Service Arms with Unconventional Robotic Parameters for Intricate Workstations: Optimal Number and Dimensional Synthesis

Abstract

A task-oriented design strategy is presented in this paper for service manipulators. The tasks are normally defined in the form of working locations where the end-effector can work while avoiding the obstacles. To acquire feasible solutions in cluttered environments, the robotic parameters (D-H parameters) are allowed to take unconventional values. This enhances the solution space and it is observed that, by inducing this flexibility, the required number of degrees of freedom for fulfilling a given task can be reduced. A bilevel optimization problem is formulated with the outer layer utilizing the binary search method for minimizing the number of degrees of freedom. To enlarge the applicability domain of the proposed strategy, the upper limit of the number of joints is kept more than six. These allowable redundant joints would help in providing solution for intricate workcells. For each iteration of the upper level, a constrained nonlinear problem is solved for dimensional synthesis of the manipulator. The methodology is demonstrated through a case study of a realistic environment of a cluttered server room. A 7-link service arm, synthesized using the proposed method, is able to fulfill two different tasks effectively.

Academic Editor:Shahram Payandeh

Mechanical Engineering Department, IIT Ropar, Rupnagar 140001, India

Received 25 November 2015; Revised 17 February 2016; Accepted 3 March 2016

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The tremendously increasing variety in the robotic services leads to less repetitive tasks . To provide solution to the resulting variations, adaptive design techniques and realization strategies have taken the attention of the researchers. Given a cluttered environment with fixed obstacles, as shown in Figure 1, tasks are generally defined as working locations for the robot end-effector. For such intricate workstations with narrow passages, manipulability needs to be induced in the required design to maneuver within the cluttered workcells. Focus of the work is to determine adaptive number of degrees of freedom (dof) for given work scenarios. There is no limitation of keeping degrees of freedom as six or less. To acquire even one connecting path in the given cluttered environments, a design with even larger dof may be used. Novelty of the work lies in enhancing the solution space through unconventional values of the robotic parameters.

Figure 1: A server room environment with task space locations.

[figure omitted; refer to PDF]

In recent works, Yang and Chen [1] presented a study on optimizing the number of degrees of freedom for given tasks. The work is limited to specific conditions, without the description of any obstacles and/or workcells. In another work by Zhang and Wang [2], kinematic redundancy is utilized for avoidance of given obstacles. A fixed degrees-of-freedom manipulator is utilized in the work and no general strategy is provided for any given task and/or workcell. The techniques used to avoid collisions are the utilization of kinematic redundancy and/or having movable platform in most of the works. Modularity in robotic arms is worked upon by few researchers [3, 4] for providing a solution for changes in the environments. Normally, in the modular development strategies, a configuration of modular components is changed and then a systematic method is developed to compute its robotic parameters, thus formulating the kinematic equations. However, to design a service arm for any given spatial workcell, a general platform is required, with least input required from the user. Importance of the customized design of robotic arms has been discussed in recent works and the algorithms have been presented either with fixed degrees of freedom or with fixed or no environment [5, 6]. The field of task-based design needs to be further explored for algorithms adaptive to given workcells. This work provides one such solution to deal with given cluttered environment, through unconventional robotic parameters and by trading-off the requirement of number of degrees of freedom.

2. Problem Formulation

A nested optimization approach is proposed in this work with all the robotic parameters as design variables in the inner loop for dimensional synthesis and a unidirectional problem solving in number of dof in the outer loop. Important aspects related to the problem are the utilization of unconventional robotic parameters and varying number of degrees of freedom:

(1) Flexibility in the values of robotic parameters (D-H parameters in this work) leads to a larger search domain, which is required for a feasible solution in intricate workstations. For highly cluttered environments, this aspect is expected to play an important role. Related to this work, for a [figure omitted; refer to PDF] -link manipulator, Patel and Sobh [7] utilized a larger range of D-H parameters. The adaptable modules are fabricated to adapt the unconventional values of D-H parameters such as adaptable links [8] with adaptable connectors. The proposed methodology provides a general platform which can provide a robotic arm design corresponding to a given workstation, defined by a workspace model.

