Unexpected Obstacles Research Articles

Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees

Three algorithms that improve the performance of the asymptotically optimal Rapidly exploring Random Tree (RRT*) are presented in this paper. First, we introduce the Goal Tree (GT) algorithm for motion planning in dynamic environments where unexpected obstacles appear sporadically. The GT reuses the previous RRT* by pruning the affected area and then extending the tree by drawing samples from a shadow set. The shadow is the subset of the free configuration space containing all configurations that have geodesics ending at the goal and are in conflict with the new obstacle. Smaller, well defined, sampling regions are considered for Euclidean metric spaces and Dubins' vehicles. Next, the Focused-Refinement (FR) algorithm, which samples with some probability around the first path found by an RRT*, is defined. The third improvement is the Grandparent-Connection (GP) algorithm, which attempts to connect an added vertex directly to its grandparent vertex instead of parent. The GT and GP algorithms are both proven to be asymptotically optimal. Finally, the three algorithms are simulated and compared for a Euclidean metric robot, a Dubins' vehicle, and a seven degrees-of-freedom manipulator.

Distributed Optimization of Multiagent Systems With Preserved Network Connectivity.

This paper deals with the problem of distributed optimization of a multiagent system with network connectivity preservation. In order to realize cooperative interactions, a connected network is the prerequisite for high-quality information exchange among agents. However, sensing or communication capability is range-limited, so it is impractical to simply make an assumption that network connectivity is preserved by default. To address this concern, a class of generalized potentials including discontinuities caused by unexpected obstacles or noises are designed. For a class of quadratic cost functions, based on the potentials, a new distributed protocol is proposed to formally guarantee the network connectivity over time and to realize the state agreement in finite time while the sum of local functions known to individual agents is optimized. Since the right-hand side of the proposed protocol is discontinuous, some nonsmooth analysis tools are applied to analyze system performance. In some practical scenarios, where initial states are unavailable, a distributed protocol is further developed to realize the consensus in a prescribed finite time while solving the distributed optimization problem and maintaining network connectivity. Illustrative examples are provided to demonstrate the effectiveness of the proposed protocols.

Hybrid Type-2 Fuzzy-Sliding Mode Controller for Navigation of Mobile Robot in an Environment Containing a Dynamic Target

We are interested in developing a new control architecture to command a mobile robot in a partially known environment. For that purpose, a hybrid control architecture is adopted, combining the reactive and the deliberative method. The reactive method consists of the hierarchical fuzzy controllers based on Type-2 Fuzzy Logic System (T2-FLS), dedicated to commanding the robot towards a mobile target while avoiding unexpected obstacles. A comparative study is made to show the efficiency of T2-FLS against Type-1 Fuzzy Logic System (T1-FLS). Additionally, the used deliberative method is the sliding mode, allowing the robot to track the mobile goal trajectory. Simulation results are given finally to test the proposed architecture.

Offline and Online Search: UAV Multiobjective Path Planning Under Dynamic Urban Environment

This paper is concerned with path planning for unmanned aerial vehicles (UAVs) flying through low altitude urban environment. Although many different path planning algorithms have been proposed to find optimal or near-optimal collision-free paths for UAVs, most of them either do not consider dynamic obstacle avoidance or do not incorporate multiple objectives. In this paper, we propose a multiobjective path planning (MOPP) framework to explore a suitable path for a UAV operating in a dynamic urban environment, where safety level is considered in the proposed framework to guarantee the safety of UAV in addition to travel time. To this aim, two types of safety index maps (SIMs) are developed first to capture static obstacles in the geography map and unexpected obstacles that are unavailable in the geography map. Then an MOPP method is proposed by jointly using offline and online search, where the offline search is based on the static SIM and helps shorten the travel time and avoid static obstacles, while the online search is based on the dynamic SIM of unexpected obstacles and helps bypass unexpected obstacles quickly. Extensive experimental results verify the effectiveness of the proposed framework under the dynamic urban environment.

Balance recovery through model predictive control based on capture point dynamics for biped walking robot

This study proposes an online walking-pattern generation algorithm with footstep adjustment. The algorithm enables a biped walking robot to effectively recover balance following external disturbance. The external disturbance is measured as a capture-point error, and a desired zero-moment point (ZMP) is determined to compensate for the capture-point error through a capture-point control method. To follow the desired ZMP, the optimal ZMP and the position of the foot to be changed are determined through model predictive control (MPC). In the MPC, quadratic programming is implemented considering a cost function that minimizes the ZMP error, the constraints that the ZMP maintains within the support polygon, and the constraints on the varying foot positions. The proposed algorithm helps a humanoid robot (DRC-HUBO+) to regain balance following disturbance, i.e., from strong pushing or stepping on unexpected obstacles.

