A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.

Refined Node Energy Consumption Modeling in a LoRaWAN Network

LPWAN technologies such as LoRa are widely used for the deployment of IoT applications, in particular for use cases requiring wide coverage and low energy consumption. To minimize the maintenance cost, which can become significant when the number of sensors deployed is large, it is essential to optimize the lifetime of nodes, which remains an important research topic. For this reason, it is necessary that it is based on a fine energy consumption model. Unfortunately, many existing consumption models do not take into account the specifications of the LoRaWAN protocol. In this paper, a refined energy consumption model based on in-situ measurements is provided for a LoRaWAN node. This improved model takes into account the number of nodes in the network, the collision probability that depends on the density of sensors, and the number of retransmissions. Results show the influence of the number of nodes in a LoRaWAN network on the energy consumption of a node and demonstrate that the number of sensors that can be integrated into a LoRaWAN network is limited due to the probability of collision.

Combined Global and Local Search for Optimization with Gaussian Process Models

Gaussian process (GP) model based optimization is widely applied in simulation and machine learning. In general, it first estimates a GP model based on a few observations from the true response and then uses this model to guide the search, aiming to quickly locate the global optimum. Despite its successful applications, it has several limitations that may hinder its broader use. First, building an accurate GP model can be difficult and computationally expensive, especially when the response function is multimodal or varies significantly over the design space. Second, even with an appropriate model, the search process can be trapped in suboptimal regions before moving to the global optimum because of the excessive effort spent around the current best solution. In this work, we adopt the additive global and local GP (AGLGP) model in the optimization framework. The model is rooted in the inducing points based GP sparse approximations and is combined with independent local models in different regions. With these properties, the AGLGP model is suitable for multimodal responses with relatively large data sizes. Based on this AGLGP model, we propose a combined global and local search for optimization (CGLO) algorithm. It first divides the whole design space into disjoint local regions and identifies a promising region with the global model. Next, a local model in the selected region is fit to guide detailed search within this region. The algorithm then switches back to the global step when a good local solution is found. The global and local natures of CGLO enable it to enjoy the benefits of both global and local search to efficiently locate the global optimum. Summary of Contribution: This work proposes a new Gaussian process based algorithm for stochastic simulation optimization, which is an important area in operations research. This type of algorithm is also regarded as one of the state-of-the-art optimization algorithms for black-box functions in computer science. The aim of this work is to provide a computationally efficient optimization algorithm when the baseline functions are highly nonstationary (the function values change dramatically across the design space). Such nonstationary surfaces are very common in reality, such as the case in the maritime traffic safety problem considered here. In this problem, agent-based simulation is used to simulate the probability of collision of one vessel with the others on a given trajectory, and the decision maker needs to choose the trajectory with the minimum probability of collision quickly. Typically, in a high-congestion region, a small turn of the vessel can result in a very different conflict environment, and thus the response is highly nonstationary. Through our study, we find that the proposed algorithm can provide safer choices within a limited time compared with other methods. We believe the proposed algorithm is very computationally efficient and has large potential in such operational problems.

The Impact of Obstacle’s Risk in Pedestrian Agent’s Local Path-Planning

While the risk from the obstacle could significantly alter the navigation path of a pedestrian, this problem is often disregarded by many studies in pedestrian simulation, or is hindered by a simplistic simulation approach. To address this problem, we proposed a novel simulation model for the local path-planning process of the pedestrian agent, adopting reinforcement learning to replicate the navigation path. We also addressed the problem of assessing the obstacle’s risk by determining its probability of collision with the obstacle, combining with the danger from the obstacle. This process is subsequently incorporated with our prediction model to provide an accurate navigation path similar to the human thinking process. Our proposed model’s implementation demonstrates a more favorable result than other simulation models, especially in the case of the obstacle’s appearance. The pedestrian agent is capable of assessing the risk from the obstacle in different situations and adapting the navigation path correspondingly.

