Autonomous mobile robots (AMRs) equipped with high-quality cameras are revolutionizing the field of autonomous photography by delivering efficient and cost-effective methods for capturing dynamic visual content. As AMRs are deployed in increasingly diverse environments, the challenge of consistently producing high-quality photographic content remains. Traditional approaches often involve AMRs following a predetermined path while capturing data-intensive imagery, which can be suboptimal, especially in environments with limited connectivity or physical obstructions. These drawbacks necessitate intelligent decision-making to pinpoint optimal vantage points for image capture. Inspired by Next Best View studies, we propose a novel autonomous photography framework that enhances image quality and minimizes the number of photos needed. This framework incorporates a proposed evaluation metric that leverages ray-tracing and Gaussian process inter-polation, enabling the assessment of potential visual information from the target in partially known environments. A derivative-free optimization (DFO) method is then proposed to sample candidate views and identify the optimal viewpoint. The effectiveness of our approach is demonstrated by comparing it with existing methods and further validated through simulations and experiments with various vehicles. Note–Code and videos of the simulations and experiments are provided in the supplementary material and can be accessed at https://www.bezzorobotics.com/sg-lb-icra25.

Motion planning failures during autonomous navigation often occur when safety constraints are either too conservative, leading to deadlocks, or too liberal, resulting in collisions. To improve robustness, a robot must dynamically adapt its safety constraints to ensure it reaches its goal while balancing safety and performance measures. To this end, we propose a Soft Actor-Critic (SAC)-based policy for adapting Control Barrier Function (CBF) constraint parameters at runtime, ensuring safe yet non-conservative motion. The proposed approach is designed for a general high-level motion planner, low-level controller, and target system model, and is trained in simulation only. Through extensive simulations and physical experiments, we demonstrate that our framework effectively adapts CBF constraints, enabling the robot to reach its final goal without compromising safety.

Humans interacting with robots often form predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might predict that the car is going to remain in the same lane. It is important for the robot to understand the human’s prediction for safe and seamless interaction: e.g., if the autonomous car knows the human thinks it is not merging — but the autonomous car actually intends to merge — then the car can adjust its behavior to prevent an accident. Prior works typically assume that humans make precise predictions of robot behavior. However, recent research on human-human prediction suggests the opposite: humans tend to approximate other agents by predicting their high-level behaviors. We apply this finding to develop a second-order theory of mind approach that enables robots to estimate how humans predict they will behave. To extract these high-level predictions directly from data, we embed the recent human and robot trajectories into a discrete latent space. Each element of this latent space captures a different type of behavior (e.g., merging in front of the human, remaining in the same lane) and decodes into a vector field across the state space that is consistent with the underlying behavior type. We hypothesize that our resulting high-level and course predictions of robot behavior will correspond to actual human predictions. We provide initial evidence in support of this hypothesis through proof-of-concept simulations, testing our method’s predictions against those of real users, and experiments on a real-world interactive driving dataset.

This paper presents a novel theory of mind (ToM)-based approach for implicit coordination of multi robot systems (MRS) in environments where direct communication is unavailable. The proposed approach integrates higher-order reasoning, epistemic theory, and active inference to coordinate the actions of each robot to clarify their own intentions and make them understandable to other robots. Further, to reduce the computational overhead of higher-order reasoning, we implement a large language model (LLM)-based attention selection mechanism that focuses on a subset of robots. Simulations and physical experiments demonstrate the applicability of the proposed approach with high success rates while significantly reducing computation complexity.

For autonomous mobile robots, uncertainties in the environment and system model can lead to failure in the motion planning pipeline, resulting in potential collisions. In order to achieve a high level of robust autonomy, these robots should be able to proactively predict and recover from such failures. To this end, we propose a Gaussian Process (GP) based model for proactively detecting the risk of future motion planning failure. When this risk exceeds a certain threshold, a recovery behavior is triggered that leverages the same GP model to find a safe state from which the robot may continue towards the goal. The proposed approach is trained in simulation only and can generalize to real world environments on different robotic platforms. Simulations and physical experiments demonstrate that our framework is capable of both predicting planner failures and recovering the robot to states where planner success is likely, all while producing agile motion.