(2) The number of dof is not confined to any predefined value. Kinematic redundancy is kept acceptable in the varying number of dof. This is to gain the inherent advantages of a large number of dof, if required, according to the work scenario.

The proposed strategy is illustrated through realistic workcells, with the changes in number of task space locations (TSLs).

2.1. Varying Number of Degrees of Freedom and Unconventional Robotic Parameters

For an [figure omitted; refer to PDF] -linked serial manipulator, D-H convention is used to attach the reference frames to the serially joined links (refer to [9]). To define the relation between [figure omitted; refer to PDF] th and [figure omitted; refer to PDF] th links, [figure omitted; refer to PDF] , four parameters-twist angle ( [figure omitted; refer to PDF] ), link length ( [figure omitted; refer to PDF] ), joint offset ( [figure omitted; refer to PDF] ), and joint angle [figure omitted; refer to PDF] -are associated.

As shown in Figure 2, the link parameters (( [figure omitted; refer to PDF] ), ( [figure omitted; refer to PDF] )) and joint offset ( [figure omitted; refer to PDF] ) are fixed to provide a manipulator configuration. With [figure omitted; refer to PDF] values of [figure omitted; refer to PDF] , this configuration will attain one particular posture. By varying the values of each ( [figure omitted; refer to PDF] ), the posture of this configuration can be changed; that is, position of the end-effector can be varied to reach the desired locations (TSLs)-say " [figure omitted; refer to PDF] " in number.

Figure 2: D-H representation.

[figure omitted; refer to PDF]

A transformation matrix, using D-H parameters, defines transformation of frame [figure omitted; refer to PDF] relative to frame [figure omitted; refer to PDF] , represented as [figure omitted; refer to PDF] , and is computed as [figure omitted; refer to PDF]

For [figure omitted; refer to PDF] dof, comprising [figure omitted; refer to PDF] frames, [figure omitted; refer to PDF] number of transformation matrices are computed. The concatenated matrix provides the required transformation from frame- [figure omitted; refer to PDF] corresponding to the end-effector to frame- [figure omitted; refer to PDF] attached to the base: [figure omitted; refer to PDF]

The robotic parameters in manipulator kinematic equations are dependent on number of dof. For a single posture of an [figure omitted; refer to PDF] -link manipulator, total [figure omitted; refer to PDF] parameters are required to compute kinematic equations. Since the value of " [figure omitted; refer to PDF] " is not defined a priori , the total number of design variables will vary with each change in dof [figure omitted; refer to PDF] . This is handled through a nested optimization problem formulation.

Apart from the possibility of using redundant joints in the design, if and when required, the robotic parameters are also kept flexible in the proposed methodology. Figures 3(a) and 3(b) present the significance of conventional twist angles with normal values as [figure omitted; refer to PDF] or [figure omitted; refer to PDF] and unconventional twist angles values. The latter provides a larger solution space and thus a possibility of getting solution even in highly constrained workcells.

Figure 3: Unconventional robotic parameters.

(a) Two links connected at conventional values of twist angle

[figure omitted; refer to PDF]

(b) Illustration of unconventional twist angles, which provide flexibility to the design process

[figure omitted; refer to PDF]

2.2. Collision Avoidance

To work on a general problem with any given cluttered workspace, an obstacle avoidance strategy is required. It is important to check any collision among the robotic links and any of the environmental objects. In this work, emphasis is given on the thorough examination of any configuration in question; that is, the collision is not checked just for end-effector and/or for a few points on the robotic links. For this purpose, the solid model of the workspace is required in Stereolithographic (stl) format which is modelled in Solidworks Premium 2013 version. The stl file provides the connectivity information of modelled environment in triangulated mesh format. The forward kinematic procedure is used to model the robotic arm at every iteration. Each link is assumed to be a rectangular parallelepiped with square cross section of a prescribed width. For collision detection, the proximity query package is utilized. The obstacle avoidance approach computes the minimum distance between the two solid models, represented in their triangulated form. In case there is collision between the two objects, the package furnishes the data about the colliding pairs. The collided pair may belong to the robot links or robot obstacles. The strategy computes a positive minimum distance " [figure omitted; refer to PDF] " between the models which is utilized in formation of corresponding constraints.

