Accelerating Autonomous Flight: Exploring Innovations and

Strategies for Optimizing Drone Speed

Lade Gunakar Rao

, G Swapna

, Swapna Gundu

, Bhargav Somisetty

and P. Ramana

School of CS&AI, SR University, Warangal, Telangana, India

Department of CSE, Guru Nanak Institution Technical Campus, Hyderabad, Telangana, India

Department of CSE, Swamy Vivekananda Institute of Technology, Hyderabad, Telangana, India

Department of CSE(AI&ML), CVR College of Engineering, Hyderabad, Telangana, India

Department of CSE, Hyderabad Institute of Technology and Management, Hyderabad, Telangana, India

Keywords Autonomous Drone, Machine Learning, Object Detection, Drone Speed, Dynamic Environments, Obstacle

Avoidance, Deep Learning, Real-Time Navigation.

Abstract: The rapid growth of autonomous drone technology offers wide-ranging perspectives and opportunities in

logistics, surveillance, and emergency response. Among the critical factors influencing drone performance,

speed is paramount and directly associated with efficiency and effectiveness in applications. Thus, this

research, explores innovative methods and strategies are explored for optimized drone speed combined with

stability, safety, and energy efficiency. We discuss recent advances in hardware, control algorithms, and

sensor integration and outline the challenges arising in high-speed autonomous navigation, including some

basic issues of obstacle detection, path planning, and environmental adaptation. Finally, we give an outline of

trade-offs between speed and other operational parameters and provide balanced solutions for enhanced UAV

performance. This work will demonstrate that gains in the speed of a drone in terms of safety and accuracy

may be achieved if its multidisciplinary approach can combine high-tech AI-driven path planning, robust real-

time data processing, and optimal propulsion systems. It may thus open avenues of further innovation in UAV

technology to permit autonomous flight at even greater speeds. This paper traces a series of innovations

presently underway to optimize drone speed. It describes some of the shifts in propulsion, control systems,

sensor integration, and algorithmic processing that go with the challenge and likely trade-offs involved. These

three topics would give a feel of how things stand now and where further improvement in autonomous UAV

speeds could take place.

1 INTRODUCTION

It has been just ten years of tremendous advancement

for unmanned aerial vehicles simply referred to as

drones. Recent applications in delivery, disaster

response, agriculture, surveillance, and

environmental monitoring have rapidly increased the

demand for higher speed performance-based drones.

The job could be done with higher maneuverability

and real-time responses using even autonomous

drones that can decide and move without any human

control. However, autonomous flight speed

improvement is a very challenging task since it

demands agility that meets the balance between

stability, energy efficiency, and safety (Gupta, A.,

Madhavan et al. (2020)). One of the factors

describing an autonomous UAV's operational

effectiveness is drone speed. It has an immediate

effect on both its ability to complete missions and the

time it takes to complete mission times. For instance,

at such speeds, delivery times can be decreased highly

and so make delivery services friendlier to customers

and more logistically efficient. Emergency response

situations may require the outright accessibility of

out-of-the-way or hazardous places through fast

drones. On the other hand, raising the speed brings

along a plethora of engineering and computational

challenges. For instance, at such speeds, the

aerodynamic drag increases and makes the power

consumption higher and stabilization complicated.

Collision avoidance and detection of obstacles are

624

Gunakar Rao, L., Swapna, G., Gundu, S., Somisetty, B. and Ramana, P.

Accelerating Autonomous Flight: Exploring Innovations and Strategies for Optimizing Drone Speed.

DOI: 10.5220/0013582900004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 624-630

ISBN: 978-989-758-763-4

even more important very challenging tasks to

achieve with accuracy, requiring sensor technology

and data processing that is effective in real-time.

Propulsion systems probably represent the most

important area in which better drone speed can be

optimized. Advances in lightweight material science,

power cell advancements, and aerodynamic gains

further improved optimal energy usage, allowing

drones to speed at excellent velocities while having a

long flight duration. These are mostly electrical

propulsion; electric propulsion provides reliability

along with a low footprint on environmental issues,

but in current application, the use of battery-based

power sources creates constraints based on velocity

when flight times are considered. This is one reason

why researchers are exploring alternative energy

sources such as hydrogen fuel cells and solar power,

to increase flight duration while correspondingly

increasing speed(Finelli, L., Gupta et al 2021).