Localization is one of the most important tasks for mobile robot operations. Without such capability, a robot may wander toward unsafe states and never complete a desired task. Such capability is even more important in multi-robot system (MRS) operations in which their motion is coordinated based on consensus schemes that leverage information from surrounding neighbors. Thus, in the event of compromised or malfunctioning on-board positioning sensing (e.g., due to cyber attacks or faults) on individual robots, the entire robotic system may be hijacked toward undesired states. In this work, we target this problem by proposing a decentralized framework where: i) robots with loss of localization capabilities detect the anomalous behavior then generate a notification signal within information exchanges to alert neighboring robots, and ii) neighboring robots leverage their mobility to aid in recovery allowing compromised robots to re-localize. Our framework is validated in simulations and lab experiments on proximity-based formations of homogeneous unmanned multi-robot swarms.

As Multi-Robot Systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring, underwater inspections, or space exploration. However, accounting for potential communication loss or the unavailability of communication infrastructures in these application domains remains an open problem. Much of the applicable MRS research assumes that the system can sustain communication through proximity regulations and formation control or by devising a framework for separating and adhering to a predetermined plan for extended periods of disconnection. The latter technique enables an MRS to be more efficient, but breakdowns and environmental uncertainties can have a domino effect throughout the system, particularly when the mission goal is intricate or time-sensitive. To deal with this problem, our proposed framework has two main phases: i) a centralized planner to allocate mission tasks by rewarding intermittent rendezvous between robots to mitigate the effects of the unforeseen events during mission execution, and ii) a decentralized replanning scheme leveraging epistemic planning to formalize belief propagation and a Monte Carlo tree search for policy optimization given distributed rational belief updates. The proposed framework outperforms a baseline heuristic and is validated using simulations and experiments with aerial vehicles.

A common challenge for mobile robots is traversing uncertain environments containing obstacles, rough terrain, or hazards. Without full knowledge of the environment, an unmanned ground vehicle (UGV) navigating towards a goal could easily drive down a path that is blocked (requiring the robot to retrace sections of its path) or run into a hazard causing a catastrophic failure. To address this issue we propose a system of systems (SoS) abstraction to group a distributed set of robots into a single system. Specifically, we propose augmenting the sensing capabilities of a UGV using an unmanned aerial vehicle (UAV). With different dynamic and sensing capabilities, the UAV scouts ahead and proactively updates the plan for the UGV using information discovered about the environment. To predict reachable states of the UGV, the UAV employs a sampling-based method in which a set of virtual particles representing simulated instances of the UGV are used to approximate the distribution of possible trajectories. The UAV assesses if the current UGV path plan is inefficient or unsafe, and if so, provides an alternative path to the UGV. For robustness, a model predictive path integral (MPPI) optimization method is used to modify the waypoints when delivered to the UGV. The strategy is validated in simulation and experimentally.Note—Videos of the simulations and experiments are provided in the supplementary material and can be accessed at: https://www.bezzorobotics.com/ps-iros24.

In communication restricted environments, a multi-robot system can be deployed to either: i) maintain constant communication but potentially sacrifice operational efficiency due to proximity constraints or ii) allow disconnections to increase environmental coverage efficiency, challenges on how, when, and where to reconnect (rendezvous problem). In this work we tackle the latter problem and notice that most state-of-the-art methods assume that robots will be able to execute a predetermined plan; however system failures and changes in environmental conditions can cause the robots to deviate from the plan with cascading effects across the multi-robot system. This paper proposes a coordinated epistemic prediction and planning framework to achieve consensus without communicating for exploration and coverage, task discovery and completion, and rendezvous applications. Dynamic epistemic logic is the principal component implemented to allow robots to propagate belief states and empathize with other agents. Propagation of belief states and subsequent coverage of the environment is achieved via a frontier-based method within an artificial physics-based framework. The proposed framework is validated with both simulations and experiments with unmanned ground vehicles in various cluttered environments.