3. Task-Oriented Problem Formulation

A nested bilevel optimization problem is formulated for minimizing the number of dof at upper level (outer loop) and designing a robotic arm for the fixed number of dof in each inner loop.

3.1. Reachability at Working Locations: Objective Function

For each current value of " [figure omitted; refer to PDF] " in the outer loop, robotic parameters are synthesized for reachability at the required TSLs. Reachability is measured as a squared error function of Euclidean distances, that is, distance between the end-effector position of the manipulator and the specified TSL as [figure omitted; refer to PDF]

A schematic diagram of the end-effector position of a redundant manipulator while reaching at the desired task locations is shown in Figure 4. For an [figure omitted; refer to PDF] -link manipulator, working for [figure omitted; refer to PDF] TSLs-with the actual position of the end-effector corresponding to the [figure omitted; refer to PDF] th TSL as vector [figure omitted; refer to PDF] and the desired TSL position as vector [figure omitted; refer to PDF] -the error square sum for [figure omitted; refer to PDF] number of TSLs is computed as [figure omitted; refer to PDF]

Figure 4: Schematic diagram of a serial redundant manipulator.

[figure omitted; refer to PDF]

In Cartesian coordinates, (3) can be expressed as [figure omitted; refer to PDF]

Finally, the cumulative error for all the [figure omitted; refer to PDF] TSLs can be written as [figure omitted; refer to PDF]

To include the orientation of a frame attached to the end-effector of the manipulator relative to the base frame, Euler angle conventions have been used. With [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] representing the Euler angles corresponding to a particular configuration of the manipulator under consideration, the objective function can be revised as [figure omitted; refer to PDF] Corresponding actual orientation parameters ( [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] ) can be derived from the concatenative rotation matrix of the end-effector with respect to the base frame.

The actual coordinates of the manipulator end-effector are given by the forward kinematics using D-H parameters ( [figure omitted; refer to PDF] ). The transformation matrices are multiplied together to form an arm matrix. The first three elements of the global transformation matrix give the current location of the end-effector in Cartesian form, that is, [figure omitted; refer to PDF] . It is worth mentioning here that the base point coordinates can also be considered as design variables, in case the application allows a flexibility in the installation point of the robot: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the coordinates of the base point.

3.2. Unconventional Robotic Parameters: Design Variables

The global transformation matrix is calculated to determine the actual position in Cartesian coordinates. The arm matrix is the function of the number of dof and D-H parameters and all are considered as design variables for the formulated problem. Out of all the D-H parameters, link parameters are fixed for each TSL, while the joint variables are varying to provide [figure omitted; refer to PDF] th configuration. The change in joint variables is required to reach at different TSLs for each particular robotic posture. With the number of TSLs as " [figure omitted; refer to PDF] ", total " [figure omitted; refer to PDF] " joint variables are required. The joint variables are expressed as [figure omitted; refer to PDF]

The total number of design variables for [figure omitted; refer to PDF] -link manipulator reaching " [figure omitted; refer to PDF] " TSLs is, therefore, [figure omitted; refer to PDF] . Since the number of dof " [figure omitted; refer to PDF] " is also a design variable, it leads to an inherent challenge of handling varying number of design variables in each iteration. Therefore, a bilevel problem is formulated to fix the number of dof at the upper level. The dimensional synthesis variable vector is expressed as [figure omitted; refer to PDF] and it is further a function of " [figure omitted; refer to PDF] ".

3.3. Constraints Handling

The minimization of the objective function is subject to the constraints due to the limits on the design variables and due to the environmental obstacles.

3.3.1. Limiting Values of D-H Parameters: Inequality Constraints

All the D-H parameters and degrees of freedom are design variables in the above formulated problem. The limiting bound/range on all the design variables are imposed as constraints for the optimization method. The limits on D-H parameters for the [figure omitted; refer to PDF] th link can be written as [figure omitted; refer to PDF] where [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] in suffix represents the lower bound, while [figure omitted; refer to PDF] represents the upper. Each inequality in (10) gives rise to a pair of constraints in the form [figure omitted; refer to PDF] The bounds on the number of degrees of freedom are also discussed later.

