In recent years, the amount of medical waste has increased due to increased numbers of unmanned aerial vehicle healthcare centers. These centers produce a large amount of medical waste. The congestion in cities creates problems in the transportation of medical waste. Much medical waste is disposed of using ordinary and contemporary methods, including incineration, steam sterilization, microwave, and landfill disposal (Mishra et al., 2020). The medical wastes include hypodermic needles, hypodermic needles with attached syringes, and needles with attached tubing, blades, broken glass, acupuncture needles, and pipettes, some contaminated with biohazardous or pharmaceutical material. These are generated from most patient care and clinical support areas. The healthcare industry is increasing rapidly, so an effective solution must be developed for the transportation of medical waste to the disposal centers with less human contact. Improved education and standards must be defined for adequate medical waste management (Windfeld and Brooks, 2015). Much planning and logistics work must be done to manage medical waste efficiently in hospital systems.

A peer-to-peer scheme could be created for hazardous medical waste treatment to avoid illegal usage in medical waste treatment. Inadequate management of medical waste in healthcare centers creates health issues for employers of these centers and draws global attention. Due to storage capacity limitations at medical centers, collection services should occur at regular intervals. A periodic and minimum route scheme must be developed to collect medical waste from dispersed hospitals. The major process of medical waste management is segregation, labeling, and separation (Shareefdeen et al., 2022).

Medical waste management comprises the process of separation, short-term storage, safe disposal, and transfer of medical waste. Standards for medical waste management must be defined. The cost that may be incurred in medical waste transportation should be defined, and new schemes must be developed to minimize this cost (Koçak et al., 2016).

A hybrid decision-making approach comprising interval 2-tuple induced distance operators with similarity to the ideal solution (TOPSIS) has been suggested to tackle the healthcare treatment. A trash system equipped with a camera and quick response (QR) code was created for medical waste management to address the uncertainty and diversity noted in the assessment report (Lu et al., 2016). In that study, artificial intelligence techniques were implemented in the classification of medical wastes that have different features. The molecular structure of the wastes was to be analyzed with a spectrum analyzer.

Major problems associated with hazardous medical waste management are inadequate and unstandardized supervision. Current methods of detecting hidden dangers in hazardous waste disposal are inefficient. Implementing fifth-generation (5G) wireless communication technology and big data in the medical waste treatment process is challenging. A platform comprising 5G broadband wireless communication technology and a big data approach is developed for an effective point-to-point (P2P) goods information system (Wang and Nai, 2021).

Radio frequency identification (RFID) technology has been implemented in medical waste management for item collection and transmission without manual intervention. The RFID technology can perform object recognition, multi-object recognition, and object tracking [8]. Existing healthcare waste treatment methods are steam sterilization, microwave, plasma pyrolysis, chemical disinfection, and incineration (Liu et al., 2019). RFID can be used to detect lost medical waste items and prevent illegal recycling and poor supervision. The RFID technology can be used to establish a tracking and processing system, incineration center subsystems, and effective supervision of medical waste (Sun et al., 2019; Wang et al., 2021). The interaction rules are designed for medical waste management. Blockchain-based methods are suggested for medical waste disposal to create trust among different stakeholders (Ahmad et al., 2021). Multi-criteria decision-making methodology has been implemented. The associated uncertainty present in multi-criterion decision-making is tackled by the Pythagorean fuzzy sets.

The process of medical waste management comprises medical waste generation from hospitals and testing centers, waste collection at waste store rooms, waste transportation, waste segregation and sorting, waste treatment, and waste disposal and recycling (Ahmad et al., 2021). A blockchain-based process is implemented to trace the medical equipment and corresponding medical waste. The main stockholders in medical waste management are doctors, medical test operators, patients, health workers, and waste collection, transportation, and disposal companies.

A literature review revealed the limited use of drones in the transfer of medical waste. A collection problem could be considered to obtain an optimal trajectory. Manual collection of waste from collection points may cause health problems for employees. A coordinated multi-drone mobile robot framework could collect medical waste and transfer it to disposal centers with minimum human intervention.

In the context of medical waste management, occupational risk and transportation risk take on specific considerations due to the unique nature of medical waste. Medical waste includes materials generated in healthcare facilities that may threaten human health and the environment if not handled properly. The following sections discuss how occupational and transportation risks relate to medical waste.

Coordination between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is essential for the successful implementation of various applications, ranging from surveillance and reconnaissance to disaster response and agriculture. Key aspects of UAV–UGV coordination are discussed in the sections that follow.

Within a healthcare facility, regulated storing and transporting of medical waste strategies must be created. Safe transport and storage facilities ensure health and environmental safety. The medical waste disposal containers are classified based on color: red for biohazardous or sharp waste, yellow for chemotherapy waste and soiled and contaminated linen, black for Resource Conservation and Recovery Act (RCRA)-regulated waste, and blue for non-RCRA-regulated waste. Challenges accompany the handling of medical waste because it has impacts on the environment and risks to public health. Figure 1 shows some of these challenges and impacts.