In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles.

Current motion planning approaches for autonomous mobile robots often assume that the low level controller of the system is able to track the planned motion with very high accuracy. In practice, however, tracking error can be affected by many factors, and could lead to potential collisions when the robot must traverse a cluttered environment. To address this problem, this paper proposes a novel receding-horizon motion planning approach based on Model Predictive Path Integral (MPPI) control theory – a flexible sampling-based control technique that requires minimal assumptions on vehicle dynamics and cost functions. This flexibility is leveraged to propose a motion planning framework that also considers a data-informed risk function. Using the MPPI algorithm as a motion planner also reduces the number of samples required by the algorithm, relaxing the hardware requirements for implementation. The proposed approach is validated through trajectory generation for a quadrotor unmanned aerial vehicle (UAV), where fast motion increases trajectory tracking error and can lead to collisions with nearby obstacles. Simulations and hardware experiments demonstrate that the MPPI motion planner proactively adapts to the obstacles that the UAV must negotiate, slowing down when near obstacles and moving quickly when away from obstacles, resulting in a complete reduction of collisions while still producing lively motion.

Autonomous mobile robots (AMR) operating in the real world often need to make critical decisions that directly impact their own safety and the safety of their surroundings. Learning-based approaches for decision making have gained popularity in recent years, since decisions can be made very quickly and with reasonable levels of accuracy for many applications. These approaches, however, typically return only one decision, and if the learner is poorly trained or observations are noisy, the decision may be incorrect. This problem is further exacerbated when the robot is making decisions about its own failures, such as faulty actuators or sensors and external disturbances, when a wrong decision can immediately cause damage to the robot. In this paper, we consider this very case study: a robot dealing with such failures must quickly assess uncertainties and make safe decisions. We propose an uncertainty aware learning-based failure detection and recovery approach, in which we leverage Decision Tree theory along with Model Predictive Control to detect and explain which failure is compromising the system, assess uncertainties associated with the failure, and lastly, find and validate corrective controls to recover the system. Our approach is validated with simulations and real experiments on a faulty unmanned ground vehicle (UGV) navigation case study, demonstrating recovery to safety under uncertainties.

Agile navigation through uncertain and obstacle-rich environments remains a challenging task for autonomous mobile robots (AMR). For most AMR, obstacles are identified using onboard sensors, e.g., lidar or cameras. The effectiveness of these sensors may be severely limited, however, by occlusions introduced from the presence of other obstacles. The occluded area may contain obstacles, static or dynamic, not included into the motion planning of the robot and could cause potential collisions if they suddenly appear in the field of view of the robot. This paper proposes a general Model Predictive Control (MPC)-based framework for handling occlusions in structured or unstructured environments, that contain known or unknown static or dynamic obstacles. Safety is promoted by commanding velocities that consider surrounding obstacle uncertainty, while perception is promoted through a specially designed objective that can reduce the occluded area created by obstacles. The effectiveness of this framework is validated through simulations that show swift and safe motion in a variety of different environments. Similarly, experimental validation is achieved with a Boston Dynamics' Spot quadruped robot operating in an occluding environment.

Cyber-attacks, failures, and implementation errors inside the controller of an autonomous system can affect its correct behavior leading to unsafe states and degraded performance. In this paper, we focus on such problems specifically on cyber-attacks that manipulate controller parameters like the gains in a feedback controller or that triggers different behaviors or block inputs based on specific values of the state and tracking error. If such attacks are undetected, they can lead to the partial or complete loss of system's control authority, resulting in a hijacking and leading the autonomous system towards unforeseen states. To deal with this problem, we propose a runtime monitoring and recovery scheme in which: 1) we leverage the residual between the expected and the received measurements to detect inconsistencies in the generated inputs and 2) provide a recovery method for counteracting the malicious effects to allow for resilient operations by manipulating the reference signal and state vector provided to the system to avoid the affected regions in the state and error space. We validate our approach with Matlab simulations and experiments on unmanned ground vehicles resiliently performing operations in the presence of malicious attacks to on-board controllers.

