A Multi-Objective Optimization Approach for Multi-Vehicle Path Planning Problems considering Human-Robot Interactions

There has been unprecedented development in the field of unmanned ground vehicles (UGVs) over the past few years. UGVs have been used in many fields including civilian and military with applications such as military reconnaissance, transportation, and search and research missions. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. The level of autonomy given to these UGVs is a critical factor to consider. In many applications of multi-robotic systems like “search-and-rescue” missions, teamwork between human and robots is essential. In this paper, given a team of manned ground vehicles (MGVs) and unmanned ground vehicles (UGVs), the objective is to develop a model which can minimize the number of teams and total distance traveled while considering human-robot interaction (HRI) studies. The human costs of managing a team of UGVs by a manned ground vehicle (MGV) are based on human-robot interaction (HRI) studies. In this research, we introduce a combinatorial, multi objective ground vehicle path planning problem which takes human-robot interactions into consideration. The objective of the problem is to find: ideal number of teams of MGVs-UGVs that follow a leader-follower framework where a set of UGVs follow an MGV; and path for each team such that the missions are completed efficiently.

Evaluation of Raised Safety Platforms (RSP) On-Road Safety Performance

A Raised Safety Platform (RSP) is a relatively new physical road safety intervention at major intersections. They aim to enhance road user safety by reducing vehicle speeds at intersections using an acute vertical deflection to the vehicle path. This study measured the change in speed at selected high-volume intersections treated with an RSP. It was a 12-month study based on a controlled before-and-after-treatment design, with speed and other measures assessed at six treated and five control intersections. Statistically significant and meaningful reductions in speeds were observed given the treatment and adjusted for the control group. A 15.6% reduction in the central tendency of speed was found overall. The odds of a vehicle exceeding nominal Safe System speeds of 30 km/h, 40 km/h, and 50 km/h also reduced markedly, with greater reductions observed at the higher speed thresholds (46%, 69%, and 80%, respectively). The change in speed corresponded to an estimated aggregate-level injurious crash-reduction benefit of around 26% and a reduction in the likelihood of a serious injury given a crash of between 38% to 57% depending on the crash type. It was concluded that RSP is an effective Safe System treatment to reduce speeds at major intersections to levels similar that at roundabouts. The results suggest that well designed RSPs at signalised intersections are an effective and sustainable Safe System treatment.

Yaw-rate-tracking-based Automated Vehicle Path Following: An MRAC Methodology with A Closed-Loop Reference Model

Concerning automated vehicles, various path-following controllers have been designed by the model reference adaptive control (MRAC) approach. Through appropriate Lyapunov redesigns, asymptotical stability and signal boundedness are ensured for the path-tracking control loops. However, transient behaviors of the closed-loop responses are seldom considered in the context of MRAC synthesis. To bridge the foregoing gap, a closed-loop reference model-based MRAC, which yields an improved transient performance compared with a traditional MRAC, is exploited to synthesize a vehicular path following control law. Besides, an infinitely differentiable projection operator is complemented to the control parameters' adaptation schemes for estimation speed-up and robustness enhancement. Hardware-in-the loop experiments are used to evaluate the proposed method and to demonstrate its improvement over some conventional MRAC designs.

Reliability-Based Multi-Vehicle Path Planning Under Uncertainty Using a Bio-Inspired Approach

Identifying a reliable path in uncertain environments is essential for designing reliable off-road autonomous ground vehicles (AGV) considering post-design operations. This paper presents a novel bio-inspired approach for model-based multi-vehicle mission planning under uncertainty for off-road AGVs subjected to mobility reliability constraints in dynamic environments. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility (i.e., maximum attainable speed) for any given terrain and soil conditions. Based on physics-based simulations, the vehicle state mobility reliability in operation is then analyzed using an adaptive surrogate modeling method to overcome the computational challenges in mobility reliability analysis by adaptively constructing a surrogate. Subsequently, a bio-inspired approach called Physarum-based algorithm is used in conjunction with a navigation mesh to identify an optimal path satisfying a specific mobility reliability requirement. The developed Physarum-based framework is applied to reliability-based path planning for both a single-vehicle and multiple-vehicle scenarios. A case study is used to demonstrate the efficacy of the proposed methods and algorithms. The results show that the proposed framework can effectively identify optimal paths for both scenarios of a single and multiple vehicles. The required computational time is less than the widely used Dijkstra-based method.

Logistics Distribution Location Algorithm Based on Improved Imperial Competition Algorithm

To solve the problems of warehouse explosion and delay of logistics distribution network under the sudden explosion of demand on “double 11” and “618,” this paper proposes a logistics distribution network location algorithm that can consider reliability, green environmental protection, and path optimization. Firstly, the transportation model of logistics distribution network location-route optimization is established. Under the condition that the transportation model satisfies the vehicle path reliability constraint, it can minimize the total cost, including logistics distribution cost, transportation oil consumption, and CO2 emission cost. This paper designs an improved imperial competition algorithm to solve it according to the characteristics of the transportation model. Firstly, the competition mechanism of “United Lian Heng” was introduced in the initial national stage, enhancing the information exchange and retaining the superior population. Secondly, in the process of empire assimilation, we can learn from the colonial rule strategy, which is gradually infiltrated and assimilated by all levels of the country to enhance the development ability of the algorithm. Finally, the algorithm designs a mechanism to judge and jump out of local optimum, so as to avoid “premature” affecting the optimization performance. The rationality of the model and the effectiveness of the improved imperial competition algorithm are verified by simulation experiments of different scales, while the influence of reliability level is analyzed. Experimental results show that the proposed method can effectively solve problems of different scales and maintain stable performance under different reliability levels. Moreover, its algorithm performance is better than that of the standard imperial competition algorithm.

UNMANNED AIR VEHICLE PATH PLANNING FOR MARITIME SURVEILLANCE USING CLUSTER-BASE METHOD

This paper discusses a method to determine the operation route for unmanned aerial vehicles for maritime surveillance. It is well known that there are several methods to make an aircraft path planning for ground related missions. On the other hand, path planning for maritime purposes is unnoticeable. The major problem of path planning for maritime is the abundant number of nodes which can make the route becomes quite long. Hence, reducing the number of nodes is necessary to rectify this problem. The main method is to separate the surveillance area into a smaller area of operation using clustering methods and then analyze the vulnerable area using the database to create an optimum flight path in each operation area. Although this paper specifically addresses a maritime-related mission, the path planning procedures can be applied to other missions as well. In this research, the input is given from satellite recorded data. Natuna Sea is chosen as the main discussion as the Natuna Sea currently is one of the most vulnerable regions in Indonesia for illegal fishing activity. The result shows that the aircraft path able to cover most of the vulnerable areas while optimizing the route distance.