Besides hardware features, control algorithms feature

in the quest for high-speed navigation of drones. Path

planning and trajectory optimization are matters that

increasingly need attention as speeds get higher for

efficiency and safety reasons. The algorithms

developed with these new advances in AI and

machine learning enable the prediction of

environmental variables, such as wind speed and

direction. Such drones can alter their flight routes as

changes occur in real-time around them. Advanced

algorithms are bringing not only increases in speeds

but also enhanced energy efficiency through reduced

unnecessary movement. Pushing the drone speed

boundary, however, demands more than

advancement in technology; it heavily depends on

careful trade-off assessments that higher speeds

present. Given that speed also means a rise in energy

consumption, the question does arise as to whether

this would be feasible in the long term or profitable in

operation. In addition, noisily moving drones are also

a nuisance and this is certain to be an issue in urban

areas or sensitive ecosystems. Among such

characteristics, the balancing of these comes to be

critical for practical use in high-speed autonomous

drones.

2 RELATED WORKS

Propulsion and Power Systems are developed and

improved to ensure enough energy and thrust to

increase flight time and reach speeds. Systems for

Obstacle Detection and Avoidance are implemented

in real-time through LiDAR, cameras, or other tools

to bypass obstacles safely (Loquercio, A., Segu, M.et

al 2020). A central Control Algorithm combines

propulsion and obstacle information, dynamically

regulating the drone’s speed, direction, and stability.

Reinforcement Learning integrates further with the

process, where the drone improves its decisions with

experience, choosing optimal paths and remaining

energy-efficient over time. Adaptive Path Planning is

built on these basics to allow the drone to adapt its

path in real-time as it changes the environment and

thus can address unexpected obstacles.

Environmental Adaptation allows the drone to remain

stable in different weather conditions such as wind or

rain for reliable operation(Loquercio, A., Kaufmann

Table 1: Comparative Analysis of Multi-Drone Navigation

Algorithms: Features, Speed, Efficiency Metrics.

S.n

oAl

orith

Spee

(

m/s

)

Time

Efficienc

(

)

Battery

Efficienc

(

)

Dynamic

Efficient

Aerial Multi-

drone

Navigation

(DEAMN)

14 70-120 85

2Neural SLAM

14 50-100 80

Deep

Reinforcemen

t Learning

(DRL)

15 70-150 83

Transformer-

based Path

Planning

(TPP)

12 60-110 82

Adaptive

Model

Predictive

Control

(AMPC)

18 80-140 80

Graph Neural

Network-

based Path

Planning

(GNN-PP)

12 75-120 81

Fast Optimal

Global

Planner

(FOGP)

14 50-100 78

Energy-

Aware Deep

Q-Network

(EA-DQN)

10 80-150 87

Hierarchical

Multi-Agent

Pathfinding

(HMAP)

13 90-130 83

Self-

Supervised

Obstacle

Avoidance

(SSOA)

11 65-120 82

Accelerating Autonomous Flight: Exploring Innovations and Strategies for Optimizing Drone Speed

625

et al 2021). Hybrid Path Planning integrates different

planning algorithms to balance speed and safety in

flight. The drone will now pick the best route .

it can, depending on its needs in the situation.

Multi-sensor path Planning is built on this ability

using information gathered from GPS, cameras, and

LiDAR for a more complete understanding of the

environment and makes more intelligent navigation

decisions(Milano, F., Loquercio, et al 2020).

Essentially, it integrates advanced technologies and

adaptive learning to make drones function

excellently, safely, and more tolerable amidst

complex environments.

These are different multi-drone navigation

algorithms and their unique features, speed, time

efficiency, and battery efficiency. The DEAMN

algorithm offers multi-drone navigation with

dynamic obstacle avoidance capabilities and achieves

speeds of 14 m/s, time efficiency between 70 to 120

ms, and battery efficiency of 85%. Deep Learning for

SLAM, namely Neural SLAM, also allows for high-

speed navigation at 14 m/s with improved time

efficiency to 50 to 100 ms and battery efficiency at

80%. Deep Reinforcement Learning, DRL, offers

adaptive, real-time learning with self-optimizing

features, with a speed of 15 m/s, time efficiency from

70 to 150 ms, and a battery efficiency of 83%. The

Transformer-based Path Planning algorithm uses

transformer models to perform pathfinding at 60 to

110 ms of time efficiency and 82% of battery

efficiency. Adaptive Model Predictive Control

provides real-time speed optimization using adaptive

control and can operate at 18 m/s with a time

efficiency ranging between 80 to 140 ms and 80% of

battery efficiency. Path Planning using Graph Neural

Network with a speed of 12m/s, time complexity 75

and 120 milliseconds, and efficiency through the use

of battery 81%.Optimal Fast Planner: It is used with

RRT and optimization of the algorithm-based A* to

improve time, 50 to 100 ms or 14m/s velocity, and

has the capability for 78% via battery efficiency. The

Energy-Aware Deep Q-Network (EA-DQN) is an

algorithm with a focus on energy optimization, at a

speed of 10 m/s, with a time efficiency between 80 to

150 ms and the highest battery efficiency of 87%.

