Review the Documentation

README.md

@@ -1 +1,80 @@
-# Robotic-Arm-simulation-
+# Robotic-Arm-simulation
+
+## Abstract
+
+A simulation of Robotic Arm in Gazebo Environment, made 3-D model with SolidWorks and controlled by ROS and Moveit framework.
+
+![Final](https://github.com/Aviral2002/Robotic-Arm-simulation/blob/main/Images%20And%20VIdeos/finalarm.png)
+
+## Motivation
+
+From manufacturing to automotive to agriculture, industrial robotic arms are one of the most common types of robots in use today.<br>
+Robotic arms are fast, reliable, and accurate and can be programmed to do an infinite number of tasks in a variety of environments. They are used in factories to automate execution of repetitive tasks, such as applying paint to equipment or parts; in warehouses to pick, select, or sort goods from distribution conveyors to fulfill consumer orders; or in a farm field to pick and place ripe fruits onto storage trays. And as robotic technologies develop and industrial environments become more connected, the capabilities of robotic arms expand to enable new use cases and business operation models.
+
+## Mechanical Aspects 
+
+3-D model of robotic arm was made in Solidworks part by part.
+
+Base of the arm-
+
+![base](https://github.com/Aviral2002/Robotic-Arm-simulation/blob/main/Images%20And%20VIdeos/base.png)
+
+Links of the arm-
+
+![link1](https://github.com/Aviral2002/Robotic-Arm-simulation/blob/main/Images%20And%20VIdeos/link1.png)
+
+![link2](https://github.com/Aviral2002/Robotic-Arm-simulation/blob/main/Images%20And%20VIdeos/link2.png)
+
+![link3](https://github.com/Aviral2002/Robotic-Arm-simulation/blob/main/Images%20And%20VIdeos/link3.png)
+
+![link4](https://github.com/Aviral2002/Robotic-Arm-simulation/blob/main/Images%20And%20VIdeos/link4.png)
+
+Gripper-
+
+![gripper](https://github.com/Aviral2002/Robotic-Arm-simulation/blob/main/Images%20And%20VIdeos/gripper.png)
+
+## Software Aspects
+
+### SolidWorks
+
+SOLIDWORKS is used to develop mechatronics systems from beginning to end. At the initial stage, the software is used for planning, visual ideation, modeling, feasibility assessment, prototyping, and project management. The software is then used for design and building of mechanical, electrical, and software elements.
+
+### ROS (Robot Operating System)
+
+ROS is an open-source, meta-operating system for your robot. It provides the services you would expect from an operating system, including hardware abstraction, low-level device control, implementation of commonly-used functionality, message-passing between processes, and package management.
+
+### MoveIt!
+
+MoveIt! is a framework package of Ros. The basic task of the MoveIt! system is to provide the necessary trajectories for the arm of a robot to put the end effector in a given place.
+
+## Applications
+
+Robotic arm can be used for multiple industrial applications, from welding, material handling, and thermal spraying, to painting and drilling. With some modifications they can be used for servicing nuclear power stations, welding and repairing pipelines on the ocean floor, remote servicing of utility power lines, or cleaning up radioactive and other hazardous wastes.
+Another example where Robotic-arm can be used is in the auto-manufacturing industry. Robots have been a boom to the auto-manufacturing industry. Most industrial robots work in auto assembly lines, putting cars together.Robots can do a lot of this work more efficiently than human beings because of its speed and relative precision. It also will significantly reduce worker injuries, including repetitive stress injuries. Additionally, the robot will turn out a more consistent product at a significantly cheaper cost than can humans.
+
+## Limitations
+
+A this is a simulation, real model of the arm will have to be made with high precision and have a high chance of inaccuracy.<br>Also the arm moves in rough trajectories with very less precision and the weight distribution and weight limit wil also a problem an weight of components and external weight could increase the stress on links and motors, and these situations could create a big problem in real life model.
+
+## Improvements
+
+Our aim would be to to improve the trajectory of the arm using planners in MoveIt and control it to pick various objects in simulation. And to make an interactive remote control for the arm.
+
+## Team Members
+
+1. [Ajay Sonwani](https://github.com/ajaysonwani)
+2. [Aviral Jain](https://github.com/Aviral2002)
+3. [Harsh Kumar](https://github.com/Harshkr03)
+4. Saksham Jaiswal
+
+## Mentors
+
+1. Diwahar
+2. Harshini S.
+
+## References
+
+1. [For installation of ROS noetic](http://wiki.ros.org/noetic/Installation/Ubuntu)
+2. [For installation of MoveIt](https://moveit.ros.org/install-moveit2/source/)
+4. [For installation of Gazebo](https://dev.px4.io/v1.10_noredirect/en/simulation/gazebo.html)
+

