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The COG trajectory planning method is the primary concern in the gait planning for quadruped robot, especially when the quadruped robot travelling on rough terrain. In this paper, we focus on the scenario where the quadruped robot walking on the rough terrain with the static walking gait. We present a smooth COG trajectory generator that the COG sm...
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... schematic diagram for the quadruped robot is shown in Figure 1. The quadruped robot has four legs, each leg with 3 degrees of freedom, a rolling rotary joint and two pitching rotary joints, as shown in Figure 1. ...
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... schematic diagram for the quadruped robot is shown in Figure 1. The quadruped robot has four legs, each leg with 3 degrees of freedom, a rolling rotary joint and two pitching rotary joints, as shown in Figure 1. ...
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... are six different foot placing sequences of nonsingular quadrupedal static gaits proved by McGhee and Frank [1]. But there is only one foot placing sequence can be used to ensure the stability of robot with the COG move forward at all times, the sequence is 4-2-3-1 [12], and the leg no. is shown in Figure 1. Therefore, this foot placing sequence is taken as the basic gait for the robot motion planning in this paper. ...
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... our simulation, we tested our quadruped robot on different degrees of slope, steps with varying step heights, and rough terrain with barriers. Figure 10 demonstrates snapshots of the robot walking over a rough terrain contained steps and obstacles of different sizes. Figure 11 to Figure 13 show the simulation results. ...
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... 10 demonstrates snapshots of the robot walking over a rough terrain contained steps and obstacles of different sizes. Figure 11 to Figure 13 show the simulation results. These data was obtained with a 10 second period for one full walking cycle, 0.55m ground clearance during walking. ...
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... 10 demonstrates snapshots of the robot walking over a rough terrain contained steps and obstacles of different sizes. Figure 11 to Figure 13 show the simulation results. These data was obtained with a 10 second period for one full walking cycle, 0.55m ground clearance during walking. ...
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... data was obtained with a 10 second period for one full walking cycle, 0.55m ground clearance during walking. a) and Figure 12(a) illustrate the position variations of the robot's torso along x and y axes during the walking processes, Figure 11(b) and Figure 12(b) show its velocity variations, while Figure 11(c) and Figure 12(c) show the acceleration variations of the robot's torso along x-axis and y-axis respectively. Figure 13 shows the COG trajectory with respect to world frame {W}. ...
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... data was obtained with a 10 second period for one full walking cycle, 0.55m ground clearance during walking. a) and Figure 12(a) illustrate the position variations of the robot's torso along x and y axes during the walking processes, Figure 11(b) and Figure 12(b) show its velocity variations, while Figure 11(c) and Figure 12(c) show the acceleration variations of the robot's torso along x-axis and y-axis respectively. Figure 13 shows the COG trajectory with respect to world frame {W}. ...
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... data was obtained with a 10 second period for one full walking cycle, 0.55m ground clearance during walking. a) and Figure 12(a) illustrate the position variations of the robot's torso along x and y axes during the walking processes, Figure 11(b) and Figure 12(b) show its velocity variations, while Figure 11(c) and Figure 12(c) show the acceleration variations of the robot's torso along x-axis and y-axis respectively. Figure 13 shows the COG trajectory with respect to world frame {W}. ...
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... data was obtained with a 10 second period for one full walking cycle, 0.55m ground clearance during walking. a) and Figure 12(a) illustrate the position variations of the robot's torso along x and y axes during the walking processes, Figure 11(b) and Figure 12(b) show its velocity variations, while Figure 11(c) and Figure 12(c) show the acceleration variations of the robot's torso along x-axis and y-axis respectively. Figure 13 shows the COG trajectory with respect to world frame {W}. ...
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... data was obtained with a 10 second period for one full walking cycle, 0.55m ground clearance during walking. a) and Figure 12(a) illustrate the position variations of the robot's torso along x and y axes during the walking processes, Figure 11(b) and Figure 12(b) show its velocity variations, while Figure 11(c) and Figure 12(c) show the acceleration variations of the robot's torso along x-axis and y-axis respectively. Figure 13 shows the COG trajectory with respect to world frame {W}. ...
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... and Figure 12(a) illustrate the position variations of the robot's torso along x and y axes during the walking processes, Figure 11(b) and Figure 12(b) show its velocity variations, while Figure 11(c) and Figure 12(c) show the acceleration variations of the robot's torso along x-axis and y-axis respectively. Figure 13 shows the COG trajectory with respect to world frame {W}. As shown in Figure 11, the period of the time from t 1 to t 2 and the period of the time from t 3 to t 4 are the LSSs of the quadruped robot. ...
