To avoid collisions between ASVs and obstacles/team members, input-to-state safe high-order control barrier functions are first introduced for encoding safety constraints. Based on the safety constraints and input constraints, a quadratic programming problem is formulated, and an optimal safety-critical control law is obtained by using projection neural networks to track the optimal solution. The closed-loop control system is proven to be input-to-state stable via Lyapunov theory. Moreover, the multiple ASV systems are proven to be input-to-state safe regardless of high-order relative degree. The salient contributions of the proposed approach lie in finite-time fuzzy learning and collision-free target enclosing control under disturbances. Simulation results validate the effectiveness of the proposed safety-critical model-free control method for cooperatively surrounding a maneuvering target.

Effective task allocation is an essential component to the coordination of heterogeneous robots. This paper proposes a hybrid task allocation algorithm that improves upon given initial solutions, for example from the popular decentralized market-based allocation algorithm, via a derivative-free optimization strategy called Speeding-Up and Slowing-Down (SUSD). Based on the initial solutions, SUSD performs a search to find an improved task assignment. Unique to our strategy is the ability to apply a gradient-like search to solve a classical integer-programming problem. The proposed strategy outperforms other state-of-the-art algorithms in terms of total task utility and can achieve near optimal solutions in simulation. Experimental results using the Robotarium are also provided.

This paper introduces a game-theoretical approach to the multi-robot task allocation problem, where each robot is considered as self-interested and cannot share its personal utility functions. We consider the case where each robot can execute multiple tasks and each task requires only one robot. For real-world applications with mobile robots, we design a utility function that includes both assignment conflict penalties and path-dependent execution cost. For a robot to maximize its own utility, it needs to select a subset of conflict-free tasks that minimizes its total travel distance. Our approaches utilize a consensus communication scheme to share robots' task selection and the Speeding-Up and Slowing-Down (SUSD) strategy to search in a combinatorial action (task selection) space for a subset of tasks that can achieve a higher utility at each iteration. The SUSD strategy can perform a gradient-like search without calculating the derivatives, which allows robots to improve upon their current task selections. Simulation results show that robots using the proposed algorithms can successfully find Nash equilibria for effective coordination.

Distributed Data Fusion (DDF), as a prevalent technique that empowers scalable, flexible, and robust information fusing, has been employed in various multi-sensor networks operating in uncertain and dynamic environments. This paper proposes a cognition difference-based mechanism to construct a dynamic trust network for real-time DDF, where the cognition difference is defined as the statistical difference between the sensors' estimated probability distributions. Distinguished by the mutual correlation between trust and cognition difference, two principles of determining trust are investigated, and their performances are analyzed by conducting simulations in the scenarios of source seeking. Our simulation and experiment results show that the proposed approach is effective in providing comprehensive and robust performance in general and unstructured environments.

We propose a memory-constrained partition-based method to extract symbolic representations of the belief state and its dynamics in order to solve planning problems in a partially observable Markov decision process (POMDP). Our K-means partitioning strategy uses a fixed number of symbols to represent the partitions of the belief space and ensures the parameterization of the belief dynamics does not grow exponentially as the system dimension increases. By casting our problem as a partitioning of the POMDP, we can then solve planning problems using traditional symbolic planning solvers (such as HTN or A* solvers). Our work is motivated by an autonomous underwater vehicle navigation problem where the vehicle is affected by uncertain flow conditions and receives severely limited position observations. Simulation experiments are provided to validate the performance of the proposed algorithms.

Swing oscillation is widely observed among indoor miniature autonomous blimps (MABs) due to their underactuated design and unique aerodynamic shape. A detailed dynamics model is critical for investigating this undesired movement and designing controllers to stabilize the oscillation. This paper presents a motion model that describes the coupled translational and rotational movements of a typical indoor MAB with saucer-shaped envelope. The kinematics and dynamic model of the MAB are simplified from the six-degrees-of-freedom (6-DOF) Newton-Euler equations of underwater vehicles. The model is then reduced to 3-DOF given the symmetrical design of the MAB around its vertical axis. Parameters of the motion model are estimated from the system identification experiments, and validated with experimental data.

This paper proposes a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field. The algorithm is based on a bio-inspired approach known as Speeding-Up and Slowing-Down (SUSD) for distributed source seeking problems. Our algorithm leverages a Gaussian Process model to predict field values as robots explore. By comparing Gaussian Process predictions with measurements of the field, agents search along the gradient of the model error while simultaneously improving the Gaussian Process model. We provide a proof of convergence to the gradient direction and demonstrate our approach in simulation and experiments using 2D wheeled robots and 2D flying autonomous miniature blimps.