The lack of communication between agents in a multi-robot system is often regarded as a limiting factor that can affect and delay cooperative exploration and exploitation of cluttered and uncertain environments. On the contrary, this paper proposes a complete planning framework to enable cooperative behavior without the need for constant communication between robots, demonstrating drastic improvements in task completion and coverage time as compared to both fully connected robotic networks and widely used frontier-based exploration methods. Specifically, the proposed scheme considers three behaviors: i) exploration, promoting separation and disconnection, ii) rendezvous to reconnect and share information gained during exploration, and iii) task allocation for prioritized objectives. Exploration is achieved via a Sobel edge detection frontier algorithm that enables navigation of unknown complex (both convex and non-convex) environments. Once a task is discovered, a multi-objective weighted sum optimization method is proposed for allocating tasks based on prioritization and expectation estimation. The utility, generality, and scalability of the proposed approach is demonstrated using extensive simulations and experiments with unmanned ground vehicles in various cluttered environments.

This paper addresses a safe planning and control problem for mobile robots operating in communication- and sensor-limited dynamic environments. In this case the robots cannot sense the objects around them and must instead rely on intermittent, external information about the environment, as e.g., in underwater applications. The challenge in this case is that the robots must plan using only this stale data, while accounting for any noise in the data or uncertainty in the environment. To address this challenge we propose a compositional technique which leverages neural networks to quickly plan and control a robot through crowded and dynamic environments using only intermittent information. Specifically, our tool uses reachability analysis and potential fields to train a neural network that is capable of generating safe control actions. We demonstrate our technique both in simulation with an underwater vehicle crossing a crowded shipping channel and with real experiments with ground vehicles in communication-and sensor-limited environments.

Navigation through unknown, cluttered environments is a fundamental and challenging task for autonomous vehicles as they must deal with a myriad of obstacle configurations typically unknown a priori. Challenges arise because obstacles of unknown shapes and dimensions can create occlusions limiting sensor field of view and leading to uncertainty in motion planning. In this paper we propose to leverage such occlusions to quickly explore and cover unknown cluttered environments. Specifically, this work presents a novel occlusion-aware frontier-based approach that estimates gaps in point cloud data and shadows in the field of view to generate waypoints to navigate. Our scheme also proposes a breadcrumbing technique to save states of interest during exploration that can be exploited in future missions. For the latter aspect we focus primarily on the generation of the minimum number of breadcrumbs that will increase coverage and visibility of an explored environment. Extensive simulations and experiment results on an unmanned ground vehicle (UGV) are demonstrated to validate the proposed technique, showing improvements over traditional state of the art frontier-based exploration methods.

Cooperative multirobot systems coordinate their motion by exchanging information through consensus schemes to achieve a common goal. In the event of stealthy cyber attacks, compromised measurements and communication broadcasts can hijack a portion or the entire system toward undesired states. However, in order for these attacks to be effective, they have to exhibit nonrandom characteristics that contradict the expected multirobot system behavior. To deal with these hidden attacks, we propose a runtime monitoring framework that considers the signedresidual, defined as the difference between the expected and the received information to identify and isolate unexpected nonrandom behavior within the multirobot system. Specifically, the technique that we propose—namedCumulative Signdetector—monitors and compares changes in signed values of residual with their expected occurrences to detect inconsistencies and trigger alarms when an attack is discovered. Our results are validated theoretically by providing detection bounds and are demonstrated with simulations and experiments on swarms of unmanned ground vehicles under different attacks in comparison with state-of-the-art residual-based detection schemes.