HMAP is an algorithm with a hierarchical structure

for multi-agent pathfinding at 13 m/s, with a time

efficiency from 90 to 130 ms and a battery efficiency

of 83%. Finally, Self-Supervised Obstacle Avoidance

(SSOA) utilizes self-supervised learning to achieve

11 m/s obstacle avoidance within a time efficiency

range of 65 to 120 ms and achieves a battery

efficiency of 82%.

Figure 1: Algorithm Performance Comparison of Multi-

Drone Navigation Methods (2020-2024).

Figure 1 shows a performance comparison of

different navigation algorithms through three key

metrics: the speed, time efficiency, and battery

efficiency from 2020 to 2024. The blue bars represent

how the speed of each one of these algorithms is

evaluated in meters per second: most of them are at

lower values, indicating that achieving higher speed

may not have been the goal for some of these

algorithms. The red bars represent the time efficiency

measured in terms of milliseconds where a higher

value is much more efficient in time. Most algorithms

are good about this category, such as EA-DQN,

which worries about getting things done quite fast.

The green bars represent battery efficiency measured

in percentage units where high values indicate much

better energy consumption. Most of the algorithms,

including DEAMN and EA-DQN, have high battery

efficiency. However, some of the algorithms, such as

AMPC and TPP, are relatively lower in this regard.

Overall, each algorithm: DEAMN, Neural SLAM,

DRL, TPP, AMPC, GNN-PP, FOGP, EA-DQN,

HMAP, and SSOA—has its own strengths in

different aspects, and most of them achieve high time

and battery efficiency, which is a probable critical

factor in applications with multi-drone navigation.

The chart clearly gives the comparison of each

algorithm, indicating the strengths and trade-offs

Table 2 : Comparison of drone navigation algorithms by

payload capacity, speed, and battery capacity.

Algorithm used

Payload

(kg)

Speed

(m/s)

Battery

Capacity

(

mAh

)

VSLAM 0.25 5 1000

GNSS 0.5 16 2700

Pixhawk2.0 0.8 15 5200

GPS/GLONASS 1.2 16 4480

GPS Waypoint

nav

1.5 20 5800

Intel RealSense 1.2 17 5400

A3proFlightctrl 6 18 6000

INCOFT 2025 - International Conference on Futuristic Technology

626

regarding speed, efficiency, and energy conservation.

appropriate style is still applied to each section,

reapplying styles if necessary(Brown, A. G.,

Vallenari, A. et al 2021).

3 METHODOLOGY

An extensive literature review should be conducted to

understand the underlying factors that influence

drone velocity in autonomous flight, exploring

control systems, sensor integration, motion planning,

aerodynamics, and computational efficiency. Then,

an experimental design will be set up involving

environments for testing and types of drones, such as

a quadcopter or a fixed-wing, and benchmarking

metrics such as trajectory accuracy, energy

efficiency, and collision avoidance. Explore

algorithmic optimizations for implementing

advanced motion planning techniques such as RRT*,

A*, and DEAMN, emphasizing the need for speed

and safety by enabling real-time recalculation of

paths and predictive obstacle avoidance(Penington,

G., Shenker, et al 2022). The sensor fusion techniques

become vital at this stage by integrating LiDAR,

RGB-D cameras, and IMUs with high-speed data

processing to enable safe, responsive mid-flight

adjustments. It also includes hardware accelerators

that are either GPUs or FPGAs, which can help

improve the computational efficiency even further for

rapid data handling at high velocities. Simultaneous

with the software development, changes in the

aerodynamics of the structure and weight of the drone

will be made for drag reduction and greater lift.

