Design, implementation and control of rehabilitation robots for upper and lower limbs

We present two novel rehabilitation robots for stroke patients. For lower limb stroke rehabilitation, we present a novel self-aligning exoskeleton for the knee joint. The primal novelty of the design originates from its kinematic structure that allows translational movements of the knee joint on the sagittal plane along with the knee rotation. Automatically adjusting its joint axes, the exoskeleton enables a perfect match between human joint axes and the device axes. Thanks to this feature, the knee exoskeleton is not only capable of guaranteeing ergonomy and comfort throughout the therapy, but also extends the usable range of motion for the knee joint. Moreover, this adjustability feature significantly shortens the setup time required to attach the patient to the robot, allowing more effective time be spend on exercises instead of wasting it for adjustments. We have implemented an impedance-type concept of the knee exoskeleton, experimentally characterized its closed-loop performance and demonstrated ergonomy and useability of this device through human subject experiments. To administer table top exercises during upper limb stroke rehabilitation, we present a novel Mecanum-wheeled holonomic mobile rehabilitation robot for home therapy. The device can move/rotate independently on its unlimited planar workspace to provide assistance to patients. We have implemented two different concepts of holonomic mobile platform based on different actuation and sensing principles: an admittance-type mobile robot and a mobile platform with series elastic actuation. The admittance-type robot is integrated with virtual reality simulations and can assist patients through virtual tunnels designed around nominal task trajectories. The holonomic platform with series elastic actuation eliminates the need for costly force sensors and enables implementation of closed loop force control with higher controller gains, providing robustness against imperfections in the power transmission and allowing lower cost drive components to be utilized. For contour following tasks with the holonomic platforms, we have synthesized passive velocity field controllers (PVFC) that ensure coordination and synchronization between various degrees of freedom of the patient arm, while letting patients to complete the task at their own preferred pace. PVFC not only minimizes the contour error but also ensures coupled stability of the human-in-the-loop system

Design, control and implementation of CoCoA: a human-friendly autonomous service robot

The growing demand to automate everyday tasks combined with the rapid development of software technologies that can furnish service robots with a large repertoire of skills, are driving the need for design and implementation of human-friendly service robots, i.e., safe and dependable machines operating in the close vicinity of humans or directly interacting with them in social domains. The technological shift from classical industrial robots utilized in structured factory oors to service robots that are used in close collaboration with humans introduces many demanding challenges to ensure safety and autonomy of operation of such robots. In this thesis, we present mechanical design, modeling and software integration for motion/navigation planning, and human-collaborative control of a human-friendly service robot CoCoA: Cognitive Collaborative Assistant. CoCoA is designed to be bimanual with dual 7 degrees-of-freedom (DoF) anthropomorphic arms, featuring spherical wrists. Each arm weighs less than 1.6 kg and possesses a payload capacity of 1 kg. Bowden-cable based transmissions are used for the arms to enable grounding of motors and this arrangement results in lightweight arms with passive back-driveability. Thanks to passive back-driveability and low inertia of its arms, the operation of CoCoA is guaranteed to be safe not only during physical interactions, but also under collisions with the robot arms. The holonomic base of Co- CoA possesses four driven and steered wheel modules and is compatible with wheelchair accessible environments. CoCoA also features a single DoF torso, and dual one DoF grippers, resulting in a service robot with a total of 25 active DoF. The dynamic/kinematic/geometric models of CoCoA are derived in open source software. Inverse kinematics, stable grasp, kinematic reachability and inverse reachability databases are generated for the robot to enable computation of kinematically-feasible collision-free motion/grasp plans for its arms/grippers and navigation plans for its holonomic base, at interactive rates. For the real-time control of the robot, motion/navigation plans characterizing feasible joint trajectories are passed to feedback controllers dedicated to each joint. The joint space control of each joint is implemented in hardware, while communication/synchronization among di erent DoF is ensured through EtherCAT/RS-485 eldbuses running at high sampling rates. To comply with human movements under physical interactions and to enable human collaborative contour tracking tasks, CoCoA also implements passive velocity eld control that guarantees user safety by ensuring passivity of interaction with respect to externally applied forces. The feasibility of the design and the applicability of the overall planning and control framework are demonstrated through dynamic simulations and physical implementations of several service robotics scenarios

