# Optimal Collision-Free Grip Planning for Biped Climbing Robots in Complex Truss Environment
## 5 Gait Interpreter
If collision avoidance is not taken into account, we do not need to compute the operational regions for negotiating obstacles prior to grip planning. Only the operational regions for transitions are considered in Equation (12). The optimization objectives are to achieve the minimum number of

Figure 14: Comparison of results of grip planning with and without consideration of collision avoidance in a scene of 25 members. climbing steps, and good manipulability of transition grips as well. Figure 13a shows us the result planned in this way. Eight climbing steps are required from the starting point to the destination. In the figure, we can see that the robot collides with a member when it moves downwards on the first member. Therefore, in this case, the robot actually cannot execute the climbing movement. If collision avoidance is considered, the planner can generate a collision-free solution, where twelve climbing steps are required. As shown in Figure 13b, four extra adjustment steps are added to the route, to change the grip locations. The robot uses two inchworm-like gaits to adjust the gripping positions in climbing. One time is to get ready to surmount the obstacle member with the creeping mode, while another is to prepare for transition since the operational region is short. The planning procedure considering collision avoidance costs 0.78 s.

Figure 14 shows the comparison of results of grip planning, with and without consideration of collision avoidance, in a complex truss environment with 25 members. According to the outputs of the global path planner, there are three feasible routes in total. On the left column are the results obtained without collision avoidance. As a result, the collision between the robot and the truss members can be found in these sub-figures. The collision can be seen more clearly from the zoom-in views. On the right column are the results with collision avoidance. Collision-free grip sequences are successfully solved for each feasible route. Table 2 summaries the comparison of the results. From the table, we know that obstacle members are detected for each route. The time consumption increases four times if collision avoidance is considered. However, the entire planning procedure is still very quick, which costs approximately 0.65 s only. Moreover, the number of grips increases slightly, as the cost of avoiding collision. ## 7 Conclusions and Future Work

Biped climbing robots have bright and broad application prospects in the field of performing high-rise truss-related routine tasks. To perform such a task, BiCRs must have the ability to plan their grips prior to climbing. In this paper, we presented a novel optimal collision-free grip planner for BiCRs generating grip sequence in complex truss environments. The planner essentially consists of three components: (1) a mathematical model for computing the operational regions for surmounting obstacles; (2) a grip placement optimizer for determining the grips within operational regions; and (3) a gait interpreter for generating grips between adjacent operational regions. The idea behind this novel scheme is to use the priority to determine the grips at key places, i.e., operational regions for surmounting obstacles and that for performing transitions, then move to other places. Because normally there is less room to choose and adjust grips for these key places. Simulation results verified that the planner was able to plan approximately 30 grips within 0.65 s, taking collision avoidance, grip manipulability and number of climbing steps into account. However, the current planner only considers the forward and backward movement on a member, which limits the applicable objects as BiCRs with, or less than, five degrees of freedom. In addition, the total time consumed in grip planning is related to the initial values for optimization.