Amaran: An Unmanned Robotic Coconut Tree Climber and Harvester

Megalingam, Rajesh Kannan; Manoharan, Sakthiprasad Kuttankulangara; Mohandas, Sreekanth Makkal; Vadivel, Shree Rajesh Raagul; Gangireddy, Rajesh; Ghanta, Sriharsha; Kumar, K. Sai; Teja, P Surya; Sivanantham, Vinu

doi:10.1109/tmech.2020.3014293

Cited by 36 publications

(18 citation statements)

References 32 publications

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“…(j) Amaran [87]. With the equivalent yield of 25 to 30 human harvesters the Berry 5 robot has a picking speed of 8 s per fruit, moving through strawberries beds at a speed of 1.6 km/h, harvesting up to eight acres of strawberries a day.…”

Section: Wheels With Actuatormentioning

confidence: 99%

“…In addition to causing serious injuries, falling a coconut tree can be fatal. In this sense, the Indian researchers Megalingam et al developed Amaran, an unmanned robotic coconut tree climber and harvester [87]. The Amaran robot climbs the coconut trees through a light mechanical structure composed of eight mechanum wheels, four located at the top and four at the bottom.…”

Section: Wheels With Actuatormentioning

confidence: 99%

“…Taking advantage of the IoT concept, the Amaran robot can be controlled through an application for smartphones, via the Bluetooth communication protocol. After several tests, Amaran proved to be able to successfully climb trees up to 15.2 m in height, with slopes of 30 • and with diameters ranging from 0.66 m to 0.92 m [87]. Although the total harvest time (preparation and harvest time) of the Amaran (21.9 min) is longer than that of a professional climber (11.8 min), the robot can climb as many coconut trees as necessary without exposing the human operator to harmful work to his health or even eventual fatal accidents.…”

Section: Wheels With Actuatormentioning

confidence: 99%

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Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

2021

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The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.

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Section: Wheels With Actuatormentioning

confidence: 99%

Section: Wheels With Actuatormentioning

confidence: 99%

Section: Wheels With Actuatormentioning

confidence: 99%

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Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

2021

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“…The extracted features are then fed into the fully connected layer, which also has exponential linear unit (ELU) as the activation function, followed by an LSTM with softmax activation. The softmax function is given by Equation (5). The outputs of the softmax function are always bounded between the range [0,1] and the resultant probabilities add up to be 1, thus, forming a probability distribution.…”

Section: Decision Networkmentioning

confidence: 99%

“…Cleaning requires maximum area coverage. Recently, based on the principles of autonomous area coverage, many robots have been deployed in essential life aspects such as underwater operations [ 1 ], de-mining [ 2 , 3 ], agriculture [ 4 , 5 ], painting [ 6 ], rescue operations [ 7 ], tiling robotics [ 8 , 9 , 10 ], ship hull cleaning [ 11 , 12 ], benchmarking for inspection [ 13 ], pavement sweeping [ 14 ], and so on. Currently, cleaning robots for domestic and industrial markets are in heavy demand.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot

Apuroop

Elara

et al. 2021

Sensors

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One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are innovative solutions for such a coverage problem. These new kinds of robots can be deployed in the cases of cleaning, painting, maintenance, and inspection, which require complete area coverage. Tiling robots’ objective is to cover the entire area by reconfiguring to different shapes as per the area requirements. In this context, it is vital to have a framework that enables the robot to maximize the area coverage while minimizing energy consumption. That means it is necessary for the robot to cover the maximum area with the least number of shape reconfigurations possible. The current paper proposes a complete area coverage planning module for the modified hTrihex, a honeycomb-shaped tiling robot, based on the deep reinforcement learning technique. This framework simultaneously generates the tiling shapes and the trajectory with minimum overall cost. In this regard, a convolutional neural network (CNN) with long short term memory (LSTM) layer was trained using the actor-critic experience replay (ACER) reinforcement learning algorithm. The simulation results obtained from the current implementation were compared against the results that were generated through traditional tiling theory models that included zigzag, spiral, and greedy search schemes. The model presented in the current paper was also compared against other methods where this problem was considered as a traveling salesman problem (TSP) solved through genetic algorithm (GA) and ant colony optimization (ACO) approaches. Our proposed scheme generates a path with a minimized cost at a lesser time.

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Modeling of novel circular gait motion through daisy sequence fitting algorithm in a vertical climbing snake robot

Megalingam,

Senthil,

Raghavan

et al. 2023

Journal of Field Robotics

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Snake robots can be used in multiple tasks, like, climbing, surveillance, pipe inspection, welding, and so forth. Current gaits of snake robots do not support rotation on a single horizontal plane in a circular fashion of a pole or tree at a fixed height. This makes snake robot extremely difficult to take the end effector to the desired target position and orientation. In addition, torque requirement of individual actuators or links of snake robot proportionally increases based on the number of links to be lifted while performing any task. In this paper, we propose a new gait called circular gait using the Daisy Sequence Fitting algorithm to solve the problems of circular rotation on a horizontal plane at a certain height with low torque. A sliding mode (SM) controller is implemented to achieve the circular gait's required position dynamically. Simulation results show that using the proposed circular gait, the end effector can be moved to any point on the circumference of the fixed horizontal plane of the pole or tree with a lesser torque. For the 0.5 kg module, circular gait moved the end effector to the target point using only 4.5 N m torque. The SM controller outperforms the proportional, integral, and derivative controllers in terms of response characteristics.

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Amaran: An Unmanned Robotic Coconut Tree Climber and Harvester

Cited by 36 publications

References 32 publications

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot

Modeling of novel circular gait motion through daisy sequence fitting algorithm in a vertical climbing snake robot

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