Wheeled Mobile Robot Obstacle Avoidance Using Compass and Ultrasonic

This paper presents the use of ARDUINO board to control an autonomous mobile robot (AMR) for navigation purpose. The wheeled robot is capable to perform two tasks. The first task is to move autonomously to the north direction. And the second task is to avoid collision with unexpected static and moveable obstacles. The robot uses two sensors to navigate and avoid the obstacle, a digital compass HMC5883L uses to detect the north direction and update the situation of the robot during its movement. And the ultrasonic sensor uses to avoid nearest obstacles on the robot way. C language used to program the system to do its mission and installed to the ARDUINO board. The results obtained show that the robot was able to navigate and move to the north and avoid obstacles in the outdoor environment.

Geometric optimization of a self-adaptive robotic leg

This paper demonstrates the self-adaptive capabilities of a two-degree-of-freedom Hoeckens-pantograph robotic leg (inspired by underactuated mechanical fingers) as well as its optimization, allowing it to overcome unexpected obstacles during its swing phase. A multi-objective optimization of the mechanism’s geometric parameters is performed using a genetic algorithm to highlight the trade-off between two conflicting objectives and select an appropriate compromise. The first of those objective functions measures the leg’s passive adaptation capability through a calculation of the input torque required to initiate the desired sliding motion along an obstacle. The second objective function evaluates the free-space trajectory followed by the leg endpoint using three criteria: linearity, stance ratio, and height-to-width ratio. In comparison with the initial geometry based on the Hoecken’s linkage, the selected final mechanism chosen from the Pareto front shows an important improvement of the adaptation capabilities, at the cost of a slight decrease in the stance phase duration. This paper expands on mechanical self-adaptive design philosophy, which has recently attracted a lot of attention in the field of grasping, to legged locomotion and paves the way for subsequent experimental validation of this approach.

A 0.55 V 1.1 mW Artificial Intelligence Processor With On-Chip PVT Compensation for Autonomous Mobile Robots

Autonomous mobile robots are receiving a lot of attention for many applications, such as package delivery and smart surveillance, however, the battery capacity is limited to implement intelligent decision making in robots because of the heavy computational costs. In this paper, an ultra-low-power artificial intelligence processor (AIP) is proposed for real-time decision making of autonomous mobile robots. To achieve low power consumption while maintaining high performance, it adopts four key features: 1) an 8-thread tree search processor for real-time path planning; 2) a reinforcement learning accelerator for the avoidance of unexpected obstacles; 3) a 3-level transposition table cache for the reduction of duplicated computations; and 4) a PVT compensation circuit for the stable operation at near-threshold voltage. The proposed 16 mm $^{{{2}}}$ AIP is fabricated using 65-nm triple-well CMOS technology. It consumes only 1.1 mW at 0.55 V supply voltage and 7 MHz operating frequency, and 151 mW at 1.2 V supply voltage and 245 MHz operating frequency. The AIP achieves fast search speed (470 000 search/s) and low energy consumption (79 nJ/search), and it is successfully applied to a battery-powered robot system for autonomous navigation without any collision in dynamic indoor environments.

Stumbling corrective reaction elicited by mechanical and electrical stimulation of the saphenous nerve in walking mice.

The ability to walk around in a natural environment requires the capacity to cope with unexpected obstacles that may disrupt locomotion. One such mechanism is called the stumbling corrective reaction (SCR) that enables animals to step over obstacles that would otherwise disturb the progression of swing movement. Here we use in vivo motion analysis and physiological recording techniques to describe the SCR in mice. We show that SCR can be elicited consistently in mice during locomotion by inserting an obstacle along the path of leg movement during swing phase. Furthermore, we show that the same behavior can be elicited if the saphenous nerve, a cutaneous nerve that would detect contact of the leg with an object, is stimulated electrically. This suggests that cutaneous afferent feedback is sufficient to elicit SCR. We further show that the SCR is phase dependent, occurring only with stimulation during swing phase, but not during early stance. During SCR elicited by either method, the foot is lifted higher to clear the object by flexing the knee, via the semitendinosus muscle, and ankle joint, by tibialis anterior contraction. The tibialis anterior also exhibits a brief extension before flexion onset. Our data provide a detailed description of SCR in mice and will be crucial for future research that aims to identify the interneurons of the premotor network controlling SCR and its neuronal mechanisms by combining motion analysis, electrophysiology and mouse genetics.