Drones are revolutionizing medical waste transportation. A coordinated robot drone framework minimizes the transportation and occupational risks concerning the temporary storage of hazardous wastes at the medical centers, as shown in Figure 2. The framework minimizes the risks associated with the transportation of potentially dangerous microorganisms. The framework collects and transports infectious waste from scattered collection points to disposal centers.

The flying operating time of drones is restricted by payload weight and battery capacity. Turbulence is also a hindrance. The latency-guaranteed multi-hop wireless communication system is considered for the flight of a drone moving from the line of sight (LOS) to beyond the line of sight (BLOS) with an obstacle avoidance facility. The obstacles may be trees, buildings, small hills, etc. The drone control mechanism comprises a multi-hop link or direct link to obtain the adaptive flying route (Kagawa et al., 2017).

Efficient MW management must be developed to avoid dangerous microorganisms that affect public health and the environment. During the treatment and disposal of medical waste, pathogens, and toxins are released. It is mandatory to develop an optimal logistical solution for medical waste transportation to minimize contact with operators and health workers. Relevant data include optimal and secure MW transportation solution suggests the MW data structure as weight of the waste, the time of waste pickup, sensor state data, and order status.

Rq _mw : Medical waste shipment request.

T _p : Waste pickup time.

T _d : Waste disposal time.

W _mw : Weight of the waste.

Id _mw : ID of medical waste (created with Secure Hash Algorithm (SHA)-256).

Au _mw : Medical waste authorization.

O _mw : Location of medical waste.

Q _k = { Rq _k,mw , T _k,p , T _k,d , W _k,mw , Id _k,mw , Au _k,mw , O _k,mw }.

After it is collected from health centers, medical waste must be transported to the transit points. From transit points, the CUU framework comprising UGVs and multiple UAVs collects the medical wastes and transports them to the disposal centers Figure 3. The whole “seek” area is divided into subareas. Each subarea is assigned to a different drone. The flight time of each drone is estimated according to the charging of the battery. The control center acquires the drones’ real-time data, such as battery information, coordinates, and sensor data. The seek area is divided into the n unit cube parts with 1-m dimension. The WiFi communication zone comprises ( n 2 + 1 ) parts that are within the control range (Celtek et al., 2018).

Let S and A represent a discrete set of environment states and a set of actions, respectively. In every state s ∈ S, a framework of multi-drone mobile robots takes feasible actions a ∈ A over the finite learning horizon. The framework transits to the next state s ¹ ∈ S, and the framework receives an immediate reward for performing the desired action. However, the framework is penalized for each undesired action leading to a collision. The goal of the framework is to maximize its total reward. It does this by learning which action is optimal for each state. With available information of < s , a , s ′ , r t > , the Q-learning process is updated according to the following equation: Q s t , a t ← Q s t , a t + α t s t , a t × r t + 1 + γ m a x a t + 1 Q s t + 1 , a t + 1 − Q s t , a t , (11) where α t s t , a t ( 0 < α ≤ 1 ) is the learning rate and R _t+1 is the reward observed after performing a _t in s _t . The learning rate may be the same for all pairs, and the discount factor γ is such that 0 ≤ γ < 1. The Bellman equation that describes the optimal action-value function Q*(s, a) is given below: Q * s , a = E s ′ ∼ P r s , a + γ m a x a ′ Q * s ′ , a ′ , (12)

where s′ ∼ P represents the next state, which is sampled from a distribution P (.|s, a). This Bellman function starts the learning approach of approximately Q * s , a . The set (s, a, r, s′, d) ∈ D represents the transitions, and d tells about the terminal of s′. To keep Q _Φ (s, a)

close to the Bellman equation, a mean square Bellman error (MSBE) is defined below: L Φ , D = E s , a , r , s ′ , d ∼ D Q Φ s , a − r + γ 1 − d m a x a ′ Q Φ s ′ , a ′ 2 , (13) where d equals 1 if s′ is terminal; else, it is 0. In the target networks, the target term is defined below: r + γ 1 − d m a x a ′ Q Φ s ′ , a ′ . (14) By minimizing the MSBE loss, it is possible to track this target by Q Φ s , a . An optimal action a* can be obtained by solving the following equation: a * = a r g m a x a Q * s , a , (15) where a = [ v m r , v d r ] T . The reward function for the optimal trajectory of the drone is as follows: r d r t = η d × R d r p t − R d r t R d r p t , (16) where R d r p t , R _dr (t) is the predicted optimal and actual route length of the drone, and η _P is the binary scaling factor. r m r t = η m × R m r p t − R m r t R m r p t , (17) where R m r p t , R _mr (t) is the predicted optimal and actual route length of the mobile robot, and η _m is the binary scaling factor.The total reward function is defined below: r t = r m r t + r d r t . (18) Collecting medical waste manually may cause health problems for the employees of the medical centers. A coordinated multi-drone mobile robot framework can collect medical waste and transfer it to disposal centers with minimum human intervention.