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... 13 shows the COG trajectory with respect to world frame {W}. As shown in Figure 11, the period of the time from t 1 to t 2 and the period of the time from t 3 to t 4 are the LSSs of the quadruped robot. In the LSS, the movement trajectory of the robot is straight line both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a), the velocities of the robot are constant both along the x-axis and y-axis as shown in Figure 11 (b) and Figure 12 (b), and the values of the accelerations of the torso are all zero both along the x-axis and y-axis in these stages as shown in Figure 11 (c) and Figure 12 (c). ...
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... shown in Figure 11, the period of the time from t 1 to t 2 and the period of the time from t 3 to t 4 are the LSSs of the quadruped robot. In the LSS, the movement trajectory of the robot is straight line both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a), the velocities of the robot are constant both along the x-axis and y-axis as shown in Figure 11 (b) and Figure 12 (b), and the values of the accelerations of the torso are all zero both along the x-axis and y-axis in these stages as shown in Figure 11 (c) and Figure 12 (c). The COG trajectory respect to world frame {W} is a straight line in the LSS as shown in the Figure 13. ...
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... shown in Figure 11, the period of the time from t 1 to t 2 and the period of the time from t 3 to t 4 are the LSSs of the quadruped robot. In the LSS, the movement trajectory of the robot is straight line both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a), the velocities of the robot are constant both along the x-axis and y-axis as shown in Figure 11 (b) and Figure 12 (b), and the values of the accelerations of the torso are all zero both along the x-axis and y-axis in these stages as shown in Figure 11 (c) and Figure 12 (c). The COG trajectory respect to world frame {W} is a straight line in the LSS as shown in the Figure 13. ...
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... shown in Figure 11, the period of the time from t 1 to t 2 and the period of the time from t 3 to t 4 are the LSSs of the quadruped robot. In the LSS, the movement trajectory of the robot is straight line both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a), the velocities of the robot are constant both along the x-axis and y-axis as shown in Figure 11 (b) and Figure 12 (b), and the values of the accelerations of the torso are all zero both along the x-axis and y-axis in these stages as shown in Figure 11 (c) and Figure 12 (c). The COG trajectory respect to world frame {W} is a straight line in the LSS as shown in the Figure 13. ...
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... shown in Figure 11, the period of the time from t 1 to t 2 and the period of the time from t 3 to t 4 are the LSSs of the quadruped robot. In the LSS, the movement trajectory of the robot is straight line both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a), the velocities of the robot are constant both along the x-axis and y-axis as shown in Figure 11 (b) and Figure 12 (b), and the values of the accelerations of the torso are all zero both along the x-axis and y-axis in these stages as shown in Figure 11 (c) and Figure 12 (c). The COG trajectory respect to world frame {W} is a straight line in the LSS as shown in the Figure 13. ...
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... shown in Figure 11, the period of the time from t 1 to t 2 and the period of the time from t 3 to t 4 are the LSSs of the quadruped robot. In the LSS, the movement trajectory of the robot is straight line both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a), the velocities of the robot are constant both along the x-axis and y-axis as shown in Figure 11 (b) and Figure 12 (b), and the values of the accelerations of the torso are all zero both along the x-axis and y-axis in these stages as shown in Figure 11 (c) and Figure 12 (c). The COG trajectory respect to world frame {W} is a straight line in the LSS as shown in the Figure 13. ...
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... shown in Figure 11, the period of the time from t 1 to t 2 and the period of the time from t 3 to t 4 are the LSSs of the quadruped robot. In the LSS, the movement trajectory of the robot is straight line both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a), the velocities of the robot are constant both along the x-axis and y-axis as shown in Figure 11 (b) and Figure 12 (b), and the values of the accelerations of the torso are all zero both along the x-axis and y-axis in these stages as shown in Figure 11 (c) and Figure 12 (c). The COG trajectory respect to world frame {W} is a straight line in the LSS as shown in the Figure 13. ...
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... the LSS, the movement trajectory of the robot is straight line both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a), the velocities of the robot are constant both along the x-axis and y-axis as shown in Figure 11 (b) and Figure 12 (b), and the values of the accelerations of the torso are all zero both along the x-axis and y-axis in these stages as shown in Figure 11 (c) and Figure 12 (c). The COG trajectory respect to world frame {W} is a straight line in the LSS as shown in the Figure 13. Base on the analysis in this paragraph, the movement of the robot in this stage is a uniform motion. ...
