Search and rescue (SAR) operations require optimal solutions to maximize efficiency and minimize risks for human responders. Quadruped robots have emerged as viable agents due to their locomotion and agility capabilities. This work presents a navigation framework designed to enhance stability and adaptability of legged robots in complex environments. A high-fidelity simulation environment was developed using NVIDIA IsaacSim, incorporating realistic disaster conditions such as unstructured terrain, fire, and smoke. Multiple stochastic routes were generated and analyzed in simulation in terms of energy consumption, stability, traversal time, and fall occurrences, to categorize them and determine the safest and most efficient path before real-world deployment, reducing the likelihood of failures. A fuzzy logic controller was proposed to regulate speed and improve locomotion adaptability. The proposed approach was validated in both simulation and real-world experiments. The results demonstrate the effectiveness of the proposed strategy in enabling safe and efficient navigation for quadruped robots in SAR missions. The code is publicly available at Robcib-GIT/IsaacSim_LeggedRobotSupplementary Video: https://youtu.be/cxEP5YVy8Qo

Abstract Modelling soft robots is a non‐trivial task since their behaviours rely on their morphology, materials and surrounding elements. These robots are very useful to safely interact with their environment and because of their inherent flexibility and adaptability skills. However, they are usually very hard to model because of their intrinsic non‐linearities. This fact presents a unique challenge in the computer graphics and simulation scopes. Current trends in these fields tend to narrow the gap between virtual and real environments. This work will explain a challenging modelling process for a cable‐driven soft robot called Kyma. For this purpose, the real‐time Finite Element Method (FEM) is applied using the Simulation Open Framework Architecture. And two methods are implemented and compared to optimize the model efficiency: a heuristic one and the Model Order Reduction. Both models are also validated with the real robot using a precise motion tracking system. Moreover, an analysis of robot–object interactions is proposed to test the compliance of the presented soft manipulator. As a result, the real‐time FEM emerges as a good solution to accurately and efficiently model the presented robot while also allowing to study the interactions with its environment.

In this paper we present an approach for analyzing the inertial effects of changing the wing shape for steering a bat-like robot. Using BaTboT, a robotic platform with massive morphing-wings, we have estimated the generation of pitching and rolling torques, which are directly related to forward and turning maneuvers. Results let us conclude that faster retraction of the wings during the upstroke, and slower extension during the downstroke increase both pitching and rolling torques in about 50% compared to those wingbeats with equal periods for retraction/extension. Also, we determined that the pitch torque generation is proportional to 0.6m1/f, whereas the rolling torque is promotional to 0.1m1/f, being m the mass of the robot and f the flapping frequency of the wings.

In this paper we present a new robotic control approach inspired in the animal nervous system control. The system implements the binomial Brain-Peripheral Nervous System (CNS-PNS) combining the use of microprocessors as the high level control, or brain, and FPGAs as the low level control, or nervous system. Thanks to the new open source tools for FPGAs, we are able to apply them in the field of robotics in new ways that were impossible before. In this paper, we will demonstrate that our approach is not only able to control the movements of robots using digital circuits built inside an FPGA, but is also capable of generating, synthesizing and uploading them inside the FPGA in real time and on demand.

Let's imagine a swarm of drones that has to visit some locations and build a map in a disaster area. Let's assume the drones only can communicate to their neighbors and manage partial information of the mission. A relevant question in this scenario is “Should the robots compete or should they cooperate?”. This work analyzes the described scenario to answer this question. Two game theoretical algorithms have been developed: one competitive and another cooperative. The competitive algorithm poses games among each drone and its neighbors and searches the Nash Equilibrium. The cooperative one defines electoral systems that allow the drones to vote their preferred task allocations for their neighbors. Both algorithms are extensively tested in multiple scenarios with different features. After the experiments the question can be answered “The robots should cooperate!”.

This paper presents a pedestrian trajectory prediction technique. Its mail novelty is that it does not require any previous observation or knowledge of pedestrian trajectories, thus making it useful for autonomous surveillance applications. The prediction requires only a set of possible goals, a map of the scenario and the initial position of the pedestrian. Then, it uses two different path planing algorithms to find the possible routes and transforms the similarity between observed and planned routes into probabilities. Finally, it applies a motion model to obtain a time-stamped predicted trajectory. The system has been used in combination with a pedestrian detection and tracking system for real-world tests as well as a simulation software for a large number of executions.