Mind Your Step: the Effects of Mobile Phone Use on Gaze Behavior in Stair Climbing

Stair walking is a hazardous activity and a common cause of fatal and non-fatal falls. Previous studies have assessed the role of eye movements in stair walking by asking people to repeatedly go up and down stairs in quiet and controlled conditions, while the role of peripheral vision was examined by giving participants specific fixation instructions or working memory tasks. We here extend this research to stair walking in a natural environment with other people present on the stairs and a now common secondary task: using one’s mobile phone. Results show that using the mobile phone strongly draws one’s attention away from the stairs, but that the distribution of gaze locations away from the phone is little influenced by using one’s phone. Phone use also increased the time needed to walk the stairs, but handrail use remained low. These results indicate that limited foveal vision suffices for adequate stair walking in normal environments, but that mobile phone use has a strong influence on attention, which may pose problems when unexpected obstacles are encountered.

Collision avoidance using neural networks

Now a days, accidents on roads are caused due to the negligence of drivers and pedestrians or due to unexpected obstacles that come into the vehicle’s path. In this paper, a model (robot) is developed to assist drivers for a smooth travel without accidents. It reacts to the real time obstacles on the four critical sides of the vehicle and takes necessary action. The sensor used for detecting the obstacle was an IR proximity sensor. A single layer perceptron neural network is used to train and test all possible combinations of sensors result by using Matlab (offline). A microcontroller (ARM Cortex-M3 LPC1768) is used to control the vehicle through the output data which is received from Matlab via serial communication. Hence, the vehicle becomes capable of reacting to any combination of real time obstacles.

Shared Control Design of a Walking-Assistant Robot

This brief presents a control design for a walking-assistant robot in a complex indoor environment, such that it can assist a walking-impaired person to walk and avoid unexpected obstacles. In this design, the robot motion is a resultant of autonomous navigation and compliant motion control. The compliance motion controller allows the robot to possess passive behavior following the motion intent of the user, while the autonomous guidance gives safe navigation of the robot without colliding with any obstacles. A shared-control approach is suggested to combine the passive compliant behavior and safe guidance of the robot. When a user exerts force to the robot, the mobile platform responds to adjust the speed in compliance with the user movement. On the other hand, the autonomous navigation controller is designed to provide collision-free guidance. Using the developed shared controller, outputs of the compliance motion controller and autonomous navigation controller are fused to generate appropriate motion for the robot. In this manner, passive behavior allows the walking-assistant robot to adapt to a user’s motion intent and move in compliance with user. Meanwhile, the active guidance adjusts the linear velocity and the direction of the robot in real time in response to the environmental data received from the on-board laser scanner. The developed algorithms have been implemented on a self-constructed walking-assistant robot. Experimental results validate the proposed design and demonstrate that the robot can actively avoid unexpected obstacles while move passively following the user to the destination.

Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios

Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics- and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs’ plan motion trajectories and make high-level decisions in uncertain environments.

Monopulse Tracking Performance of a Satcom Antenna on a Moving Platform

A satellite communication (Satcom) antenna mounted on a moving platform provides a controlled heading that enables a geosynchronous satellite to communicate with the ground. A monopulse tracking method is effective for antenna control on a vehicle when it vibrates severely. However, this method has unexpected obstacles and its control performance is insufficient. To improve its control performance, the control command and monopulse error, the signal delay, and the radome effect are evaluated through tests. The authors then propose a method to transform the antenna error from 3D coordinates to 2D antenna coordinates. As a result, the antenna control performance is improved. As indicated in this study, examining antenna systems using the monopulse method on moving platforms is possible by understanding the antenna test process.

Video analysis of "YouTube funnies" to aid the study of human gait and falls - preliminary results and proof of concept.

Because falls are funny, YouTube and other video sharing sites contain a large repository of real-life falls. We propose extracting gait and balance information from these videos to help us better understand some of the factors that contribute to falls. Proof-of-concept is explored in a single video containing multiple (n=14) falls/non-falls in the presence of an unexpected obstacle. The analysis explores: computing spatiotemporal parameters of gait in a video captured from an arbitrary viewpoint; the relationship between parameters of gait from the last few steps before the obstacle and falling vs. not falling; and the predictive capacity of a multivariate model in predicting a fall in the presence of an unexpected obstacle. Homography transformations correct the perspective projection distortion and allow for the consistent tracking of gait parameters as an individual walks in an arbitrary direction in the scene. A synthetic top view allows for computing the average stride length and a synthetic side view allows for measuring up and down motions of the head. In leave-one-out cross-validation, we were able to correctly predict whether a person would fall or not in 11 out of the 14 cases (78.6%), just by looking at the average stride length and the range of vertical head motion during the 1-4 most recent steps prior to reaching the obstacle.