3.3.2. Cluttered Workstations: Inequality Constraints

To compute the minimum distance between the robot and the workspace or among the robot links, the workspace and each link of the manipulator are presented in a triangulated mesh format, as discussed in Section 2.2. A function [figure omitted; refer to PDF] is defined as the minimum distance between two nearest triangle pairs. The minimum distance of separation (positive in nature), reported by the package, is returned as [figure omitted; refer to PDF] . The function [figure omitted; refer to PDF] gets a negative value representing the overall intersection, when there is a collision between objects (robot-obstacle or link-link).

The inequality constraint including both cases is represented as [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the number of TSLs. Thus, the [figure omitted; refer to PDF] th constraint gets active only when there is an intersection of the manipulator with any obstacle and/or among the links, at the posture corresponding to the [figure omitted; refer to PDF] th TSL.

All these constraints, along with the objective function, constitute the problem in [figure omitted; refer to PDF] . As discussed earlier, this constrained optimization problem is solved by using augmented Lagrangian method.

4. Binary Search Method: Outer Loop

The binary search method is applied at the outer layer of the formulated bilevel optimization problem. The technique is used to locate the target value in a sorted array [10]. In this work, targeted value is the minimum number of dof at which the manipulator can be designed for reachability at desired TSLs. The range of dof is the array under consideration from which the middle element will be selected and every element of the array is an integer.

It is used to find out the optimal number of degrees of freedom [figure omitted; refer to PDF] . Total number of design variables are dependent on dof as discussed in Section 3.2, that is, [ [figure omitted; refer to PDF] ], where [figure omitted; refer to PDF] is the number of TSLs. " [figure omitted; refer to PDF] " is an integer value and possesses a finite range of 3-12 in this work.

To initialize the method, the array is split in half and the middle element is selected as an input to the inner loop; that is, [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are the upper and the lower limit of the number of dof and [figure omitted; refer to PDF] represents the iteration number. In the inner loop, a nonlinear optimization problem has been formulated and augmented Lagrangian method is used to solve the highly constrained problem. [figure omitted; refer to PDF] is the value at which design vector [figure omitted; refer to PDF] is to be determined in inner loop optimization.

In case the solution does not exist at [figure omitted; refer to PDF] , it becomes the new [figure omitted; refer to PDF] as [figure omitted; refer to PDF]

However, if there exists a design solution at [figure omitted; refer to PDF] , then it becomes new [figure omitted; refer to PDF] and [figure omitted; refer to PDF] The iterative process terminates with [figure omitted; refer to PDF] .

5. Augmented Lagrangian Method: Inner Loop

This constrained optimization method uses the combination of duality and penalty aspects. A penalty function is induced in the objective function to check the constraint violation. The Hessian of Lagrangian can be ill-conditioned in some cases due to which it affects the rate of convergence. The dual method can be applied only on convex functions. So, in augmented Lagrangian method, a moderate penalty is applied to augment the objective function into its convex function [11].

Suppose [figure omitted; refer to PDF] is the objective function to be minimized subject to the inequality constraints [figure omitted; refer to PDF] and equality constraints [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are the number of inequality and equality constraints, respectively. The augmented Lagrangian function is then expressed as [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are the Lagrange multipliers corresponding to [figure omitted; refer to PDF] th inequality and [figure omitted; refer to PDF] th equality constraints, respectively, whereas [figure omitted; refer to PDF] is the penalty parameter.

6. Methodology

For a given workcell (in triangulated mesh format) with the tasks defined as the working locations (in Euclidean space), the problem formulated in the previous sections facilitates the solution of the problem with minimum number of dof. The complete methodology is summarized through the following steps.

Upper Level Start

(1) Define [figure omitted; refer to PDF] and [figure omitted; refer to PDF] for binary search algorithm.

(2) Compute the [figure omitted; refer to PDF] value and update it to lower level.

