Affordable Collision Detection for a 3D-Printed Robotic Arm

Memory loss and the inability to form new memories properly is a prominent and devastating feature of many widespread neurological conditions, particularly Alzheimer's disease and other forms of dementia. Memory function is supported by the limbic system, and more specifically the ability to remember recent events requires the hippocampus and associated structures including the entorhinal, perirhinal, and parahippocampal cortices. In rodent models, scientists have found that direct electrical stimulation of the perforant pathway, which arises from the entorhinal cortex in the medial temporal lobe and projects to the hippocampus, results in the cellular correlates of long term potentiation. In a new study, Suthana et al (Memory enhancement and deep-brain stimulation of the entorhinal area. N Engl J Med. 2012;366[6]:502-510.) have demonstrated enhancement of memory formation in human subjects by entorhinal stimulation during the learning process. The authors studied seven patients with intracranial depth electrodes who were undergoing monitoring for medically refractory epilepsy. These patients had magnetic resonance image- (MRI) guided stereotactic implantation of entorhinal or hippocampal depth electrodes, or in 4 of the 7 cases, ipsilateral implantation of both entorhinal and hippocampal electrodes. In these 4 cases hippocampal electroencephalography (EEG) data was collected during entorhinal stimulation. Patients were stimulated with parameters similar to those from other neuromodulation therapies and at levels below the threshold for inducing measureable after-discharges. Subjects were also unaware of the stimulation. They each completed a computerized spatial learning task in a virtual environment in which they had to learn the location of a number of stores and then drop off passengers at each store. Stimulation was applied to the depth electrodes as a subject was learning to navigate to particular stores (3 randomly selected stores out of 6 stores that were encountered in randomized order in each trial). Subjects completed 3 trials each in which stimulation was provided during navigation to the same 3 out of 6 stores. During a 4th trial, memory retention was tested. Spatial memory was quantified by calculating the excess path length, or the difference between the shortest possible route to a store and the actual route taken by a subject to that store. Latency, or the time elapsed between passenger pickup and dropoff at an assigned store was also measured. In each of 6 subjects, stimulation of the entorhinal cortex during the initial 3 trials resulted in a reduction of excess path length and latency for the corresponding navigation tasks in the 4th trial as compared to navigation tasks in which there had been no stimulation. The average reduction of excess path length was 64%. This was true despite the wide disparity in executive functioning pre-existing between the various patients. This effect was not observed with stimulation of hippocampal electrodes (Figure 1). In addition, for the 4 patients with both hippocampal and entorhinal electrodes, entorhinal stimulation induced greater theta-phase resetting as monitored in hippocampal electrodes as compared to non-stimulation. The large amplitude/low frequency hippocampal theta rhythm is thought to be a marker of active memory circuitry. These authors have demonstrated that entorhinal stimulation during learning can significantly enhance spatial memory, as demonstrated by improvements in this virtual world scenario performance. Although the study was limited to a small number of epilepsy patients these preliminary results are very exciting. It remains to be seen whether this kind of stimulation can be used to improve memory in patients who suffer from Alzheimer's disease or other forms of dementia and degenerative neurological conditions. Further study is needed and may reveal that stimulation in different hemispheres may result in differential improvement in verbal and spatial memory.Figure 1: Reduction in path length for the 2 different stimulated regions: A) entorhinal region, B) hippocampus. A 100% reduction would represent an excess path length of zero. Entorhinal stimulation showed consistent path length reduction, while hippocampal did not. From The New England Journal of Medicine, Suthana N, Haneef Z, Stern J, et al, Deep Brain Stimulation of Entorhinal Cortex Shows Early Promise for Enhancement of Memory Function, Volume No. 366, Pages 502-510. Copyright © (2012) Massachusetts Medical Society. Reprinted with permission from Massachusetts Medical Society.