The control and monitoring of the UAV missions is a challenge in terms of operator workload. Two relevant issues are the situational awareness and the decision support of operators. This paper proposes a system that is able to analyze the telemetry of UAV to deduce the state of mission. This system uses Petri nets for determining the state and decision trees for estimating the evolution. Both the Petri nets and the decision trees are generated automatically from the telemetry of previous missions. The whole system is validated by monitoring a set of UAV missions in a realistic simulator. The results can be applied to diverse areas, including the development of intelligent and adaptive interfaces or decision support systems.

Bat wings contain dozens of joints that enable the animal to perform aggressive maneuvers by means of changing the wing shape during flight. There is evidence that the inertial forces produced by their wings during flapping have a key role in the attitude movements of the animal, i.e. aerial rotations. In fact, bats efficiently generate net body forces to manoeuvre by taking advantage of their large wing-to-body mass ratio. In this paper, the following question is formulated: Could a Micro Aerial Vehicle (MAV) inspired by the biomechanics of bats take advantage of the morphing-wings aimed at increasing net body forces? Using BaTboT, a novel bat-like MAV with highly articulated wings actuated by shape memory alloy actuators, our goal is to quantify the effects of different wing modulation patterns on the generation of net body forces. Experiments are carried out to confirm the important physical role that changing the wing shape enables: the contraction time of the wings (upstroke) should be faster than the extension time (downstroke), taking about 37.5% of the wingbeat period. This modulation pattern has enabled a lift force increment of 22% (from L = 0.92N to L = 1.12N), abrupt drag reduction (from D = 0.22N to D = 0.11N) and also an increase of net body forces (F net ) about 28% compared to those wing modulation patterns defined with equal periods for contraction/extension. These findings can be useful for accurate dynamics modelling and efficient design of flight controllers applied to morphing-wing micro aerial vehicles.

In this paper, we present our current work in the design and characterisation of a new shape memory alloy (SMA)-based High Phase Order Motor (HPOM). The motor can be used either in stepping mode or in servo mode of operation. Each phase of the motor consists of an SMA wire with a spring. The principle of operation of the HPOM is presented. In its operation the motor resembles a stepper motor though the actuation principles are different and hence has been characterised similar to a stepper motor.This motor can be actuated in either direction depending on which SMA is actuated, which are presented in this work. The motor is modelled and simulated and the results of simulations and experiments are presented.

In this paper, we describe our current work on bio-inspired locomotion systems using a deformable structure and smart materials, concretely Shape Memory Alloys, exploring the possibility of building motor-less and gear-less robots. A swimming underwater robot has been developed whose movements are generated using such actuators, used for bending the backbone of the fish, which in turn causes a change on the curvature of the body. This paper focuses on how standard swimming patterns can be reproduced with the proposed design, using an actuation dynamics model identified in prior work.

This paper is about modeling and control of miniature quadrotors, with a special emphasis on attitude control. Mathematical models for simulation and nonlinear control approaches are introduced and subsequently applied to commercial aircraft: the DraganFlyer quadrotor, which has been hardware-modified in order to perform experimental autonomous flying. Hybrid Backstepping control and the Frenet-Serret theory is used for attitude stabilization, introducing a desired attitude angle acceleration function dependent on aircraft velocity. Finally, improvements on disturbance rejection and attitude tracking at moderate aircraft speeds are validated through various simulation scenarios (indoor navigation based on camera tracking), and flight experiments conducted on the DraganFlyer quadrotor.

An important topic in the field of Multi Robot Systems focuses on motion coordination and synchronization for formation keeping. Although several works have addressed such problem, little attention has been devoted to study the computational complexity within the framework of large-scale systems. This paper presents our current work on how to achieve high computational performance for systems composed by a large number of robots that must fulfill with a marching and formation task. A scalable Multi-Processor Parallel Architecture is introduced with the purpose of achieving scalability, i.e., computation time of O(log2n) for a n-robots system. Our architecture has been tested onto a multi-processor system and validated against several simulations testing.

In this paper, we present a negotiation protocol for simultaneous task subdivision and allocation for heterogeneous multi-robot systems. An abstraction of the concept of task is presented that allows to apply the protocol on a variety of tasks. The negotiation adopts Rubinstein's alternate offers protocol, where offers are evaluated and generated using a heuristic search step. The protocol has been tested on computer simulations.