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... the period of the time from t 2 to t 3 and the period of the time from t 4 to t 5 are the CASs of the robot. In the CAS, the movement trajectory of the robot are transition curves both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a). As shown in Figure 11 (b) and Figure 12 (b), through the adjustments in the CAS, the robot realize the smooth transition between the different velocities of the robot in two adjacent legs swing stage both in the x-axis direction and y-axis direction. ...
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... the period of the time from t 2 to t 3 and the period of the time from t 4 to t 5 are the CASs of the robot. In the CAS, the movement trajectory of the robot are transition curves both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a). As shown in Figure 11 (b) and Figure 12 (b), through the adjustments in the CAS, the robot realize the smooth transition between the different velocities of the robot in two adjacent legs swing stage both in the x-axis direction and y-axis direction. ...
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... the CAS, the movement trajectory of the robot are transition curves both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a). As shown in Figure 11 (b) and Figure 12 (b), through the adjustments in the CAS, the robot realize the smooth transition between the different velocities of the robot in two adjacent legs swing stage both in the x-axis direction and y-axis direction. As shown in Figure 11 (c) and Figure 12 (c), the values of the acceleration of the torso both in the x-axis direction and y-axis direction at the beginning and the end of this stage are zero to guarantee the acceleration are smooth. ...
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... the CAS, the movement trajectory of the robot are transition curves both in the x-axis and y-axis direction as shown in Figure 11 (a) and Figure 12 (a). As shown in Figure 11 (b) and Figure 12 (b), through the adjustments in the CAS, the robot realize the smooth transition between the different velocities of the robot in two adjacent legs swing stage both in the x-axis direction and y-axis direction. As shown in Figure 11 (c) and Figure 12 (c), the values of the acceleration of the torso both in the x-axis direction and y-axis direction at the beginning and the end of this stage are zero to guarantee the acceleration are smooth. ...
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... shown in Figure 11 (b) and Figure 12 (b), through the adjustments in the CAS, the robot realize the smooth transition between the different velocities of the robot in two adjacent legs swing stage both in the x-axis direction and y-axis direction. As shown in Figure 11 (c) and Figure 12 (c), the values of the acceleration of the torso both in the x-axis direction and y-axis direction at the beginning and the end of this stage are zero to guarantee the acceleration are smooth. The COG trajectory of the robot in this stage is a transition curve as shown in Figure 13. ...
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... shown in Figure 11 (b) and Figure 12 (b), through the adjustments in the CAS, the robot realize the smooth transition between the different velocities of the robot in two adjacent legs swing stage both in the x-axis direction and y-axis direction. As shown in Figure 11 (c) and Figure 12 (c), the values of the acceleration of the torso both in the x-axis direction and y-axis direction at the beginning and the end of this stage are zero to guarantee the acceleration are smooth. The COG trajectory of the robot in this stage is a transition curve as shown in Figure 13. ...
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... shown in Figure 11 (c) and Figure 12 (c), the values of the acceleration of the torso both in the x-axis direction and y-axis direction at the beginning and the end of this stage are zero to guarantee the acceleration are smooth. The COG trajectory of the robot in this stage is a transition curve as shown in Figure 13. Through the analysis in this paragraph, the robot realizes the smooth transition between both the velocities and the accelerations in two adjacent legs swing stages. ...
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... on the analysis of the experimental data as shown in Figure 11, Figure 12 and Figure 13, the COG of the quadruped robot move in a very smooth, i.e. twice differentially, way. So the robot can avoid any form of jerkiness which can lead to foot slipping and instability of the robot by using the COG trajectory planning method proposed in this paper. ...
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... on the analysis of the experimental data as shown in Figure 11, Figure 12 and Figure 13, the COG of the quadruped robot move in a very smooth, i.e. twice differentially, way. So the robot can avoid any form of jerkiness which can lead to foot slipping and instability of the robot by using the COG trajectory planning method proposed in this paper. ...
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... on the analysis of the experimental data as shown in Figure 11, Figure 12 and Figure 13, the COG of the quadruped robot move in a very smooth, i.e. twice differentially, way. So the robot can avoid any form of jerkiness which can lead to foot slipping and instability of the robot by using the COG trajectory planning method proposed in this paper. ...