src/robot_config/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.1.3)
+project(robot7_config)
+
+find_package(catkin REQUIRED)
+
+catkin_package()
+
+install(DIRECTORY launch DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
+  PATTERN "setup_assistant.launch" EXCLUDE)
+install(DIRECTORY config DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION})

src/robot_config/config/cartesian_limits.yaml

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+cartesian_limits:
+  max_trans_vel: 1
+  max_trans_acc: 2.25
+  max_trans_dec: -5
+  max_rot_vel: 1.57

src/robot_config/config/chomp_planning.yaml

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+planning_time_limit: 10.0
+max_iterations: 200
+max_iterations_after_collision_free: 5
+smoothness_cost_weight: 0.1
+obstacle_cost_weight: 1.0
+learning_rate: 0.01
+smoothness_cost_velocity: 0.0
+smoothness_cost_acceleration: 1.0
+smoothness_cost_jerk: 0.0
+ridge_factor: 0.0
+use_pseudo_inverse: false
+pseudo_inverse_ridge_factor: 1e-4
+joint_update_limit: 0.1
+collision_clearance: 0.2
+collision_threshold: 0.07
+use_stochastic_descent: true
+enable_failure_recovery: false
+max_recovery_attempts: 5

src/robot_config/config/fake_controllers.yaml

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+controller_list:
+  - name: fake_body_controller
+    type: $(arg fake_execution_type)
+    joints:
+      - j1
+      - j2
+      - j3
+      - j4
+initial:  # Define initial robot poses per group
+#  - group: body
+#    pose: home
+
+  []

src/robot_config/config/gazebo_controllers.yaml

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+# Publish joint_states
+joint_state_controller:
+  type: joint_state_controller/JointStateController
+  publish_rate: 50

src/robot_config/config/joint_limits.yaml

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+# joint_limits.yaml allows the dynamics properties specified in the URDF to be overwritten or augmented as needed
+
+# For beginners, we downscale velocity and acceleration limits.
+# You can always specify higher scaling factors (<= 1.0) in your motion requests.  # Increase the values below to 1.0 to always move at maximum speed.
+default_velocity_scaling_factor: 0.1
+default_acceleration_scaling_factor: 0.1
+
+# Specific joint properties can be changed with the keys [max_position, min_position, max_velocity, max_acceleration]
+# Joint limits can be turned off with [has_velocity_limits, has_acceleration_limits]
+joint_limits:
+  j1:
+    has_velocity_limits: true
+    max_velocity: 3
+    has_acceleration_limits: false
+    max_acceleration: 0
+  j2:
+    has_velocity_limits: true
+    max_velocity: 3
+    has_acceleration_limits: false
+    max_acceleration: 0
+  j3:
+    has_velocity_limits: true
+    max_velocity: 3
+    has_acceleration_limits: false
+    max_acceleration: 0
+  j4:
+    has_velocity_limits: true
+    max_velocity: 3
+    has_acceleration_limits: false
+    max_acceleration: 0

src/robot_config/config/kinematics.yaml

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+body:
+  kinematics_solver: kdl_kinematics_plugin/KDLKinematicsPlugin
+  kinematics_solver_search_resolution: 0.005
+  kinematics_solver_timeout: 0.005