Autonomous Navigation for Unmanned Aerial Systems - Visual Perception and Motion Planning

L'abstract è presente nell'allegato / the abstract is in the attachmen

Scaling Robot Motion Planning to Multi-core Processors and the Cloud

Imagine a world in which robots safely interoperate with humans, gracefully and efficiently accomplishing everyday tasks. The robot's motions for these tasks, constrained by the design of the robot and task at hand, must avoid collisions with obstacles. Unfortunately, planning a constrained obstacle-free motion for a robot is computationally complex---often resulting in slow computation of inefficient motions. The methods in this dissertation speed up this motion plan computation with new algorithms and data structures that leverage readily available parallel processing, whether that processing power is on the robot or in the cloud, enabling robots to operate safer, more gracefully, and with improved efficiency. The contributions of this dissertation that enable faster motion planning are novel parallel lock-free algorithms, fast and concurrent nearest neighbor searching data structures, cache-aware operation, and split robot-cloud computation. Parallel lock-free algorithms avoid contention over shared data structures, resulting in empirical speedup proportional to the number of CPU cores working on the problem. Fast nearest neighbor data structures speed up searching in SO(3) and SE(3) metric spaces, which are needed for rigid body motion planning. Concurrent nearest neighbor data structures improve searching performance on metric spaces common to robot motion planning problems, while providing asymptotic wait-free concurrent operation. Cache-aware operation avoids long memory access times, allowing the algorithm to exhibit superlinear speedup. Split robot-cloud computation enables robots with low-power CPUs to react to changing environments by having the robot compute reactive paths in real-time from a set of motion plan options generated in a computationally intensive cloud-based algorithm. We demonstrate the scalability and effectiveness of our contributions in solving motion planning problems both in simulation and on physical robots of varying design and complexity. Problems include finding a solution to a complex motion planning problem, pre-computing motion plans that converge towards the optimal, and reactive interaction with dynamic environments. Robots include 2D holonomic robots, 3D rigid-body robots, a self-driving 1/10 scale car, articulated robot arms with and without mobile bases, and a small humanoid robot.Doctor of Philosoph

Study of Communication Issues in Dynamically Scalable Cloud-Based Vision Systems for Mobile Robots

Thanks to the advent of technologies like Cloud Computing, the idea of computation offloading of robotic tasks is more than feasible. Therefore, it is possible to use legacy embedded systems for computationally heavy tasks like navigation or artificial vision, hence extending its lifespan. In this chapter we apply Cloud Computing for building a Cloud-Based 3D Point Cloud extractor for stereo images. The objective is to have a dynamically scalable solution (one of Cloud Computing’s most important features) and applicable to near real-time scenarios. This last feature brings several challenges that must be addressed: meeting of deadlines, stability, limitation of communication technologies. All those elements will be thoroughly analyzed in this chapter, providing experimental results that prove the efficacy of the solution. At the end of the chapter, a successful use case of the platform is explained: navigation assistance.Ministerio de Economía y Competitividad TEC2012-37868-C04-02/01 (BIOSENSE)Junta de Andalucía P12-TIC-1300 (MINERVA