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... the robot can avoid any form of jerkiness which can lead to foot slipping and instability of the robot by using the COG trajectory planning method proposed in this paper. Figure 14 shows the stability margin of the robot in the whole process of the robot walking through the rough terrain show in Figure 10. As shown in Figure 14, the stability margin of the robot is no less than the value of the S min (the value of S min when walking on the rough terrain show in Figure 10 is set to be 96mm). ...
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... the robot can avoid any form of jerkiness which can lead to foot slipping and instability of the robot by using the COG trajectory planning method proposed in this paper. Figure 14 shows the stability margin of the robot in the whole process of the robot walking through the rough terrain show in Figure 10. As shown in Figure 14, the stability margin of the robot is no less than the value of the S min (the value of S min when walking on the rough terrain show in Figure 10 is set to be 96mm). ...
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... 14 shows the stability margin of the robot in the whole process of the robot walking through the rough terrain show in Figure 10. As shown in Figure 14, the stability margin of the robot is no less than the value of the S min (the value of S min when walking on the rough terrain show in Figure 10 is set to be 96mm). Thus it can be seen, the stability of the robot has been effectively guaranteed. ...
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... 14 shows the stability margin of the robot in the whole process of the robot walking through the rough terrain show in Figure 10. As shown in Figure 14, the stability margin of the robot is no less than the value of the S min (the value of S min when walking on the rough terrain show in Figure 10 is set to be 96mm). Thus it can be seen, the stability of the robot has been effectively guaranteed. ...
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... it can be seen, the stability of the robot has been effectively guaranteed. Figure 14. The Stability Margin of the Robot during Walking with the S min is equal to 96mm Figure 15 shows the kinematic margin of leg 2 during walking on the rough terrain. ...
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... 14. The Stability Margin of the Robot during Walking with the S min is equal to 96mm Figure 15 shows the kinematic margin of leg 2 during walking on the rough terrain. As shown in Figure 15, the value of kinematics margin of the leg 2 is near to zero just before the leg 2 lift-off the ground(as the time t shown in Figure 15), that is, the robot move forward with the maximum allowed speed during walking. ...
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... Stability Margin of the Robot during Walking with the S min is equal to 96mm Figure 15 shows the kinematic margin of leg 2 during walking on the rough terrain. As shown in Figure 15, the value of kinematics margin of the leg 2 is near to zero just before the leg 2 lift-off the ground(as the time t shown in Figure 15), that is, the robot move forward with the maximum allowed speed during walking. The kinematic margins of other three legs are similar to the performance of leg 2. Therefore, the robot can walk through the rough terrain as quickly as possible. ...
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... Stability Margin of the Robot during Walking with the S min is equal to 96mm Figure 15 shows the kinematic margin of leg 2 during walking on the rough terrain. As shown in Figure 15, the value of kinematics margin of the leg 2 is near to zero just before the leg 2 lift-off the ground(as the time t shown in Figure 15), that is, the robot move forward with the maximum allowed speed during walking. The kinematic margins of other three legs are similar to the performance of leg 2. Therefore, the robot can walk through the rough terrain as quickly as possible. ...
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Citations
... This attribute enables legged robot to accomplish tasks in hard and hazardous environments such as minefields Fig. 1. In order to devise walking robot to achieve this facilities, a very efficient and effective controller should be implemented in the robot [1]. In the context of legged robot control, there is a vast amount of work addressing locomotion control. ...
... Another approach to controlling legged systems is to consider the body Centre of Gravity (CoG) as a control point [11], [1], [12]. Then the contribution of each leg in the motion generation of the robot body is calculated accordingly [11]. ...
... Researchers have made achievements on the problem. (Ysway and E-sway motions [29,30], a sinusoidal sway [31] and the trajectory planning method based on the quantic spline curve [32]). However, few attentions have been paid to methods of foot trajectory planning for quadruped parallel robots. ...