Lower Level Start

(1) Formulate the objective function [figure omitted; refer to PDF] referring to (18).

(2) Specify the constraints due to parameter bounds and obstacles as mentioned in Section 3.3.

(3) Apply the augmented Lagrangian method to solve the formulated NLP problem. This involves updating the Lagrange multipliers as discussed in previous section.

(4) Update the solution status to upper level.

Lower Level Ends

(1) Based on the solution status at lower level, update [figure omitted; refer to PDF] according to (14) and (15).

(2) Check termination criteria. If the condition mentioned after (15) is fulfilled, the corresponding inner loop solution [figure omitted; refer to PDF] is the required feasible solution with minimum number of dof.

Upper Level Ends . It is possible that, on termination, the objective function is not zero, which signifies that no solution is possible within the prescribed range of dof. The range of dof can also be expanded to increase the redundancy. The bounds on the link lengths can be relaxed, if allowed. The range of degrees of freedom is [figure omitted; refer to PDF] to [figure omitted; refer to PDF] in this work. Only revolute joint has been taken in the design process which means joint offset will be fixed parameter in all the cases.

7. Results and Discussion

The problem, discussed and formulated in the previous sections, is implemented in C++. For the execution of the code, the information of workspace environment in the triangulated mesh format, the task space locations in the environment, base position of the manipulator, and the limits on the D-H parameters (design variables) are required as the input data.

Using the proposed strategy, manipulator design for several environments has been synthesized and some of the results are presented in this section.

7.1. Example 1: Strategy Validation

Case 1.

An exemplary workspace is taken into consideration for the validation of the strategy. It has 10 blocks scattered in the space, as shown in Figure 5(a). The required manipulator needs to work at [figure omitted; refer to PDF] TSLs [ [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] ], while avoiding all the blocks. The base of the manipulator is fixed at ( [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] ). The design of an [figure omitted; refer to PDF] -link manipulator has been reported in the work of Singla et al. [5] for the same environment and TSLs. The number of dof was reported fixed in the work.

By using the proposed strategy given in this chapter, the [figure omitted; refer to PDF] -link manipulator can reach all the given TSLs. In the first iteration, the degrees of freedom have been taken as [figure omitted; refer to PDF] at the outer loop and initialize the augmented Lagrangian method in the inner loop for reachability. The [figure omitted; refer to PDF] -link design outcome is expected because, with same input, it has been reported earlier.

The [figure omitted; refer to PDF] -update is done according to the algorithm discussed in Section 4. The number of dof is updated to [figure omitted; refer to PDF] at the outer loop and this updated value is checked for the success in the inner loop. Table 1 presents the robotic parameters and Figure 5(b) shows the corresponding skeletal view of the manipulator in the workspace.

Binary search will update the new value of dof that is [figure omitted; refer to PDF] in this case. At number of dof [figure omitted; refer to PDF] , the method fails to converge. A positive error value that is [figure omitted; refer to PDF] is left, which signifies that no solution exists at dof [figure omitted; refer to PDF] . The method will terminate here with a [figure omitted; refer to PDF] -link design as an output. The case showcases the importance of varying number of links.

Table 1: Six-link manipulator design.

S. number	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
1	0	0	0.4	0.74	0.32	0.18	0.55
2	0.97	2.20	-0.4	0.43	0.48	0.69	-0.87
3	0.64	1.99	-0.37	0.03	0.9	0.94	-0.38
4	0.55	1.99	-0.19	-0.14	1.44	0.77	0.54
5	0.77	1.49	-0.24	-0.14	1.26	0.5	0.92
6	0.99	1.49	-0.41	0.07	0.6	0.38	0.70

Figure 5: An exemplary case study for strategy validation.

(a) A 10-obstacle environment with 4 TSLs

[figure omitted; refer to PDF]

(b) A skeletal view of the 6-link manipulator in the environment.

[figure omitted; refer to PDF]

Case 2.