This paper considers a group of mobile sensing agents in a flow field and presents a distributed method for motion tomography (MT) that estimates the underlying flow field. MT formulates an underdetermined nonlinear system of equations as an inverse problem. Inspired by the Kaczmarz method which is an optimization approach for solving a linear system of equations, our previous work developed a nonlinear Kaczmarz method that solves the system of equations associated with MT. Considering distributed multi-agent systems for MT, this paper extends the nonlinear Kaczmarz method into a distributed framework. The distributed nonlinear Kaczmarz method is developed by formulating a constrained consensus problem that belongs to a class of projected consensus algorithms. To study the convergence and consensus for the method, its linear case is analyzed first and then its nonlinear case is discussed. The nonlinear case of the method is further validated through simulations by estimating a gyre flow field using mobile sensor networks with different numbers of neighboring agents. Resulting estimated flow fields are compared with a flow field estimated by its centralized counterpart.

We present the Georgia Tech Miniature Autonomous Blimp (GT-MAB), which is designed to support human-robot interaction experiments in an indoor space for up to two hours. GT-MAB is safe while flying in close proximity to humans. It is able to detect the face of a human subject, follow the human, and recognize hand gestures. GT-MAB employs a deep neural network based on the single shot multibox detector to jointly detect a human user’s face and hands in a real-time video stream collected by the onboard camera. A human-robot interaction procedure is designed and tested with various human users. The learning algorithms recognize two hand waving gestures. The human user does not need to wear any additional tracking device when interacting with the flying blimp. Vision-based feedback controllers are designed to control the blimp to follow the human and fly in one of two distinguishable patterns in response to each of the two hand gestures. The blimp communicates its intentions to the human user by displaying visual symbols. The collected experimental data show that the visual feedback from the blimp in reaction to the human user significantly improves the interactive experience between blimp and human. The demonstrated success of this procedure indicates that GT-MAB could serve as a flying robot that is able to collect human data safely in an indoor environment.

Indoor miniature autonomous blimp (MAB) is a small-sized aerial platform with outstanding safety and flight endurance. A detailed six-degree-of-freedom (6DOF) dynamics model is critical for controller design and motion simulation. This paper presents the identification of the rotation-related parameters of the blimp dynamics model through swing motion of the robot. A pendulum-like grey box model is constructed to identify the parameters from physical measurements and system identification experiments. The pendulum-like dynamics model with identified parameters is then linearized for future controller design and validated with experimental data.

Micro-sized autonomous underwater vehicles (μAUVs) are well suited to various applications in confined underwater spaces. Acoustic communication is required for many application scenarios of μAUVs to enable data transmission without surfacing. This paper presents the integration of a compact acoustic communication device with a μAUV prototype. Packet reception rate (PRR) and bit error rate (BER) of the acoustic communication link are evaluated in a confined pool environment through experiments while the μAUV is either stationary or moving. We pinpoint several major factors that impact the communication performance. Experimental results show that the multi-path effect significantly affects the synchronization signals of the communication device. The relative motion between the vehicle and the base station also degrades the communication performance. These results suggest future methods towards improvements.

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We present an approach that allows the Georgia Tech Miniature Autonomous Blimp (GT-MAB) to detect and follow a human. This accomplishment is the first Human Robot Interaction (HRI) demonstration between an uninstrumented human and a robotic blimp. GT-MAB is an ideal platform for HRI missions because it is safe to humans and can support sufficient flight time for HRI experiments. However, due to complex aerodynamic influence on the blimp, the human following task for GT-MAB with a single on-board camera is a challenging problem. We integrate Haar face detector and KLT feature tracker to achieve robust human tracking. After a human face is detected in the real-time video stream, we estimated the 3D positions of the human with respect to GT-MAB. Visionbased PID controllers are designed based on estimated relative position and the motion primitives of GT-MAB such that it can achieve stable and continuous human following behavior. Experimental results are presented to demonstrate the human following capability on GT-MAB.

We develop a plume tracking algorithm for a swarm of mobile sensing agents in turbulent flow. Inspired by blue crabs, we propose a stochastic model for plume spikes based on the Poisson counting process, which captures the turbulent characteristic of plumes. We then propose an approach to estimate the parameters of the spike model, and transform the turbulent plume field detected by sensing agents into a smoother scalar field that shares the same source with the plume field. This transformation allows us to design path planning algorithms for mobile sensing agents in the smoother field instead of in the turbulent plume field. Inspired by the source seeking behaviors of fish schools, we design a velocity controller for each mobile agent by decomposing the velocities into two perpendicular parts: the forward velocity incorporates feedback from the estimated spike parameters, and the side velocity keeps the swarm together. The combined velocity is then used to plan the path for each agent in the swarm. Theoretical justifications are provided for convergence of the agent group to the plume source. The algorithms are also demonstrated through simulations.