Advanced Wellbore Surveying Technology Extends the Drilling Limits in Offshore Operation

Wellbore surveying is critical while drilling in order to assure the drilled well is following the plan and is penetrating the geological target. Additionally, wellbore surveying is the key to allowing a well to be drilled safely, avoiding other wells drilled in the same field, and optimizing reservoir production. Standard wellbore surveying accuracy is increasingly inadequate for optimizing the well placement in real time to maximize the reservoir recovery due to maturity of the field. The other disadvantage of the standard wellbore surveying often requires running an additional wellbore surveying tool to improve the accuracy in order to manage the collision avoidance with nearby wells in the same field, introducing unwanted time and costs. Hence, this article presents the advanced wellbore surveying technology that is successfully implemented in offshore fields of Abu Dhabi to overcome the limitations of the standard surveying accuracy without compromising rig time. Magnetic measurement while drilling (MWD) surveys are common standard and utilized in every directional well in this operation. To overcome the standard accuracy limitation, advanced survey correction to the magnetic MWD surveys is introduced. This includes in-field referencing to provide a higher resolution magnetic reference to calculate a more accurate well direction, correction to the effect of the steel components in the bottom hole assembly on the magnetic MWD surveys, correction to the errors associated with survey sensors calibration, and correction to any misalignment between the survey tool and the wellbore. Correcting the surveys in real-time while drilling is the key to placing the well accurately and to avoid offset wells in the close proximity. The details of the corrections methodology are discussed. Advanced magnetic survey correction procedures in real-time are outlined and mapped out. Finally, results of improving the magnetic surveys while drilling in placing the wells and minimizing the collision risk of offset wells are presented. This advanced survey technology allows drilling previously un-drillable wells in these offshore fields, and the allowance for increased density of wells in the reservoir gives the operator opportunity to maximize production recovery and extend the life of reservoir. Higher accuracy of wellbore surveys is an increasing requirement in mature fields to safely allow more accurately placed wellbores with the required production rates. This allows for improved well placement along the trajectory facilitating adjustment at control points and landing points to maximize the hydrocarbon production. In addition, it allows controlling the probability of collision with any nearby wells. The enhanced wellbore surveying accuracy is achieved by advanced magnetic survey corrections in real time. This is controlled by a stringent novel process and communication protocol in order to meet the accuracy objectives.

Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

As the potential for deploying low-flying unmanned aerial vehicles (UAVs) in urban spaces increases, ensuring their safe operations is becoming a major concern. Given the uncertainties in their operational environments caused by wind gusts, degraded state of health, and probability of collision with static and dynamic objects, it becomes imperative to develop online decision-making schemes to ensure safe flights. In this paper, we propose an online decision-making framework that takes into account the state of health of the UAV, the environmental conditions, and the obstacle map to assess the probability of mission failure and re-plan accordingly. The online re-planning strategy considers two situations: (1) updating the current trajectory to reduce the probability of collision; and (2) defining a new trajectory to find a new safe landing spot, if continued flight would result in risk values above a pre-specified threshold. The re-planning routine uses the differential evolution optimization method and takes into account the dynamics of the UAV and its components as well as the environmental wind conditions. The new trajectory generation routine combines probabilistic road-maps with B-spline smoothing to ensure a dynamically feasible trajectory. We demonstrate the effectiveness of our approach by running UAV flight simulation experiments in urban scenarios.

On Probabilistic Risk of Aircraft Collision along Air Corridors

The separation of aircraft in cruising flight in air corridors is based on the assurance of an extremely low probability of collision due to position inaccuracy caused by navigation errors, atmospheric disturbances, or other factors. The appropriate standard is the International Civil Aviation Organization (ICAO) Target Level of Safety (TLS) of frequency of collision less than 5 × 10−9 per flight hour. An upper bound for the collision probability per unit distance is the probability of coincidence, in the case of aircraft flying at the same speed along parallel tracks in the same direction. This leads to the case of two aircraft flying at a constant separation, for which at least three probabilities of coincidence can be calculated: (i) the maximum probability of coincidence at the most likely point; (ii) the cumulative probability of coincidence integrated along the flight path; and (iii) the cumulative probability of coincidence integrated over all space. These three probabilities of coincidence are applied to the old standard and new reduced vertical separations of 2000 ft and 1000 ft respectively, for comparison with the ICAO TLS, and also to assess their suitability as safety metrics. The possibility is raised of complementing the ICAO TLS 5 × 10−9 per hour, which is suitable for the cumulative probability of collision, by two additional safety metrics: (i) one per hour flown squared, which is suitable for comparison with the maximum joint probability density of collision; and (ii) another times hour flown, for comparison with the three-dimensional cumulative probability of coincidence. These three metrics (i) to (iii) have distinct dimensions, give different information, and could be alternatives or supplements.