src/robot_config/config/ompl_planning.yaml

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+planner_configs:
+  AnytimePathShortening:
+    type: geometric::AnytimePathShortening
+    shortcut: true  # Attempt to shortcut all new solution paths
+    hybridize: true  # Compute hybrid solution trajectories
+    max_hybrid_paths: 24  # Number of hybrid paths generated per iteration
+    num_planners: 4  # The number of default planners to use for planning
+    planners: ""  # A comma-separated list of planner types (e.g., "PRM,EST,RRTConnect"Optionally, planner parameters can be passed to change the default:"PRM[max_nearest_neighbors=5],EST[goal_bias=.5],RRT[range=10. goal_bias=.1]"
+  SBL:
+    type: geometric::SBL
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+  EST:
+    type: geometric::EST
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0 setup()
+    goal_bias: 0.05  # When close to goal select goal, with this probability. default: 0.05
+  LBKPIECE:
+    type: geometric::LBKPIECE
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    border_fraction: 0.9  # Fraction of time focused on boarder default: 0.9
+    min_valid_path_fraction: 0.5  # Accept partially valid moves above fraction. default: 0.5
+  BKPIECE:
+    type: geometric::BKPIECE
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    border_fraction: 0.9  # Fraction of time focused on boarder default: 0.9
+    failed_expansion_score_factor: 0.5  # When extending motion fails, scale score by factor. default: 0.5
+    min_valid_path_fraction: 0.5  # Accept partially valid moves above fraction. default: 0.5
+  KPIECE:
+    type: geometric::KPIECE
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    goal_bias: 0.05  # When close to goal select goal, with this probability. default: 0.05
+    border_fraction: 0.9  # Fraction of time focused on boarder default: 0.9 (0.0,1.]
+    failed_expansion_score_factor: 0.5  # When extending motion fails, scale score by factor. default: 0.5
+    min_valid_path_fraction: 0.5  # Accept partially valid moves above fraction. default: 0.5
+  RRT:
+    type: geometric::RRT
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    goal_bias: 0.05  # When close to goal select goal, with this probability? default: 0.05
+  RRTConnect:
+    type: geometric::RRTConnect
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+  RRTstar:
+    type: geometric::RRTstar
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    goal_bias: 0.05  # When close to goal select goal, with this probability? default: 0.05
+    delay_collision_checking: 1  # Stop collision checking as soon as C-free parent found. default 1
+  TRRT:
+    type: geometric::TRRT
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    goal_bias: 0.05  # When close to goal select goal, with this probability? default: 0.05
+    max_states_failed: 10  # when to start increasing temp. default: 10
+    temp_change_factor: 2.0  # how much to increase or decrease temp. default: 2.0
+    min_temperature: 10e-10  # lower limit of temp change. default: 10e-10
+    init_temperature: 10e-6  # initial temperature. default: 10e-6
+    frountier_threshold: 0.0  # dist new state to nearest neighbor to disqualify as frontier. default: 0.0 set in setup()
+    frountierNodeRatio: 0.1  # 1/10, or 1 nonfrontier for every 10 frontier. default: 0.1
+    k_constant: 0.0  # value used to normalize expresssion. default: 0.0 set in setup()
+  PRM:
+    type: geometric::PRM
+    max_nearest_neighbors: 10  # use k nearest neighbors. default: 10
+  PRMstar:
+    type: geometric::PRMstar
+  FMT:
+    type: geometric::FMT
+    num_samples: 1000  # number of states that the planner should sample. default: 1000
+    radius_multiplier: 1.1  # multiplier used for the nearest neighbors search radius. default: 1.1
+    nearest_k: 1  # use Knearest strategy. default: 1
+    cache_cc: 1  # use collision checking cache. default: 1
+    heuristics: 0  # activate cost to go heuristics. default: 0
+    extended_fmt: 1  # activate the extended FMT*: adding new samples if planner does not finish successfully. default: 1
+  BFMT:
+    type: geometric::BFMT
+    num_samples: 1000  # number of states that the planner should sample. default: 1000
+    radius_multiplier: 1.0  # multiplier used for the nearest neighbors search radius. default: 1.0