This article establishes a novel analytical approach to quantify robustness of scheduling and battery management for battery supported cyber-physical systems. A dynamic schedulability test is introduced to determine whether tasks are schedulable within a finite time window. The test is used to measure robustness of a real-time scheduling algorithm by evaluating the strength of computing time perturbations that break schedulability at runtime. Robustness of battery management is quantified analytically by an adaptive threshold on the state of charge. The adaptive threshold significantly reduces the false alarm rate for battery management algorithms to decide when a battery needs to be replaced.

A class of path following and formation controllers are implemented on marine robots performing autonomous surveys in regions polluted by crude oil during the Deepwater Horizon oil spill. The controllers enable the robots to follow lines and curves, and maintain formation collectively while measuring reminiscent crude oil along their paths. The controllers are mathematically sound with proven convergence and robustness. However, their performance in the surveying missions is affected by natural disturbances caused by wind and water currents, and constraints such as sensor inaccuracy, localization errors, and network delays. This paper evaluates the performance of our controllers based on data collected during a survey performed at Grand Isle, Louisiana. These results will provide guidance for mission designs and inspire the future developments of our marine robots used to perform autonomous environmental surveys.

A gliding robotic fish is developed for promising applications in aquatic environment monitoring. The design concept combines the strengths of both underwater gliders and robotic fish, featuring long operation duration and high maneuverability. This paper presents both analytical and experimental results for the three-dimension spiraling motion, an essential working pattern of the gliding fish for detecting pollution in a water column. A dynamic model of the gliding robotic fish with actuated tail is established. Then the steady-state spiraling equations are derived and solved recursively using Newton's method. The gliding fish prototype is tested in experiments. Both model prediction and experimental results show that the spiraling motion has very low energy consumption, and the gliding fish can achieve high maneuverability with a turning radius less than 1 m, 2.5% of the reported turning radius of a typical underwater glider.

Biological inspirations have lead us to develop a switching strategy for a group of robotic sensing agents searching for a local minimum of an unknown noisy scalar field. Starting with individual exploration, the agents switch to cooperative exploration only when they are not able to converge to a local minimum at a satisfying rate. We derive a cooperativeH∞filter that provides estimates of field values and field gradients during cooperative exploration and give sufficient conditions for the convergence and feasibility of the filter. The switched behavior from individual exploration to cooperative exploration results in faster convergence, which is rigorously justified by the Razumikhin theorem, to a local minimum. We propose that the switching condition from cooperative exploration to individual exploration is triggered by a significantly improved signal-to-noise ratio (SNR) during cooperative exploration. In addition to theoretical and simulation studies, we develop a multiagent testbed and implement the switching strategy in a lab environment. We have observed consistency of theoretical predictions and experimental results, which are robust to unknown noises and communication delays.

We design a switching strategy for a group of robots to search for a local minimum of an unknown noisy scalar field. Starting with individual exploration, the robots switch to cooperative exploration only when they are not able to locate the field minimum based on the information collected individually. In order to test and demonstrate the switching strategy in real-world environment, we implement the switching strategy on a multi-robot test-bed. The behaviors of a group of robots are compared when different parameters for exploration are adopted. Especially, we observe the effect of memory lengths on the switching behaviors as predicted by theoretical results. The experimental results also justify the effects of different formation sizes and noise attenuation levels on the performance of the cooperative H∞filter that are utilized in the cooperative exploration phase.

This paper presents a novel robust controller design for formation control of autonomous underwater vehicles (AUVs). We consider a nonlinear three-degree-of-freedom dynamic model for the horizontal motion of each AUV. By using the Jacobi transform, the horizontal dynamics of AUVs are explicitly expressed as dynamics for formation shape and formation center, and are further decoupled by feedback control. We treat the coupling terms as perturbations to the decoupled system. An H∞. state feedback controller is designed to achieve robust stability of the closed loop formation and translation dynamics. By incorporating an orientation controller, the formation shape under control converges and the formation center tracks a desired trajectory simultaneously. Simulation results demonstrate the effectiveness of the controllers.

Underwater Gliders have found broad applications in ocean sampling. In this paper, the nonlinear dynamic model of the glider developed by the Shenyang Institute of Automation, Chinese Academy of Sciences, is established. Based on this model, we solve for the parameters that characterize steady state spiraling motions of the glider. A set of nonlinear equations are simplified so that a recursive algorithm can be used to find the solutions.

This paper establishes the method of controlled Lagrangian particle tracking (CLPT) to analyse the offsets between physical positions of marine robots in the ocean and simulated positions of controlled particles in an ocean model. This offset (which we term CLPT error) has characteristics that are not previously seen in free-drifting ocean sampling platforms with no active control. CLPT error growth over time is exponential until it reaches a turning point that depends only on the resolution of the ocean model, after which the error growth is bounded by polynomial functions of time. In the ideal case, a theoretical lower bound on the steady-state CLPT error can be derived. These characteristics are proved theoretically for particles moving in a planar flow field. The method of CLPT may be applied to improve the accuracy of ocean circulation models and navigation performance of marine robots.