Cells are the fundamental building blocks of organs and tissues. Information and mass flow through cellular contacts in these structures is vital for the orchestration of organ function. Constraints imposed by packing and cell immobility limit intercellular communication, particularly as organs and organisms scale up to greater sizes. In order to transcend transport limitations, delivery systems including vascular and respiratory systems evolved to facilitate the movement of matter and information. The construction of these delivery systems has an associated cost, as vascular elements do not perform the metabolic functions of the organs they are part of. This study investigates a fundamental trade-off in vascularization in multicellular tissues: the reduction of path lengths for communication versus the cost associated with producing vasculature. Biologically realistic generative models, using multicellular templates of different dimensionalities, revealed a limited advantage to the vascularization of two-dimensional tissues. Strikingly, scale-free improvements in transport efficiency can be achieved even in the absence of global knowledge of tissue organization. A point of diminishing returns in the investment of additional vascular tissue to the increased reduction of path length in 2.5- and three-dimensional tissues was identified. Applying this theory to experimentally determined biological tissue structures, we show the possibility of a co-dependency between the method used to limit path length and the organization of cells it acts upon. These results provide insight as to why tissues are or are not vascularized in nature, the robustness of developmental generative mechanisms and the extent to which vasculature is advantageous in the support of organ function.

This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized signal strength as a surrogate probability of source location. Building on Behavioral Entropy (BE) with Prelec's probability weighting function, we define an objective function that adapts robot behavior from risk-averse to risk-seeking based on signal reliability and mission urgency. The framework provides theoretical convergence guarantees under unimodal signal assumptions and practical stability under bounded disturbances. Experimental validation across DARPA SubT and multi-room scenarios demonstrates that BEASST consistently outperforms state-of-the-art methods and exhibits strong robustness to noisy gradient estimates while maintaining convergence. BEASST achieved 15% reduction in path length and 20% faster source localization through intelligent uncertainty-driven navigation that dynamically transitions between aggressive pursuit and cautious exploration.

This paper introduces a new approach for efficient rectangular boundary surveillance guidance for unmanned aerial vehicles (UAVs) using a continuous-curvature path described by a Lamé curve. Curvature and path length characteristics of the Lamé curve paths are analyzed, and conditions are deduced for the Lamé-curve-based circumscription of rectangular boundaries. Given the maximum turn rate capability of the UAV, a comparative study highlights significant reduction in path length as compared to the widely used elliptic circumscription. In conjunction with the desired Lamé curve path, a vector field guidance method is proposed for path-following. The vector field around the path is generated using a commanded heading angle, which encapsulates a tangent vector along the path and an arcsine shaping function of the UAV position error with respect to the desired path. Asymptotically converging behavior of the UAV position error is ascertained, and simulation studies illustrate the accurate following of the desired path for arbitrary UAV initial conditions. Indoor experimental studies utilizing a quadrotor UAV platform present the practical applicability of the proposed surveillance guidance solution.

For a mobile service robot, safe navigation is essential. To do so, the robot needs to be equipped with collision avoidance and global path planning capabilities. The target platforms considered in this paper are Ackermann-driven vehicles. Hence, a planning approach which directly takes the kinematic and dynamic constraints of the vehicle into account is required. The Search-based Planning Library (SBPL) package of the Robot Operating System (ROS) provides global path planning which takes these constraints into account. However, it misses a local planner that can also make use of the Ackermann kinematic constraints for collision avoidance. A local planner is useful to take dynamic obstacles into account as early as possible. In this paper, we extend the SBPL included in ROS by a local planner, which makes use of motion primitives. We extend the ROS package and show first experimental results on Ackermann-driven vehicles.

This paper presents a new method based on the application of the Hough Transform for solving the robot motion problem and their intermediate steps: geometric modelling and collision detection. In terms of modelling, given a set of points, which is the representation of a given object, the maximum inner and the minimum outer spherically-extended polytopes (s-tope) are automatically generated. Collision detection is based on the computation of the translational penetration distance. Such a distance also allows to compute an intermediate point when collision avoidance is required. The set of intermediate points describes a solution to the path-planning problem. This new approach introduces an important decrease in the computational time required for robot motion planning.

In this paper, we propose the infinity-norm based worst-case collision avoidance control system for quadrotors with the collision detector. The associated worst-case collision avoidance is captured via the infinity-norm in <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^{3}$ </tex-math></inline-formula> (equivalently the cube in <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^{3}$ </tex-math></inline-formula>). With the proposed infinity-norm based collision avoidance approach, the quadrotors are able to avoid any obstacles that cannot be detected by the standard two-norm in <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^{3}$ </tex-math></inline-formula> (the cylinder in <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^{3}$ </tex-math></inline-formula>). In our proposed collision avoidance method, the obstacle approximation algorithm approximates the true coordinates of obstacles by simple integer values to reduce computational complexity. Then the approximated obstacle is surrounded by infinity-norm based to cover the actual obstacle. Note that the actual obstacle may not be covered with the standard two-norm, which can cause the collision. We then design the collision detector and the Model Predictive Control (MPC) tracker, where the latter generates the optimal trajectory for the quadrotor by cooperating with the proposed collision detector. The optimal trajectory generated by the MPC tracker is regarded as the reference input for the quadrotor without collision, where the quadrotor is controlled by the modified backstepping controller. The proposed approach is validated by performing the hardware-in-the-loop simulations (HILS) and experiments. Specifically, we show the superiority of the proposed infinity-norm based collision avoidance performance, by comparing it with the standard two-norm based approach in various maneuver situations of the quadrotor.

