Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone

Савин, Сергей; Badr, Amer Al; Devitt, Dmitry; Fedorenko, Roman; Klimchik, Alexandr

doi:10.3390/app12115588

Cited by 8 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Soft aerial robots were developed for physical interactions [15,16]. Further, insect-inspired multicopters are presented with compliant frames to handle collisions [17] whereas compliant frames based on tensegrity [18] are proved helpful to reduce impact. Our prior work developed a collision-resilient MAV with compliant arms [19].…”

Section: Related Work a Impact-resilient Compliant Mavsmentioning

confidence: 99%

Online Search-Based Collision-Inclusive Motion Planning and Control for Impact-Resilient Mobile Robots

Liu

Campbell

et al. 2023

IEEE Trans. Robot.

View full text Add to dashboard Cite

The article develops an impact-resilient aerial robot (s-ARQ) equipped with a compliant arm to sense contacts and reduce collision impact and featuring a real-time contact force estimator and a non-linear motion controller to handle collisions while performing aggressive maneuvers and stabilize from highspeed wall collisions. Further, a new collision-inclusive planning method that aims to prioritize contacts to facilitate aerial robot navigation in cluttered environments is proposed. A range of simulated and physical experiments demonstrate key benefits of the robot and the contact-prioritized (CP) planner. Experimental results show that the compliant robot has only a 4% weight increase but around 40% impact reduction in drop tests and wall collision tests. s-ARQ can handle collisions while performing aggressive maneuvers and stabilize from high-speed wall collisions at 3.0 m/s with a success rate of 100%. Our proposed compliant robot and contact-prioritized planning method can accelerate computation time while having shorter trajectory time and larger clearances compared to A * and RRT * planners with velocity constraints. Online planning tests in partially-known environments further demonstrate the preliminary feasibility of our method to apply in practical use cases.

show abstract

Section: Related Work a Impact-resilient Compliant Mavsmentioning

confidence: 99%

Online Search-Based Collision-Inclusive Motion Planning and Control for Impact-Resilient Mobile Robots

Liu

Campbell

et al. 2023

IEEE Trans. Robot.

View full text Add to dashboard Cite

show abstract

“…(ii). Energy-optimized execution of tasks requested by the sites [35][36][37][38][39][40][41][42][43].…”

Section: Related Workmentioning

confidence: 99%

“…An ant-colony optimization or ACO-based heuristic framework 75 concentrates on the optimal deployment of GCS for minimization of recharge cost of drones as well as UAVs. Mixed-integer linear programming (MILP) model-based trajectory planning is proposed in [42,43]. Here, sensor nodes are themselves embedded with the UAV capacity (namely SN-UAV) and in their context, a flat topology routing protocol is proposed for energy optimization.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

Al-Naeem

Rahman

Banerjee³

et al. 2023

Sustainability

View full text Add to dashboard Cite

Crop monitoring and smart spraying have become indispensable parts of precision agriculture where unmanned aerial vehicles (UAVs) play a lead role. In particular, in large agricultural fields, aerial monitoring is a sustainable solution provided it can be performed in an energy-efficient manner. The existing literature points out that the research on precision agriculture using UAVs is still very minimal. In this article, we propose a support vector machine (SVM)-based UAV location management technique where UAVs change position over various portions or regions of a large agricultural field so that crops are properly monitored in an energy-efficient manner. Whenever a processing request is generated from any sensor in a part of the field, the UAV investigates with an SVM to decide whether to move on to the center of that field based on various parameters or characteristics such as region-id, packet-id, time of day, waiting times of the packets, the average waiting time of others within a predefined time window, location of the UAV, residual energy of the UAV after processing the packet, and movement after processing the packet. We use 70% of our data for training and the other 30% for testing. In our simulation study, we use accuracy, precision, and recall to measure in both contexts to determine the efficiency of the model, and also the amount of energy preserved is computed corresponding to every move. We also compare our approach with current state-of-the-art energy-preserving UAV movement control techniques which are compatible with the present application scenario. The proposed technique produced 6.5%, 34.5%, and 61.5% better results in terms of percentage of successful detection (PSD), composite energy consumption (CEC), and average delay (ADL), respectively.

show abstract

“…Another example of an aerial robot with a tensegrity shell is the 'Tensodrone' [26], wherein a soft shell with springs is introduced. A self-morphing 'Tensodrone' is later presented as extension work in [27]. The soft tensegrity design helps increase collision resilience at the cost of larger vehicle size and vibration.…”

Section: B Related Work: Tensegrity Structurementioning

confidence: 99%

A collision-resilient aerial vehicle with icosahedron tensegrity structure

Zha¹,

Wu²,

Kroeger³

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

We present the tensegrity aerial vehicle, a design of collision-resilient rotor robots with icosahedron tensegrity structures. The tensegrity aerial vehicles can withstand highspeed impacts and resume operation after collisions. To guide the design process of these aerial vehicles, we propose a modelbased methodology that predicts the stresses in the structure with a dynamics simulation and selects components that can withstand the predicted stresses. Meanwhile, an autonomous re-orientation controller is created to help the tensegrity aerial vehicles resume flight after collisions. The re-orientation controller can rotate the vehicles from arbitrary orientations on the ground to ones easy for takeoff. With collision resilience and re-orientation ability, the tensegrity aerial vehicles can operate in cluttered environments without complex collision-avoidance strategies. Moreover, by adopting an inertial navigation strategy of replacing flight with short hops to mitigate the growth of state estimation error, the tensegrity aerial vehicles can conduct short-range operations without external sensors. These capabilities are validated by a test of an experimental tensegrity aerial vehicle operating with only onboard inertial sensors in a previously-unknown forest.

show abstract

Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone

Cited by 8 publications

References 42 publications

Online Search-Based Collision-Inclusive Motion Planning and Control for Impact-Resilient Mobile Robots

Online Search-Based Collision-Inclusive Motion Planning and Control for Impact-Resilient Mobile Robots

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

A collision-resilient aerial vehicle with icosahedron tensegrity structure

Contact Info

Product

Resources

About