A resilient multi-vehicle system cooperatively performs tasks by exchanging information, detecting, and removing cyber attacks that have the intent of hijacking or diminishing performance of the entire system. In this paper, we propose a framework to: i) detect and isolate misbehaving vehicles in the network, and ii) securely encrypt information among the network to alert and attract nearby vehicles toward points of interest in the environment without explicitly broadcasting safety-critical information. To accomplish these goals, we lever-age a decentralized virtual spring-damper mesh physics model for formation control on each vehicle. To discover inconsistent behavior of any vehicle in the network, we consider an approach that monitors for changes in sign behavior of an inter-vehicle residual that does not match with an expectation. Similarly, to disguise important information and trigger vehicles to switch to different behaviors, we leverage side-channel information on the state of the vehicles and characterize a hidden spring-damper signature model detectable by neighbor vehicles. Our framework is demonstrated in simulation and experiments on formations of unmanned ground vehicles (UGVs) in the presence of malicious man-in-the-middle communication attacks.

Autonomous vehicles may not behave as expected when subject to environmental disturbances. For instance, control commands suitable for driving on dry, paved roads may lead to unsafe conditions and undesired deviations when on slippery dirt or icy roads. Furthermore, it becomes increasingly important to offer human-understandable explanations of autonomous robots’ actions – especially when they operate in a space shared with humans such as public roads. In this work, we present an interpretable, risk-aware online adaptation approach that leverages a combination of Gaussian process regression and decision tree theory. Our approach predicts the system state in unknown environments using pre-trained models, which are continuously refined during runtime using a fast lookup table-based procedure. Every time the originally intended behavior is predicted to cause an unsafe state, our method computes a set of safe behaviors under the current model uncertainty and risk. The most suitable behavior is subsequently selected depending on mission requirements and a human-understandable explanation about the system’s decision-making is delivered. The proposed framework is applied to unmanned ground vehicles and validated in simulations, in which we demonstrate a successful safety-critical cargo delivery through a priori unknown, rough and slippery terrain.

This paper presents a novel method for transferring motion planning and control policies between a teacher and a learner robot. With this work, we propose to reduce the sim-to-real gap, transfer knowledge designed for a specific system into a different robot, and compensate for system aging and failures. To solve this problem we introduce a Schwarz–Christoffel mapping-based method to geometrically stretch and fit the control inputs from the teacher into the learner command space. We also propose a method based on primitive motion generation to create motion plans and control inputs compatible with the learner’s capabilities. Our approach is validated with simulations and experiments with different robotic systems navigating occluding environments.

Mobile robots are traditionally developed to be reactive and avoid collisions with surrounding humans, often moving in unnatural ways without following social protocols, forcing people to behave very differently from human-human interaction rules. Humans, on the other hand, are seamlessly able to understand why they may interfere with surrounding humans and change their behavior based on their reasoning, resulting in smooth, intuitive avoiding behaviors. In this paper, we propose an approach for a mobile robot to avoid interfering with the desired paths of surrounding humans. We leverage a library of previously observed trajectories to design a decision-tree based interpretable monitor that: i) predicts whether the robot is interfering with surrounding humans, ii) explains what behaviors are causing either prediction, and iii) plans corrective behaviors if interference is predicted. We also propose a validation scheme to improve the predictive model at run-time. The proposed approach is validated with simulations and experiments involving an unmanned ground vehicle (UGV) performing go-to-goal operations in the presence of humans, demonstrating non-interfering behaviors and run-time learning.