In clinics, customized archwires are demanded for lingual orthodontic treatment. However, only very experienced orthodontists can handle the manual appliance preparation. This pattern not only occupies lots of the orthodontist's labor time, but also can not ensure the accuracy of the appliances. Therefore, a robotic system was developed for automatic and accurate orthodontic archwire bending in our study. First, a method for customized archwire parameterization was developed. Second, an adaptive sampling-based bending planner with collision checker in time-varying environment was designed. Finally, a bending control strategy was used to eliminate the springback effect of the archwires and bending point shift during the bending process. A self-developed simulation platform based on Robot Operating System with MoveIt was used for preliminary validation of the proposed method. Physical experiments for multi-functional orthodontic bends on the robotic system were conducted as well. The results have shown that the developed robotic system using the proposed planning and control method was able to accomplish the automatic and accurate orthodontic archwire bending.

This paper presents a framework schema that allows to bring simulation results obtained from different dual-arm robotic applications to executions in real environments. The framework uses the Robot Operating System (ROS) to communicate each component of the dual-arm, and the robotic software tool The Kautham Project for the computation of motion paths, inverse kinematics validations, collision checking and the graphical visualization of the simulated environment. The Anthropomorphic Dual Arm Robotic System (ADARS) was used in this work which is composed of two robotic arms of 6 degree of freedom, two anthropomorphic hands, and tactile sensors in the fingertips. The real execution of two different applications are presented to show the robustness of the framework. The resulting framework is general enough, allowing the reimplementation with minimum changes in any other dual-arm system.

Uninhabited Aerial Vehicles will require autonomous collisio n detection and avoidance capabilities in order to gain wide acceptance and usage in the National Airspace System. The collision detection and avoidance problem is primarily a motion estimation problem, relying on the accuracy of the sensing, processing, actuation and control algorithms onboard the vehicle. Computer vision is a growing field in robotics and autonomous vehicles, and has made vision a feasible technology for object detection in different platforms. In this work, we investigate the feasibil ity of using optic flow techniques in order to design an altitude hold controller for a fixed wing aircraft flying a nap -of -the -earth trajectory in the presence of variable terrain. Simulated results yield an error of approximately 1.5% in altitude variat ion for steady level flight (approximately 15 cm) for terrain disturbances of maximum amplitude 4 m. Experimental results obtained from flight data using the UIUC UAV platform are used to validate the controller, and sources of error along with potential improvements in equipment and procedures are cited. I. Introduction N essential challenge inherent to the integration of uninhabited aerial vehicles (UAVs) into the National Airspace System (NAS) is the inability to provide adequate safety assurances for th ese vehicles in a verifiable manner. The FAA requires that UAVs be capable of see -and -avoid functionality, unfortunately most solutions to the problem of autonomous surveillance, guidance and navigation are insufficiently scalable, reliable, verifiable or cost -effective. Implementation of safe and reliable object avoidance is crucial for the survival of autonomous vehicles intended to navigate in densely populated environments such as the NAS. In order to meet these stringent requirements, UAVs must be eq uipped with reactive sensing technologies that enable real -time conflict detection and object avoidance. Numerous techniques have been developed in the past few years to optimize the decision making process and solve the path planning problem in real -time , while accounting for aircraft dynamics, obstacle avoidance constraints and safe flight considerations. Regrettably, many of these techniques underestimate the difficulty of accurately sensing a dynamic and imperfect environment in an accurate and practic al manner. Making decisions based on distorted estimations of the surrounding environs is a pitfall that can destabilize even the most robust of path planning algorithms. Presently, state -of -the -art conflict detection and resolution systems employ a combi nation of radar, sonar and laser systems: while reliable, these systems are often prohibitive due to weight, size and equipment cost for certain UAV applications. Video recording devices are another alternative for environmental sensing, and have become standard equipment on many UAVs due to their exploratory nature. Although video images from this source are often intended for secondary mission purposes, such as target tracking and surveillance, they have been shown capable of providing valuable informati on for more important tasks, like collision avoidance. However, the nature of a fixed wing UAV platform brings rigorous safety requirements and constraints to the field of autonomous conflict detection and resolution: limited minimum flying speeds and res tricted maneuverability, thereby making the need for real -time sensing and decision -making paramount for safety purposes. In this paper we investigate the applicability of c omputer vision techniques that have been developed in order to estimate motion from two dimensional images. In the next section we provide a brief discussion of optical flow theory, and section 3 provides a direct implementation of the optical flow theory to an aircraft altitude controlle r. Section 4 provides the setup and simulation of several scenarios used to evaluate the performance of the controller designed in the previous section. Experimental video data taken from actual flight tests is used in order to asses the 1