This paper presents a novel approach based on Jacobi shape theory and geometric reduction for formation control of autonomous underwater vehicles (AUVs). We consider a three degree-of-freedom (DOF) dynamic model for the horizontal motion of each AUV that has control inputs over surge force and yaw moment. By using the Jacobi transform, the horizontal dynamics of AUVs are expressed as dynamics for formation shape, formation motion and vehicle orientation. The system decouples when additional symmetries in vehicle design are presented. Hence formation shape controllers, formation motion controllers, and vehicle orientation controllers can be designed separately. This approach reduces the complexity of formation controllers. We use the model for ODIN as an example to demonstrate the controller design process. Simulation results show the effectiveness of the controllers.

We design sensing algorithms and a control law for a group of mobile robots to follow a boundary curve without utilizing a global positioning system (GPS). The sensing algorithms allow each robot to estimate the shape and the orientation of the entire formation from readings of range sensors and a speedometer. The usage of GPS is avoided because each robot is able to estimate the relative position of the entire formation with respect to the boundary curve. Based on these estimates, we present a control law that allows the robot formation to achieve desired non-singular shape while following the boundary curve. We control the distance between the center of mass of the formation and the boundary curve so that it converges to desired value. Our control law also guarantees that there will be no collision between any pair of robots and no collision between any robot and the boundary curve.

The wide applications of cyber-physical systems (CPS) call for effective design strategies that optimize the performance of both computing units and physical plants.We study the task scheduling problem for a class of CPS whose behaviors are regulated by feedback control laws. We co-design the control law and the task scheduling algorithm for predictable performance and power consumption for both the computing and the physical systems. We use a typical example, multiple inverted pendulums controlled by one processor, to illustrate our method.

We prove a convergent strategy for a group of mobile sensors to generate contour plots, i.e., to automatically detect and track level curves of a scalar field in the plane. The group can consist of as few as four mobile sensors, where each sensor can take only a single measurement at a time. The shape of the formation of mobile sensors is determined to minimize the least mean square error in the estimates of the scalar field and its gradient. The algorithm to generate a contour plot is based on feedback control laws for each sensor platform. The control laws serve two purposes: to guarantee that the center of the formation moves along one level curve at unit speed; and to stabilize the shape of the formation. We prove that both goals can be achieved asymptotically. We show simulation results that illustrate the performance of the control laws in noisy environments.

A novel curvature-based steering control law is introduced to produce obstacle avoidance behavior for unicycle type robots traveling (flying) at constant speed. Different methods of curvature estimation from noisy range data are explored and compared via experiments. The performance of the obstacle avoidance algorithm is investigated through simulations and experiments.

Formations that contain a small number of robots are modeled as controlled Lagrangian systems on Jacobi shape space. This allows a blocked decoupled control for position, orientation and shape of the formation. Feedback control laws are derived using control Lyapunov functions. The controlled dynamics converges to the invariant set where desired shape is achieved. Controllers are implemented in a layered fashion via the extended motion description language (MDLe) system. Group MDLe plans are constructed to allow structured controller design for formations.

The original paper of Su and Stepanenko (ibid., vol.14, p.651-5, 1998) presents the design of an adaptive/robust controller for uncertain electrically-driven robots with no velocity measurements. This note shows that the claim that velocity measurements are not required for control implementation is incorrect.

This paper presents the design of a differentiable, kinematic control law that achieves global asymptotic tracking. In addition, we also illustrate how the proposed kinematic controller provides global exponential tracking provided the reference trajectory satisfies a mild persistency of excitation (PE) condition. We also illustrate how the proposed kinematic controller can be slightly modified to provide for global asymptotic regulation of both the position and orientation of the mobile robot. Finally, we embed the differentiable kinematic controller inside of an adaptive controller that fosters global asymptotic tracking despite parametric uncertainty associated with the dynamic model. Experimental results are also provided to illustrate the performance of the proposed adaptive tracking controller.

We consider the problem of designing a boundary controller for a flexible link robot arm with a payload mass at the link's free-end. Specifically, we utilize a nonlinear, hybrid dynamic system model (the model is hybrid in the sense that it comprises a distributed parameter, dynamic field equation coupled to discrete, dynamic boundary equations) to design a model-based control law which asymptotically stabilizes the link displacement while driving the actuator hub's position to a desired setpoint. We then illustrate how the control law can be redesigned as an adaptive controller which achieves the same control objective while compensating for parametric uncertainty including unknown payload mass. The control strategy is composed of a boundary control torque applied to the actuator hub and a boundary control force at the link's free-end. Experimental results are presented to illustrate the performance of the proposed control laws.