In this paper, we present an application to Search and Rescue of a task negotiation protocol for teams of heterogeneous robots. Self-organization through autonomous negotiations allow the robots to assign themselves a number of target observation points decided by the operator, who is relieved from deciding the optimal assignment. The operator can then focus on monitoring the mission and deciding next actions. The protocol has been tested on both computer simulations and real robots.

The dynamic optimization problem concerns finding an optimum in a changing environment. In the tracking problem, the optimizer should be able to follow the optimum's changes over time.In this paper we present two adaptive mutation operators designed to improve the following of a time-changing optimum, under the assumption that the changes follow a non-random law. Such law can be estimated in order to improve the optimum tracking capabilities of the algorithm. For experimental assessment, a (1,λ) evolution strategy has been applied to a dynamic version of the sphere problem.

This paper proposes the utilization of hybrid models of supervised neural networks for the modelling of dynamic systems. Particularly, as an example of a system, an autonomous helicopter or UAV is identified in both attitude and position systems. The evaluation of the model is done by comparing the radial basis and multilayer perceptron with the real system

This paper describes a mathematical model of a small-scale helicopter developed at "Universidad Politecnica of Madrid" as a part of VAMPIRA (multi-sensor aerial vehicle for autonomous inspections) project. A hybrid (analytic and empiric) model of a small helicopter is presented. The stability and response of the model is presented after a brief comparison of different techniques for helicopters dynamic modelling as well as the parameters identification using genetic algorithms. Accuracy of the model is verified by comparing real flight and simulation data obtained during the experiments.

In this paper a new open platform for experimentation in tele-manipulation tasks is presented. The main goal is to obtain an open system with several features in order to evaluate different factors like kinematics relations between master and slave, haptic feedback, bilateral control, communications bandwidth, man-machine interface with the operator, and other factors that can affect the results of the telerobotics system tasks. In this paper the platform is described and an analysis of the factors that affect the telemanipulation tasks is also presented. Finally, an experimental analysis with some results is shown.

Well known collision avoiding approaches used in automatic robot path planning are also applied in teleoperation systems when moving in hazardous environments. In such cases, it is very useful to define forbidden zones in order to avoid dangerous situations for both the equipment and the operator. An efficient collision detection algorithm, based on the generation of virtual forces, which avoids dangerous situations in teleoperated robots, is presented. Whenever the operator moves the robot to a predefined forbidden zone, the algorithm will generate a virtual force on the human operator arm in real time, not allowing the operator to keep further with the movement. The algorithm has been successfully applied to a robot system for live-line maintenance (ROBTET) substituting the time consuming operation of the placement of insulated covers by a software protection. By this approach, short-cuts and any unwanted collision are immediately avoided.

Outage-free maintenance of electrical power lines has been conducted throughout the years in order to fulfil the increasing demand of uninterrupted power supply. In these techniques care regarding electric shock and falls must be exercised while the work is performed on or near energized elements. In order to increase the safety and comfort of the workers as well as the overall efficiency, a teleoperated system for live-line maintenance, called ROBTET, has been developed for the electrical utility IBERDROLA. ROBTET is described in detail in the paper. It has two hydraulic-driven master-slave teleoperated manipulators on top of an insulated boom over a truck. The operator commands the manipulators from a cabin on the truck via a pair of master arms, while he receives visual feedback through a vision system. Multimedia display, voice commands, stereo vision and force-feedback are some of the new features implemented in order to achieve the required telepresence of the operator and hence increase the performance of the system.

In master-slave teleoperation systems the control loop is closed through the human operator. He acts as a controller generating actuating signals based in visual and kinesthetic cues. It is then indispensable to have a model of his behavior in order to design and study this type of systems coherently. Up to now only simplified arm dynamic models have been included in the study of bilateral control schemes. This paper makes use of already existing human-in-the loop models, developed for aircraft piloting, in order to construct a complete and simple model of human behavior for master-slave teleoperation systems with visual and kinesthetic feedback. The model was validated through several experiments with two different master arms and under various circumstances of operation. It includes complete human dynamic behavior: both the generation of control actions and limb dynamics.

This paper presents a robot assembly system for the construction industry based on an articulated robot placed over a mobile platform. The assembly process and the robot has been developed under the computer integrated construction concept. Its main task is the assembly of blocks for the erection of walls in industrial buildings.