This paper presents a “low-cost, small, lightweight Guidance, Navigation and Control (GNC) Micro-System for Aircraft Collision Avoidance: Vision Based Miniaturized Aircraft Integrated Collision Avoidance System” which is referred to as “The Integrated System” in this paper. The Integrated System takes advantage of the American GNC Corporation’s (AGNC) coremicro AHRS/INS/GPS Integration Unit and object detection algorithms to achieve a vision based integrated GNC micro-system for aircraft and UAVs, which is capable of assisting aircraft pilots to avoid approach and collision with ground/water and other near objects in flight, thus, improving safety, reliability and cost effectiveness. The various data from the coremicro AHRS/INS/GPS Integration Unit, terrain data base, and object detection sensors are processed to produce collision warning audio/visual messages and collision detection and avoidance of terrain and obstacles through generation of guidance commands in a closed-loop system.

This article presents an optimal trajectory-planning method for robot manipulators with collision detection and avoidance. The obstacles and robot segments are represented by a set of convex polyhedra. The collision detection is performed at each discretized robot configuration by an efficient procedure devel oped with the computational geometry method, which computes a distance function of the robot segments and the obstacles. By introducing this function for specifying the collision-free con straint, the path-planning problem is formulated as an optimal control problem using the augmented Lagrangian, which may be considered as a combination of the duality, penalty and con straint relaxation methods. The problem is solved by a robust UZAWA-like algorithm, where a subgradient method is applied for the primal optimization, as the distance function is not ev erywhere differentiable. An example is given for the trajectory planning of a robot arm with three revolute joints.

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

A flexible operation of multiple robotic manipulators operating in a dynamic environment requires online trajectory planning to ensure collision-free trajectories. In this work, we propose a real-time capable motion control algorithm, based on nonlinear model predictive control, which accounts for static and dynamic obstacles. The proposed algorithm is realized in a distributed scheme, where each robot optimizes its own trajectory with respect to the related objective and constraints. We propose a novel approach for collision avoidance between multiple robotic manipulators, where each robot accounts for the predicted movement of the neighboring robots. Additionally, we propose a method to reliably detect and resolve deadlocks occurring in a setup of multiple robotic manipulators. We validate our approach on pick and place scenarios involving multiple robotic manipulators operating in a common workspace in a realistic simulation environment set up in Gazebo. The robots are controlled using the Robot Operating System. Our approach scales up to 4 manipulators and computes a path for each robot in a simultaneous pick and place operation in 94&#x0025; of all investigated cases without deadlock detection and 100 &#x0025; of cases with the proposed deadlock resolution algorithm. In contrast, the investigated conventional path planners, such as PRM, PRM&#x002A;, CHOMP and RRT-Connect, successfully plan a trajectory in at most 54&#x0025; of all investigated cases for a simultaneous operation of 4 robotic manipulators hindering their application in setups of multiple manipulators.

Robot Operating System (ROS) has been widely used to develop robotic applications. The first generation of ROS generally lacks security features, and ROS 2 is introduced with security support. However, security concerns still exist for running ROS in practical multi-tenant environments. In this paper, we conduct an in-depth investigation into the security of ROS 2. We focus on vulnerabilities in ROS nodes and topics and intend to explore methods to break the isolation and security mechanisms systematically. We devise a set of strategies that can be exploited by attackers to escalate privilege or cause information leakage in a multi-tenant environment. These attacks can bypass existing isolation and security mechanisms, including ROS 2's native security module. To validate our findings, we employ simulations across various real-world scenarios to demonstrate how attackers could exploit these vulnerabilities to bypass existing security mechanisms. Finally, we present several defense practices to mitigate these identified threats.