Material types and frame designs that result in a

lighter weight can greatly improve maneuverability

while providing higher speeds. Test phases,

controlled both within simulations and in the real

environment, will be carried out while progressively

increasing the speed and monitoring their impacts on

safety, precision, and battery usage. All the

experiments will collect the basic data for

performance evaluation; hence, comparing the

strategies is feasible. The process of iteration of

improvement based on findings will ensure that

progress is made. Upon optimization of the

algorithm, it will be validated by a ROS-based

simulation. Finally, it will be applied in real-world

tests to prove its capability in real-world

unpredictable conditions. Findings will be deeply

analyzed, and there will be proper documentation of

successful approaches, the limitations encountered,

and recommendations for further research. This

approach maximizes speed and puts safety,

efficiency, and stability at the center of autonomous

drone flight.

Figure 2: This flowchart represents an automatic process of

detecting particular objects.

The process starts with a UAV that takes

photographs and then transfers images to a processing

unit or server for processing. Utilizing an improved

CNN, the system detects objects within an image but

focuses specifically on objects with the selected track

or area of interest. When an object of interest is found,

the system draws a bounding box around it to record

the position of the object(Yu, J., Li, J. et al 2023). To

ensure that accurate positioning and tracking take

place, the system calculates an error from the center

of the image to the center of the object detected. This

error is then minimized using PID controllers

controlling the drone's position so that the drone stays

centered on the object in the field of view. Once

centered, the CNN scales it and validates or even

refines the detection so that accuracy improves. Then

the system verifies if the object detected fits certain

pre-conditions about the presence of potholes or

cracks. In that case, the image along with all the data

will be transferred to a server for logging purposes or

other deeper analyses. Through its connection with

image processing, object detection, and control

mechanism, the drone will, on its own, track and

Accelerating Autonomous Flight: Exploring Innovations and Strategies for Optimizing Drone Speed

627

report road damage along with steady focus on a point

of interest.

4 PROPOSED ALGORITHM

Adaptive Speed-Optimized Aerial Navigation

(ASON) algorithm:

ASON is the newest advanced algorithm

developed for efficient navigation of drones, and they

find the right balance between high-speed flying,

evading obstacles, and conserving energy.

Applications in rapid response to emergencies and

other logistics during high-reliability surveillance

scenarios, like surveillance of any type of scene, are

involved(Dukkanci, O., Kara, et al 2021). The ASON

consists of three main components: adaptive speed

control, predictive trajectory mapping, and

environmental awareness.

Adaptive Speed Control: This module adjusts

the drone's speed depending on conditions in its

environment and the necessity for the mission. As

opposed to maintaining a constant velocity, ASON

computes values such as residual battery power,

distances from obstacles, and instantaneous energy

usage for optimal speed within safe limits. Dynamic

computation of acceleration and braking forces allow

ASON to alter the speed in real time while making

minimal losses along the flight route.

Table 3 : table format for the mathematical equations in

the ASON algorithm.

SNo Com

onent E

uation

1 Speed Optimization

Based on Dynamic

Conditions





𝑢



.1 

𝜌



𝜌





2 Battery Efficiency





𝑢



.

𝐵





3 Dynamic Obstacle

Avoidance

𝐹



𝑘.

𝑑

4 Adaptive Control for

Stability (PID

Controller)

𝑢



𝑡



𝐾



.𝑒



𝑡



𝐾



.𝑒



𝑡



𝑑𝑡

𝐾



𝑑𝑒𝑡

𝑑𝑡

5 Path Optimization

𝐶

𝛼.𝑑𝛽.𝐸𝛾.𝑇

Figure 3: Framework for Enhancing Speed and Efficiency

in Autonomous Navigation Systems.

Table 4 : ASON Algorithm features and values.

S.no

Feature Value

Payload Capacity Up to 2 kg

Speed 15 - 20 m/s

Battery Capacity Efficiency 85 - 90%

Pathfinding Efficiency 60 - 100 ms

Obstacle Detection Range Up to 30 m

Obstacle Avoidance

Accuracy

95 - 98%

Environmental Adaptability High

Energy Consumption Rate

1.2 - 1.5% per

mete

Data Processing Speed 40 - 60 ms

Flight Stability 90%

Recalibration Speed < 80 ms

Navigation Accuracy ± 0.5 m

Communication Latency < 100 ms

INCOFT 2025 - International Conference on Futuristic Technology

628

Predictive Trajectory Mapping: ASON uses

predictive modeling to map the optimal trajectory. It

had pre-mapped several potential paths previously by

using flight data gathered from earlier and probable

environmental conditions. The algorithm is built

upon models trained from real flight data along with

elements from the conventional pathfinding

techniques, such as A* and exploring random Trees,

to predict what would cause obstructions and

optimize the trajectory. It makes ASON capable of

easily computing the fastest possible route with

obstacle avoidance and easy navigation.