It is quite possible to achieve the required set of design variables with [figure omitted; refer to PDF] dof, if variable bounds are relaxed. For [figure omitted; refer to PDF] -dof design, the link length limits in the failed case are [figure omitted; refer to PDF] which are changed to [figure omitted; refer to PDF] . With the changed limits, the reachability is achieved as shown in Figure 6(b). With the initial limits, robot arm was nearly outstretched to its full length and yet not able to reach one of the TSLs, as shown in Figure 6(a). Therefore, the variations in the link lengths are tried and successfully delivered the result.

Figure 6: A 5-link design by varying the limits of the design variables.

(a) A 5-link design failed to reach

[figure omitted; refer to PDF]

(b) A 5-link manipulator design

[figure omitted; refer to PDF]

7.2. Example 2: Comparison with Conventional Values of D-H Parameters

This example is included to compare the design results having conventional values of twist angles with the proposed flexibility in twist angles. The environment and the working locations are kept the same as used in the previous case study. In this example, the outer loop is not active and the number of dof is fixed manually at different levels. The problem starts with a [figure omitted; refer to PDF] -link configuration in which the link lengths and the joint offsets can vary and all the twist angles are taken as either perpendicular or parallel. The values of all the twist angles are taken the same as for PUMA configuration. This example is an attempt to compare whether a manipulator with conventional twist angles that is [figure omitted; refer to PDF] or [figure omitted; refer to PDF] achieves the objective with same number of dof. If not, then how many dof are required to reach? Hence, the problem started with minimum number of dof which has been calculated in the previous example. It is observed that, with least variation in twist angles, no solution is obtained with number of dof [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] . Twist angle can vary along with other variables from dof [figure omitted; refer to PDF] onwards because there is no set conventional value of twist angle in redundant manipulators. In this example, with first [figure omitted; refer to PDF] -dof puma configuration, a [figure omitted; refer to PDF] -link manipulator is the design outcome. Corresponding D-H parameters are shown in Table 2 and the configurations are presented in Figure 7. There is an important aspect that with increase in solution space degrees of freedom can be decreased for reaching desired locations.

Table 2: Nine-link manipulator design with conventional twist angles.

S. number	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
1	0	0	-0.51	0.41	1.39	3.00	-2.32
2	-1.48	3.06	-0.75	0.77	-2.89	2.01	-1.01
3	-0.01	0.61	-0.23	0.69	2.80	1.74	0.00
4	-1.49	1.99	0.99	0.39	1.34	-0.61	-0.50
5	-1.51	1.79	0.86	0.34	-2.17	-2.66	2.85
6	1.55	1.95	-0.51	0.39	-1.15	-2.99	-1.10
7	-0.01	1.91	-0.28	0.44	1.15	-2.50	0.91
8	0.09	1.81	0.32	0.49	-0.76	-2.98	-0.72
9	0	1.89	0.27	0.50	0.011	-1.13	-0.01

Figure 7: A 9-link manipulator design with the first 6 links in PUMA configuration.

[figure omitted; refer to PDF]

7.3. Example 3: Realistic Environment of a Server Room

In this case study, a server room environment has been taken into consideration, as shown in Figure 8(a). The manipulator has to reach [figure omitted; refer to PDF] TSLs, [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] , as shown in Figure 8(b) and the base point of the manipulator is taken at [figure omitted; refer to PDF] . Minimum [figure omitted; refer to PDF] -link manipulator is required to reach all these TSLs. The corresponding design is presented in Table 3 and Figure 8(c) presents the visualization for desired working postures.

Table 3: Six-link manipulator design for server room environment.

S. number	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
1	0	0	0.09	0.13	1.9	-2.8
2	2.5	20.03	0.09	2.8	1.25	-2.09
3	0.72	20.3	0.08	2.7	0.47	-2.7
4	2.05	20.2	0.06	-0.44	-0.71	0.38
5	2.06	20.3	0.06	-0.75	-0.38	0.032
6	1.75	20.3	0.071	-0.30	0.58	0.08

Figure 8: A server room case study.