A hospital located in Bareilly, India, is considered for a medical waste transportation study. Currently, medical waste is transported in trucks. This system could expose the people involved in MW transportation to risks. In some situations, unauthorized transportation may occur. The drone possesses a camera and an inertial measurement unit (IMU) to create reference elevation movement, and the robot generates a probabilistic network of paths. The ground robot and aerial drone work collectively to create the multi-level platform. The real-time appearance-based mapping technique is used to localize the robot and map the environment. The IMU estimates the attitude using multinomial logistic regression. Both visual and inertial data are combined to produce odometry estimates. The ground robot with four mecanum wheels is a custom-designed platform. The UAV creates a 3D map with a ground path and altitude information using the IMU and a camera. The ground robot follows the tracks within the 3D map of the environment generated by the UAV. Synergistically, the robot drone platform is equipped with sensors such as a PIR sensor, infrared camera, and Intel RealSense D435 with IMU. The ground robot moves on predefined paths to slow, stop, or reroute its path for collection of medical waste. The UAV is controlled in tandem to collect the packet of medical waste and then steer away for transportation of the packet to the MW disposal centers.

The types of medical waste are classified and represented by colors (red, yellow, blue, and black). The considered medical wastes comprises 110 gauze ( ∼ 0.02 k g ) , 120 gloves ( ∼ 0.002 − 0.007 k g ) , 140 infusion bags ( ∼ 0.5 − 1 k g ) , 135 infusion bottles ( ∼ 3 − 5 k g ) , 132 infusion apparatus ( ∼ 0.001 − 0.004 k g ) , 125 syringe needles ( ∼ 0.02 − 0.004 k g ) , 132 tweezers ( ∼ 0.015 − 0.020 k g ) , and 128 syringe (∼0.002–0.004 kg). The mobile robot helps to form the MW packets with weights W m w m r ≤ W m w m r , m a x and W m w t r ≤ W m w t r , m a x . Python programming has been used to encrypt medical waste packets. Hash keys for these packets have been obtained and are mentioned below.

In a similar fashion, the next subsequent encrypted MW packet data are obtained. Let maximums of T _mr and T _dr be taken as 10 min and 25 min. The maximum velocities of the robot and drone are chosen as 5 km/h and 80 km/h, respectively. The various maximum values of transportation costs are taken as C _mr = 5 Rs/kg, C _dr = 50 Rs/kg, and C _dr = 40 Rs/kg. One hundred (100) samples of MW data are generated. After taking the mean velocities of the robot and drone at different time instants, the average velocities of the robot and drone are obtained as 2.48 km/h and 47.8 km/h, respectively. Next, the prediction error is calculated as 0.4%.

The posterior density was obtained with the transfer learning algorithm, and a predicted route for the mobile robots and drones is generated in a 3D environment Figure 4. Using the data related to the SoC of the battery and the MW weight, the proportional logit model is obtained: l n π optimal π nonotimal = 1.47 + 2.54 S o C + 1.24 W m w . (19)

The SoC of the battery of the drone and the weight of the medical waste packet are considered for the prediction of the optimal route length of the framework (18). The SoC of batteries varies from 0.2 to 0.8, and the weight of MW varies from 1 kg to 2 kg. The optimal predicted velocity of the robot and drone are shown in Figures 5 and 6, respectively (14)–(15). The rewards of actions done by robots and drones are obtained with a Q-learning mechanism using (16). The accumulative rewards are estimated as per (17) and shown in Figure 7. The coordinated framework has been rewarded for the optimal and successful delivery of medical waste from the collection point to the disposal center through transit points. At various iterations, the rewards vary from 0.7 to 0.9. The limitations of the various UAV–UGV coordination algorithms are mentioned in Table 1. None of these algorithms is applied in medical waste transportation. The effectiveness of the proposed learning-based UAV–UGV coordinated algorithm is shown by obtaining the results, and the features of this algorithm are mentioned in Table 2.

In this paper, a coordinated UAV–UGV framework is proposed for medical waste transportation to minimize human involvement. For optimal medical waste transportation, a learning-based mechanism has been implemented to predict the route length followed by the robots and the drone. The time-stamped medical waste packets are encrypted with hash keys. The predicted route length from the transit point to the disposal center has been obtained. The reinforcement learning algorithm has been considered for optimal successful and unsuccessful transportation of medical waste from collection points to disposal centers.

As the field of medical waste transportation continues to evolve, several potential future research directions can be explored to address emerging challenges and promote sustainable practices. Here are some areas that researchers might consider:

• Green technologies and sustainable practices: Examine and create eco-friendly technology for the transportation of medical waste. To lessen the influence of transportation on the environment, this could involve using hybrid or electric cars, renewable energy sources, and other environmentally friendly methods.