With the development of e-commerce, the last-mile delivery has become a significant part of customers’ shopping experience. In this paper, an autonomous last-mile delivery method using multiple unmanned ground vehicles is investigated. Being a smart logistics service, it provides a promising solution to reduce the delivery cost, improve efficiency, and avoid the spread of airborne diseases, such as SARS and COVID-19. By using a cooperation strategy with multiple heterogeneous robots, contactless parcel delivery can be carried out within apartment complexes efficiently. In this paper, the last-mile delivery with heterogeneous UGVs is formulated as an optimization problem aimed at minimizing the maximum makespan to complete all tasks. Then, a heuristic algorithm combining the Floyd’s algorithm and PSO algorithm is proposed for task assignment and path planning. This algorithm is further realized in a distributed scheme, with all robots in a swarm working together to obtain the best task schedule. A good solution with an optimized makespan is achieved by considering the constraints of various robots in terms of speed and payload. Simulations and experiments are carried out and the obtained results confirm the validity and applicability of the developed approaches.

In this paper, we seek to provide unmanned ground vehicles with positioning service using ultrawideband (UWB) technology, a high-accuracy positioning approach. UWB is chosen for two distinct reasons. First, it does not rely on global navigation satellite systems like GPS, making it able to be applied indoors or in an environment where GPS signal is unstable. Second, it is immune to interference from other signals and accurate enough to guide unmanned ground vehicles moving precisely in a complex environment within a narrow road. In this paper, three UWB base stations are aggregated as a group in a 2D space for localization. A large number of tests are performed with a UWB base station cluster in order to validate its positioning performance. Based on the experiment results, we further develop a dynamic particle swarm optimization-based algorithm and a genetic algorithm to deploy multiple clusters of UWB base stations to cover an area of interest. The performance of the proposed algorithms has been tested through a series of simulations. Finally, experiments using unmanned ground vehicles are carried out to validate the localization performance. The results confirm that the robots can follow complex paths accurately with the proposed UWB-based positioning system.

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

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