Due to changes in model dynamics or unexpected disturbances, an autonomous robotic system may experience unforeseen challenges during real-world operations which may affect its safety and intended behavior: in particular actuator and system failures and external disturbances are among the most common causes of degraded mode of operation. To deal with this problem, in this work, we present a meta-learning-based approach to improve the trajectory tracking performance of an unmanned aerial vehicle (UAV) under actuator faults and disturbances which have not been previously experienced. Our approach leverages meta-learning to train a model that is easily adaptable at runtime to make accurate predictions about the system’s future state. A runtime monitoring and validation technique is proposed to decide when the system needs to adapt its model by considering a data pruning procedure for efficient learning. Finally, the reference trajectory is adapted based on future predictions by borrowing feedback control logic to make the system track the original and desired path without needing to access the system’s controller. The proposed framework is applied and validated in both simulations and experiments on a faulty UAV navigation case study demonstrating a drastic increase in tracking performance.

For safe and efficient human-robot interaction, a robot needs to predict and understand the intentions of humans who share the same space. Mobile robots are traditionally built to be reactive, moving in unnatural ways without following social protocol, hence forcing people to behave very differently from human-human interaction rules, which can be overcome if robots instead were proactive. In this paper, we build an intention-aware proactive motion planning strategy for mobile robots that coexist with multiple humans. We propose a framework that uses Hidden Markov Model (HMM) theory with a history of observations to: i) predict future states and estimate the likelihood that humans will cross the path of a robot, and ii) concurrently learn, update, and improve the predictive model with new observations at run-time. Stochastic reachability analysis is proposed to identify multiple possibilities of future states and a control scheme that leverages temporal virtual physics inspired by spring-mass systems is proposed to enable safe proactive motion planning. The proposed approach is validated with simulations and experiments involving an unmanned ground vehicle (UGV) performing go-to-goal operations in the presence of multiple humans, demonstrating improved performance and effectiveness of online learning when compared to reactive obstacle avoidance approaches.

Autonomous vehicles are typically developed and trained to work under certain system and environmental conditions defined at design time and can fail or perform poorly if unforeseen conditions such as disturbances or changes in model dynamics appear at runtime. In this work, we present a fast online planning, learning, and recovery approach for safe autonomous operations under unknown runtime disturbances. Our approach estimates the behavior of the system with an unknown model and provides safe plans at runtime under previously unseen disturbances by leveraging Gaussian Process regression theory in which a model is continuously trained and adapted using data collected during the autonomous operation. A recovery procedure is event-triggered any time a safety constraint is violated to guarantee safety and enable learning and replanning. The proposed framework is applied and validated both in simulation and experiment on an unmanned aerial vehicle (UAV) delivery case study in which the UAV is tasked to carry an a priori unknown payload to a goal location in a cluttered/constrained environment.

While executing nominal tests on mobile robots is required for their validation, such tests may overlook faults that arise under trajectories that accentuate certain aspects of the robot's behavior. Uncovering such stressful trajectories is challenging as the input space for these systems, as they move, is extremely large, and the relation between a planned trajectory and its potential to induce stress can be subtle. To address this challenge we propose a framework that 1) integrates kinematic and dynamic physical models of the robot into the automated trajectory generation in order to generate valid trajectories, and 2) incorporates a parameterizable scoring model to efficiently generate physically valid yet stressful trajectories for a broad range of mobile robots. We evaluate our approach on four variants of a state-of-the-art quadrotor in a racing simulator. We find that, for non-trivial length trajectories, the incorporation of the kinematic and dynamic model is crucial to generate any valid trajectory, and that the approach with the best hand-crafted scoring model and with a trained scoring model can cause on average a 55.9% and 41.3% more stress than a random selection among valid trajectories. A follow-up study shows that the approach was able to induce similar stress on a deployed commercial quadrotor, with trajectories that deviated up to 6m from the intended ones.