+    nearest_k: 1  # use the Knearest strategy. default: 1
+    balanced: 0  # exploration strategy: balanced true expands one tree every iteration. False will select the tree with lowest maximum cost to go. default: 1
+    optimality: 1  # termination strategy: optimality true finishes when the best possible path is found. Otherwise, the algorithm will finish when the first feasible path is found. default: 1
+    heuristics: 1  # activates cost to go heuristics. default: 1
+    cache_cc: 1  # use the collision checking cache. default: 1
+    extended_fmt: 1  # Activates the extended FMT*: adding new samples if planner does not finish successfully. default: 1
+  PDST:
+    type: geometric::PDST
+  STRIDE:
+    type: geometric::STRIDE
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    goal_bias: 0.05  # When close to goal select goal, with this probability. default: 0.05
+    use_projected_distance: 0  # whether nearest neighbors are computed based on distances in a projection of the state rather distances in the state space itself. default: 0
+    degree: 16  # desired degree of a node in the Geometric Near-neightbor Access Tree (GNAT). default: 16
+    max_degree: 18  # max degree of a node in the GNAT. default: 12
+    min_degree: 12  # min degree of a node in the GNAT. default: 12
+    max_pts_per_leaf: 6  # max points per leaf in the GNAT. default: 6
+    estimated_dimension: 0.0  # estimated dimension of the free space. default: 0.0
+    min_valid_path_fraction: 0.2  # Accept partially valid moves above fraction. default: 0.2
+  BiTRRT:
+    type: geometric::BiTRRT
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    temp_change_factor: 0.1  # how much to increase or decrease temp. default: 0.1
+    init_temperature: 100  # initial temperature. default: 100
+    frountier_threshold: 0.0  # dist new state to nearest neighbor to disqualify as frontier. default: 0.0 set in setup()
+    frountier_node_ratio: 0.1  # 1/10, or 1 nonfrontier for every 10 frontier. default: 0.1
+    cost_threshold: 1e300  # the cost threshold. Any motion cost that is not better will not be expanded. default: inf
+  LBTRRT:
+    type: geometric::LBTRRT
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    goal_bias: 0.05  # When close to goal select goal, with this probability. default: 0.05
+    epsilon: 0.4  # optimality approximation factor. default: 0.4
+  BiEST:
+    type: geometric::BiEST
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+  ProjEST:
+    type: geometric::ProjEST
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+    goal_bias: 0.05  # When close to goal select goal, with this probability. default: 0.05
+  LazyPRM:
+    type: geometric::LazyPRM
+    range: 0.0  # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
+  LazyPRMstar:
+    type: geometric::LazyPRMstar
+  SPARS:
+    type: geometric::SPARS
+    stretch_factor: 3.0  # roadmap spanner stretch factor. multiplicative upper bound on path quality. It does not make sense to make this parameter more than 3. default: 3.0
+    sparse_delta_fraction: 0.25  # delta fraction for connection distance. This value represents the visibility range of sparse samples. default: 0.25
+    dense_delta_fraction: 0.001  # delta fraction for interface detection. default: 0.001
+    max_failures: 1000  # maximum consecutive failure limit. default: 1000
+  SPARStwo:
+    type: geometric::SPARStwo
+    stretch_factor: 3.0  # roadmap spanner stretch factor. multiplicative upper bound on path quality. It does not make sense to make this parameter more than 3. default: 3.0
+    sparse_delta_fraction: 0.25  # delta fraction for connection distance. This value represents the visibility range of sparse samples. default: 0.25
+    dense_delta_fraction: 0.001  # delta fraction for interface detection. default: 0.001
+    max_failures: 5000  # maximum consecutive failure limit. default: 5000
+body:
+  planner_configs:
+    - AnytimePathShortening
+    - SBL
+    - EST
+    - LBKPIECE
+    - BKPIECE
+    - KPIECE
+    - RRT
+    - RRTConnect
+    - RRTstar
+    - TRRT
+    - PRM
+    - PRMstar
+    - FMT
+    - BFMT
+    - PDST
+    - STRIDE
+    - BiTRRT
+    - LBTRRT
+    - BiEST
+    - ProjEST
+    - LazyPRM
+    - LazyPRMstar
+    - SPARS
+    - SPARStwo
+  projection_evaluator: joints(j1,j2)
+  longest_valid_segment_fraction: 0.005