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

Motion Planning in Artificial and Natural Vector Fields

This dissertation advances the field of autonomous vehicle motion planning in various challenging environments, ranging from flows and planetary atmospheres to cluttered real-world scenarios. By addressing the challenge of navigating environmental flows, this work introduces the Flow-Aware Fast Marching Tree algorithm (FlowFMT*). This algorithm optimizes motion planning for unmanned vehicles, such as UAVs and AUVs, navigating in tridimensional static flows. By considering reachability constraints caused by vehicle and flow dynamics, flow-aware neighborhood sets are found and used to reduce the number of calls to the cost function. The method computes feasible and optimal trajectories from start to goal in challenging environments that may contain obstacles or prohibited regions (e.g., no-fly zones). The method is extended to generate a vector field-based policy that optimally guides the vehicle to a given goal. Numerical comparisons with state-of-the-art control solvers demonstrate the method\u27s simplicity and accuracy. In this dissertation, the proposed sampling-based approach is used to compute trajectories for an autonomous semi-buoyant solar-powered airship in the challenging Venusian atmosphere, which is characterized by super-rotation winds. A cost function that incorporates the energetic balance of the airship is proposed to find energy-efficient trajectories. This cost function combines the main forces acting on the vehicle: weight, buoyancy, aerodynamic lift and drag, and thrust. The FlowFMT* method is also extended to consider the possibility of battery depletion due to thrust or battery charging due to solar energy and tested in this Venus atmosphere scenario. Simulations showcase how the airship selects high-altitude paths to minimize energy consumption and maximize battery recharge. They also show the airship sinking down and drifting with the wind at the altitudes where it is fully buoyant. For terrestrial applications, this dissertation finally introduces the Sensor-Space Lattice (SSLAT) motion planner, a real-time obstacle avoidance algorithm for autonomous vehicles and mobile robots equipped with planar range finders. This planner uses a lattice to tessellate the area covered by the sensor and to rapidly compute collision-free paths in the robot surroundings by optimizing a cost function. The cost function guides the vehicle to follow an artificial vector field that encodes the desired vehicle path. This planner is evaluated in challenging, cluttered static environments, such as warehouses and forests, and in the presence of moving obstacles, both in simulations and real experiments. Our results show that our algorithm performs collision checking and path planning faster than baseline methods. Since the method can have sequential or parallel implementations, we also compare the two versions of SSLAT and show that the run-time for its parallel implementation, which is independent of the number and shape of the obstacles found in the environment, provides a significant speedup due to the independent collision checks

Selected topics in robotics for space exploration

Papers and abstracts included represent both formal presentations and experimental demonstrations at the Workshop on Selected Topics in Robotics for Space Exploration which took place at NASA Langley Research Center, 17-18 March 1993. The workshop was cosponsored by the Guidance, Navigation, and Control Technical Committee of the NASA Langley Research Center and the Center for Intelligent Robotic Systems for Space Exploration (CIRSSE) at RPI, Troy, NY. Participation was from industry, government, and other universities with close ties to either Langley Research Center or to CIRSSE. The presentations were very broad in scope with attention given to space assembly, space exploration, flexible structure control, and telerobotics

Spatio-temporal map maintenance for extending autonomy in long-term mobile robotic tasks

Working in hazardous environments requires routine inspections in order to meet safety standards. Dangerous quantities of nuclear contamination can exist in infinitesimally small volumes. In order to confidently inspect a nuclear environment for radioactive sources, especially those which emit alpha radiation, technicians must carefully maintain detectors at a consistent velocity and distance from a source. Technicians must also take careful records of which areas have been surveyed or not are important so that no area is left unmonitored. This is a difficult, exhausting task when the coverage area is larger than an office space. An autonomous mobile robotic platform with Complete Coverage Path Planning (CCPP) can reduce dangerous exposure to humans and provide better information for Radiological Control Technicians (RCT). The developed robotic system - or RCTbot - is designed for long-term deployment with little human correction, intervention, or maintenance required. To do this, the RCTbot creates a map of the environment, continually updates it based on multiple sensor inputs, and searches its map for contamination. In nuclear environments, the areas of interest often remain spatially constant throughout the duration of an inspection and are considered temporally static. The RCTbot monitors temporally static environments but adapts to dynamic changes over time. It then uses its sensor data to update and maintain its map so no manual human intervention is necessary. The spatio-temporal map maintenance (STMM) is agnostic to the survey type, so the RCTbot system is viable for application domain other than nuclear.Mechanical Engineerin