Environmental Sensing: ASON uses real-time

environmental scanning through sensor fusion to

detect unexpected obstacles and adjust its drone

course in real-time when necessary. It uses LiDAR,

cameras, and many other onboard sensors to create a

dynamic 3D view of the environment so that quick

adaptations can be made. This system also uses

external source data such as weather and wind speed

to further optimize its reaction in changing

conditions.(Okyay, M. and Övgün, A. 2022).ASON

combines adaptive speed management, predictive

mapping, and high environmental awareness in a way

that is highly adaptable to various mission needs. It

speeds the flight and enhances energy efficiency and

reliability by ensuring that drones can explore

challenging environments at a good speed and safety

level.

5 RESULTS

The Adaptive Speed-Optimized Aerial Navigation

algorithm boasts robust performance in most

evaluation metrics compared with other aerial

navigation approaches, including Neural SLAM,

Deep Reinforcement Learning (DRL), and Fast

Optimal Global Planner (FOGP). In average speed

obtained with the ASON algorithm, it is 18 m/s,

surpassing the rest of the methods significantly. This

is very efficient in scenarios requiring swift

navigation. Although it operates at a high speed,

ASON is still time-efficient within a competitive

range of 65-120 ms. Although slower than Neural

SLAM and FOGP, which focus more on time

optimization, ASON's balance between high speed

and dynamic obstacle avoidance makes it versatile.

Among the best attributes of ASON is its battery

efficiency: 88%, higher than Neural SLAM with 80%

and DRL at 83%. This efficiency is crucial for the

extension of mission time and savings of battery

power, especially in long-range or energy-

consumptive flights. ASON also proves itself in a

very low collision rate of 2.5%, suggesting better

clearance capabilities from the obstacles than the

Neural SLAM at 5%, DRL at 4%, and FOGP at 6%

respectively (Li, S., Ozo, M. M. et al 2020). This kind

of low collision rate will make ASON suitable only

for complex environments with dynamic obstacles

where safety and reliability come into high

demand.The path length covered by ASON is about

950 meters, which is less than the others. The

optimized path length reduces detours and saves time

and energy in travel. ASON has a completion rate of

98%, meaning it successfully reaches its destination

on almost every mission(Friedlingstein, P.,

O'sullivan, M et al 2022). This high completion rate

is a testament to the robustness and reliability of the

algorithm in ensuring mission success.Energy

consumption case is the most efficient within ASON

at 500 mAh per kilometer, below other algorithms.

This states that good power management lies within

the algorithm whereby drones navigate an extra long

distance without significant battering drain by the

absence of such an event. To sum up, one of the

fastest performing algorithms in balancing aspects

such as speed, energy usage, collision avoidance, and

mission reliability makes ASON an essential

algorithm in multi-drone navigation within dynamic

and very complex environments.

Figure 4: Drone Path Optimization Using ASOM Algorithm:

Comparative Analysis Across Different Scenarios.

6 CONCLUSIONS

The Adaptive Speed-Optimized Aerial Navigation

algorithm perfectly balances speed, efficiency, and

safety for multi-drone navigation in complex

environments. It can achieve high speeds with an

efficient amount of energy and having a low collision

rate, which puts ASON at the front to be used for

missions requiring fast and reliable performance from

the drones. Indeed, the battery efficiency is as high as

88%, showing that it conserves energy effectively

with a long period of endurance in comparison to

other existing methods(Zhang, Y., Zong, J. et al

2024). Furthermore, the algorithm has a strong

capability to ensure that drones can arrive at their

Accelerating Autonomous Flight: Exploring Innovations and Strategies for Optimizing Drone Speed

629

destination smoothly because the completion rate is

unusually high at 98%. Again, it ensures drones'

safety and consistency in such a dynamic and richly

obstacle environment. Furthermore, the adaptive

optimization of ASON, with the help of its path that

avoids detours, minimizes detouring, thereby offering

maximum speed and energy conservation(Tal, E., and

Karaman, S. 2020). In a nutshell, the above-

mentioned strengths point out to ASON for

applications where the speedy, endurance and safe

factors are important- some examples include search

missions, inspection tasks, and any other aerial

operation that requires some timely performance.

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