The mosquitoe population is reaching critical levels globally, posing significant threats to public health and ecosystems due to their role as vectors for diseases. This paper presents the development of a mobile robotic platform named Boa Fumigator with autonomous fumigation and prioritized path planning capabilities in urban landscapes. The robot’s locomotion is based on a differential drive, facilitating easier maneuverability on semi-automated planar surfaces in landscaping infrastructure. The robot’s fumigator payload consists of a spray gun and a chemical tank, which can pan and fumigate up to 4.5 m from the ground. The system incorporates a wireless charging mechanism to allow for the autonomous charging of the mosquito catchers. A genetic algorithm fused with an A*-based prioritized path planning algorithm is developed for efficient navigation and charging of mosquito catchers. The algorithm, designed for maximizing charging efficiency, considers the initial charge percentage of mosquito catchers and the time required for fumigation to determine the optimal path for charging and fumigation. The experiment results show that the path planning algorithm can generate an optimized path for charging and fumigating multiple mosquito catchers based on their initial charge percentage. This paper concludes by summarizing the key findings and highlighting the significance of the fumigation robot in landscaping applications.

Complex robotic tasks nowadays are demanding more computational resources than ever before. This results in an increase in load on the computers on-board the robots. This issue can be addressed through leveraging the power of the cloud to offload computationally intensive tasks. The current paper introduces a robot-cloud system architecture that can accommodate multiple drain inspection robots and fleets. This also provides a flexible arrangement to store the gigantic sensor data, into specific databases, generated from the robot fleet. Throughput tests involving stress loading a developmental virtual machine, in a private cloud, and a Raspberry Pi, from a drain inspection robot, was experimented and presented in the current paper. Latency tests under different networks were also tested while deploying the robot in a drainage.

Fumigation effectively manages pests, yet manual spraying poses long-term health risks to operators, making autonomous fumigation robots safer and more efficient. Path planning is a crucial aspect of deploying autonomous robots; it primarily focuses on minimizing energy consumption and maximizing operational time. The Payload and Energy-aware Tactical Allocation Loop (PETAL) algorithm integrates a genetic algorithm to search for waypoint permutations, applies a 2-OPT (two-edge exchange) local search to refine those routes, and leverages an energy cost function that reflects payload weight changes during spraying. This combined strategy minimizes travel distance and reduces energy consumption across extended fumigation missions. To evaluate its effectiveness, a comparative study was performed between PETAL and prominent algorithms such as A*, a hybrid Dijkstra with A*, random search, and a greedy distance-first approach, using both randomly generated environments and a real-time map from an actual deployment site. The PETAL algorithm consistently performed better than baseline algorithms in simulations, demonstrating significant savings in energy usage and distance traveled. On a randomly generated map, the PETAL algorithm achieved 6.05% higher energy efficiency and 23.58% shorter travel distance than the baseline path-planning algorithm. It achieved 15.69% and 31.66% in energy efficiency and distance traveled saved on a real-time map, respectively. Such improvements can diminish operator exposure, extend mission durations, and foster safer, more efficient urban pest control.

Decontamination robots are becoming more common in environments where reducing human exposure to hazardous substances is essential, including healthcare settings, laboratories, and industrial cleanrooms. Designing terrain-capable decontamination robots quickly is challenging due to varying operational surfaces and mobility limitations. To tackle this issue, a structured recommendation framework is proposed to automate selecting optimal locomotion types and track configurations, significantly cutting down design time. The proposed system features a two-stage evaluation process: first, it creates an annotated compatibility score matrix by validating locomotion types against a robust dataset based on factors like friction coefficient, roughness, payload capacity, and slope gradient; second, it employs a weighted scoring model to rank wheel/track types based on their appropriateness for the identified environmental conditions. User needs are processed dynamically using a large language model, enabling flexible and scalable management of various deployment scenarios. A prototype decontamination robot was developed following the proposed algorithm’s guidance. This framework speeds up the configuration process and establishes a foundation for more intelligent, terrain-aware robot design workflows that can be applied to industrial, healthcare, and service robotics sectors.