(a) Server room model

[figure omitted; refer to PDF]

(b) Work environment and the TSLs

[figure omitted; refer to PDF]

(c) Skeletal view of the 6-link manipulator in the server room

[figure omitted; refer to PDF]

In the first case study, it has been shown that, with less number of dof and varying the limits of variable, it is possible to compute the solution set of parameters. Now, in the present scenario, number of TSLs increased from [figure omitted; refer to PDF] to [figure omitted; refer to PDF] and placed in such a way that [figure omitted; refer to PDF] -link design failed to reach which means strategy will automatically select the higher number of dof. A [figure omitted; refer to PDF] -link manipulator design is selected to reach [figure omitted; refer to PDF] TSLs [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] with the same base point as shown in Figure 9(a) showing the violation of constraints in an intermediate iteration. The method will give the solution when there is no collision and all the parameters are converged according to the termination criteria. Figure 9(b) shows the model which is an outcome of a fully converged problem. The corresponding D-H parameters are shown in Table 4.

Table 4: Seven-link manipulator design for server room environment.

S. number	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
1	0	0	-2.72	0.81	-0.26	-0.35	1.89
2	2.49	15.99	0.00	-0.58	0.08	-2.9	-0.89
3	1.05	14.99	0.58	0.50	-0.36	0.53	0.56
4	-0.28	13.99	0.19	-0.07	-0.42	-0.06	2.80
5	1.19	12.97	0.26	-0.82	0.09	0.01	1.79
6	1.33	11.97	-0.04	0.45	0.04	-0.01	-0.43
7	0.73	10.98	0.11	0.06	-0.66	-0.00	-0.25

Figure 9: Analysis: the server room case study.

(a) Seven-link manipulator in the server room, intermediate iteration number 5

[figure omitted; refer to PDF]

(b) Seven-link design in the server room: final solution

[figure omitted; refer to PDF]

(c) Number of violated constraints and the objective function evaluation

[figure omitted; refer to PDF]

7.4. Example 4: Synthesis for Both Position and Orientation

This example presents a manipulator synthesis problem for both position and orientation of the end-effector, that is, for the entire arm-wrist combination. The workspace consists of an enclosed environment including a table and a big box, representing a cupboard, as shown in Figure 10(a). The figure includes two TSLs along with the desired approaching directions of the end-effector. The prescribed positions for these locations are [figure omitted; refer to PDF] and [figure omitted; refer to PDF] with ( [figure omitted; refer to PDF] ) as the three orientation angles, the same for both locations. A 7-link manipulator is synthesized for the situation, with fixed base point locations as [figure omitted; refer to PDF] . Table 5 contains the synthesis solution and pictorial view of the corresponding configurations is presented in Figure 10(b).

Table 5: Synthesis result: tasks included both specified position and orientation.

S. number	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
1	17.4	0.70	-2.1	0.17	0.93
2	17.2	0.11	-1.4	0.31	1.41
3	15.8	1.16	-0.08	0.50	0.53
4	15.8	1.40	1.8	0.10	-0.55
5	16.1	1.01	1.07	-0.43	0.36
6	16.6	0.95	2.6	-0.25	0.90
7	18.7	1.00	3.1	0.76	-0.08

Figure 10: Seven-link manipulator in a room environment with both position and orientation prescribed.

(a) The workspace

[figure omitted; refer to PDF]

(b) The resulting configurations

[figure omitted; refer to PDF]

8. Conclusion

In this work, the variation in the working environments for a service robotic arm is dealt with through a general methodology for a constrained workcell. For this purpose, least constraints are applied at the number of dof and over the values of the robotic parameters. A bilevel optimization problem is formulated, involving the minimization of the number of degrees of freedom required to work in the given environment and for the given tasks (positions and orientations). Binary search algorithm is implemented for the outer layer of the optimization problem, which solves the unidirection problem in number of dof. In each of the outer iterations, a nonlinear optimization problem is solved for dimensional synthesis. The proposed methodology is validated through the reduction of the number of dof required for an environment, reported in earlier works. The importance of flexibility in robotic parameters is illustrated through two different cases in a realistic workcell of a computer server room where a robotic assistance is required for maintenance.

Acknowledgments

Authors gratefully acknowledge the financial support of Department of Science and Technology (DST), Government of India, for this work.

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