Optical wireless communications (OWC) and the internet of things (IoT) are two recent paradigms that are expected to be ubiquitous in the near future. In this paper, we introduce an early concept of how OWC can play a critical role in IoT applications in the context of real-time location-based services. OWC systems such as visible light communications (VLC) use light-emitting diodes (LED), already present in most user electronics as data transmission nodes. Studies on indoor OWC positioning generally assume that the location of the nodes, the LEDs that are connected to the backbone communications network, is provided to the user equipment (UE). In this paper, we consider a scenario where no a priori knowledge of the LED and UE locations is available, and the UE uses a single photodetector to measure the received optical intensity. We use these measurements to extract the distance between the UE and LEDs, and form geometric relations to find their locations. Once the LED locations have been found, we can track the UE using an extended Kalman filter. The joint source LED localization and UE tracking process is enhanced by using measurements that are collected by other members of the OWC-IoT network. Results show that the root mean square error of the LED localization can be decreased to 10 centimeters, and this leads to a user tracking accuracy better than 20 centimeters using the proposed method.

In this paper, we present a fast run-time monitoring framework for safety assurance during autonomous system operations in uncertain environments. Modern unmanned vehicles rely on periodic sensor measurements for motion planning and control. However, a vehicle may not always be able to obtain its state information due to various reasons such as sensor failures, signal occlusions, and communication problems. To guarantee the safety of a system during these circumstances under the presence of disturbance and noise, we propose a novel fast reachability analysis approach that leverages Gaussian process regression theory to predict future states of the system at run-time. We also propose a self/event-triggered monitoring and replanning approach which leverages our fast reachability scheme to recover the system when needed and replan its trajectory to guarantee safety constraints (i.e., the system will not collide with any obstacles). Our technique is validated both with simulations and experiments on unmanned aerial vehicles case studies in cluttered environments under the effect of unknown wind disturbance at run-time.

Modern unmanned aerial vehicles (UAVs) rely on constant periodic sensor measurements to detect and avoid obstacles. However, constant checking and replanning are time and energy consuming and are often not necessary especially in situations in which the UAV can safely fly in uncluttered environments without entering unsafe states. Thus, in this paper, we propose a self-triggered framework that leverages reachability analysis to schedule the next time to check sensor measurements and perform replanning while guaranteeing safety under noise and disturbance effects. Further, we relax sensor checking and motion replanning operations by leveraging a risk-based analysis that determines the likelihood to reach undesired states over a certain time horizon. We also propose an online speed adaptation policy based on the planned trajectory curvature to minimize drift from the desired path due to the system dynamics. Finally, we validate the proposed approach with simulations and experiments for a quadrotor UAV motion planning case study in a cluttered environment.

In this paper we consider an online planning problem for unmanned aerial vehicle (UAV) operations. Specifically, a UAV has the task of reaching a goal from a set of possible goals while minimizing the amount of energy required. Due to unforeseen disturbances, it is possible that initially attractive goals might end up being very expensive during the execution. Thus, two main problems are investigated here: i) how to predict and plan the motion of the UAV at run time to minimize its energy consumption and ii) when to schedule next replanning time to avoid unnecessary periodic re-evaluation executions. Our approach considers a nonlinear model of the system for which a model predictive controller is used to determine the desired control inputs for each possible goal. These control inputs are then used to estimate the energy required to reach the different goals. Finally, a self-triggered scheduling policy determines how long to wait before replanning the goal to aim for. The proposed framework is validated through simulations and experiments in which a quadrotor must choose and reach some goal while being subject to external disturbances.

In this paper we present a methodology to control ground robots under malicious attack on sensors. Within the term attack we intend any malicious disturbance injection on sensors, actuators, and controller that would compromise the safety of a robot. In order to guarantee resilience against attacks, we use a control-level technique implemented within a recursive algorithm that takes advantage of redundancy in the information received by the controller. We use the case study of a vehicle cruise-control, however, the strategy we present in this work is general for several applications. Our methodology relays on redundancy in the sensor measurements: specifically we consider N velocity measurements and use a recursive filtering technique that estimates the state of the system while being resilient against sensor attacks by acting on the variance of the measurements noise. Finally, we move our focus on hardware validation demonstrating our algorithm through extensive outdoor experiments conducted on two unmanned ground robots.