Networked Service Robots Control and Synchronization with Surveillance System Assistance

This paper proposes an efficient navigation control and synchronization mechanism of multiple networked robots for operation in large confined areas. An adaptable grid based navigation and control strategy is adopted to eliminate potential collisions among robots. Unexpected obstacles are handled and the speed of individual robot is maintained using the node-ordering technique. The proposed navigation control and synchronization mechanism is scalable and can be easily extended for multi-cell large environment. The obstacles information is gathered through local information by the robots for better planning of the navigation. The system collaborates with the existing surveillance systems in case of additional visual information is necessary. The interaction with the surveillance system is minimized to reduce potential overhead. The proposed methodology is evaluated for a large-scale simulation with multiple robots.

Cooperative cloud robotics architecture for the coordination of multi-AGV systems in industrial warehouses

In this paper we introduce a novel cloud robotics architecture that provides different functionalities to support enhanced coordination of groups of Automated Guided Vehicles (AGVs) used for industrial logistics. In particular, we define a cooperative data fusion system that, gathering data from different sensing sources, provides a constantly updated global live view of the industrial environment, for coordinating the motion of the AGVs in an optimized manner. In fact, local sensing capabilities are complemented with global information, thus extending the field of view of each AGV. This knowledge extension allows to support a cooperative and flexible global route assignment and local path planning in order to avoid congestion zones, obstacles reported in the global live view map and deal with unexpected obstacles in the current path. The proposed methodology is validated in a real industrial environment, allowing an AGV to safely perform an obstacle avoidance procedure.

The ‘campus imaginary’: online students’ experience of the masters dissertation at a distance

ABSTRACTHigher education research has overlooked online distance Masters students’ experiences of independent research, and this is an important gap at a time when increasing numbers of taught postgraduate programmes are delivered online. This article discusses findings from interviews with eighteen graduates from four online Masters programmes. It introduces a key theme from the research: the concept of the ‘campus imaginary’, which emerged during analysis as a way of accounting for interviewees’ tendencies to attribute challenging experiences to being at a distance from their supervisors, peers and the university campus. Common issues for Masters students, such as unexpected obstacles, difficult supervisory relationships, lack of time, and feelings of isolation were interpreted by students as features of the online dissertation process. We argue that the over-privileging of the campus and the face-to-face experience affects students’ campus imaginaries, but that imaginaries also leave space for more productive ways of engaging with online students at the independent research stage.

Embedded context aware diagnosis for a UAV SoC platform

Autonomous Unmanned Aerial Vehicles (UAVs) operate under uncertain environmental conditions and can have to face unexpected obstacles, weather changes and sensor or hardware/software component failures. In such situations, the UAV must be able to detect and locate the failure and to take adequate recovery actions. In this paper, we focus on the Health Management of the system depending on the context of the mission. The task of this Health Management is to monitor the status of the system components based on observations from sensors and appearance contexts, and it is designed by means of Bayesian Networks arising from the Failure Mode and Effects Analysis. We jointly introduce a framework to generate embedded software and hardware implementations for online and real-time observations, which are demonstrated on a Hybrid CPU/FPGA Zynq platform.

When an Object Appears Unexpectedly: Object Circumvention in Adults

ABSTRACTObstacles often appear unexpectedly in our pathway and these require us to make immediate adjustments. Despite how regularly we encounter such situations only few studies have considered how we adjust to unexpected obstacles in the pathway that require us to walk around them. The authors considered how adults adjust to the possibility of an obstacle appearing and then also how foot placement is adjusted to circumvent an obstacle. Fifteen healthy adults walked down an 11-m walkway, initially they were told this was a clear pathway and nothing in the environment would change (no gate), they then performed a series of trials in which a gate may (gate close) or may not (gate open) partially obstruct their pathway. The authors found that mediolateral trunk velocity and acceleration was significantly increased when there was the possibility of an obstacle but before the obstacle appeared. This demonstrates an adaptive walking strategy that seems to enable healthy young adults to successfully circumvent obstacles. The authors also categorized foot placement adjustments and found that adults favored making shorter and wider steps away from the obstacle. They suggest this combination of adjustments allows participants to maintain stability while successfully circumventing the obstacle.