In many traditional soft-landing missions, researchers design the lander and the rover as two separate individuals, which has its limitations. At present, research on landers mainly focuses on the performance analysis of those who cannot move, and the motion of legged mobile lander has not yet been studied. In this paper, a novel Mobile Landing Mechanism (MLM) is proposed. Firstly, the monte-Carlo method is used to solve the workspace, and the motion feasibility of the mechanism is verified. Secondly, combining with the constraints of velocity, acceleration and secondary acceleration of each driving joint of the MLM, the trajectory of its joint space is planned by using cubic spline curve. And based on the weighted coefficient method, an optimal time-jerk pedestal trajectory planning model is established. Finally, by comparing the genetic algorithm (GA) with the adaptive genetic algorithm (AGA), an optimization algorithm is proposed to solve the joint trajectory optimization problem of the MLM, which can obtain better trajectory under constraints. Simulation shows that the motion performance of the mechanism is continuous and stable, which proves the rationality and effectiveness of the foot trajectory planning method.
... RHEX capable to travel more than one body length per second over terrain. S. Zhang et al proposed a composite CoG trajectory planning algorithm associated with a new mode of CoG trajectory generation which enhanced the efficiency and the stability a quadruped robot [4]. The CoG trajectory has been generated automatically according to the foothold pattern in real time. ...
Mode of locomotion of a robot can be chosen according to the condition of the terrain; these are wheeled, Legged and Crawler or Hybrid. Legged mobile robots are superior to conventional wheeled mobile robot (WMR) for rough and marshy terrain due to its terrain adaptability. They have also the ability to raise or lower bodies or tilt them by varying the length of its legs by bending knees. However, unlike WMR, legged robots are much complex and needs more comprehensive analysis for developing a realistic autonomous system. The goal of this research is to develop a path planning of powered and autonomous hexapod robotic kit which is capable of navigating different terrain within the mechanical motion limits. It has been observed that the kit is capable to take turn at sharp corner or when it needs to turn at a large angle at any instant.
... Similarly to animals, they maintain good stability in rough terrain and overcome terrain obstacles [1]. Autonomous robots require advanced control including vision system, LIDARs and other systems basing on visual experiences. ...
... Many other kinds of controllers based on optimization methods can be found in the studies of walking mode designing. [15][16][17][18][19] In addition to the controllers based on optimization methods, bionic controllers are also widely studied, which can be mainly divided into controllers based on central pattern generators (CPGs) and walking rules. Since Shik et al. 20 announced that rhythmic motions of some animals and insects were generated by CPGs, many studies of applying the CPG control principles to real legged robots have been carried out. ...
As macroscopic rough terrains are time varying and full of local topographic mutations, stable locomotions of legged robots moving through such terrains in a fixed gait form can be hardly obtained. This problem becomes more severe as the size and weight of the robot increase. An ideal pre-planned gait changing method can also be hardly designed due to the same limitations. Aiming to solve the problem, a new kind of free gait controller applied to a large-scale hexapod robot with heavy load is developed. The controller consists of two parts, a free gait planner and a gait regulator. Based on the observed macro terrain changes, the free gait planner adopts the macro terrain recognition method and the status searching method for selecting the best leg support status automatically. The gait regulator is adopted for the correction of the selected status to cope with local topographic mutations. Detailed simulation experiments are presented to demonstrate that, with the designed controller, the adopted hexapod robot can change moving gaits automatically in terms of the terrain conditions and obtain stable locomotions through rough terrains.
... Quadruped robot should walk with enough stability margin to travelling on rough terrains successfully. In many existed gait planning methods, such as the methods proposed in the references [31][32][33], the body trajectories were generated by using the static stability criterion. As for the static stability criterion, the projection of the COG is regard as the approximate location of the COP (Center of Pressure), but ZMP is actually equivalent to the COP. ...
... Then, a threshold value (U T ) is set to determine the areas suitable for putting the foot on or not. If the variance of elevations of the grid (i, j) meets Equation (32), the grid (i, j) is seen as the forbidden area. Finally, using this approach, the forbidden area of rough terrain (covered by the red color) shown in Figure 13 can be obtained. ...
Generating a robust gait is one of the most important factors to improve the adaptability of quadruped robots on rough terrains. This paper presents a new continuous free gait generation method for quadruped robots capable of walking on the rough terrain characterized by the uneven ground and forbidden areas. When walking with the proposed gait, the robot can effectively maintain its stability by using the Center of Gravity (COG) trajectory planning method. After analyzing the point cloud of rough terrain, the forbidden areas of the terrain can be obtained. Based on this analysis, an optimal foothold search strategy is presented to help quadruped robot to determine the optimum foothold for the swing foot automatically. In addition, the foot sequence determining method is proposed to improve the performance of robot. With the free gait proposed in this paper, quadruped robot can walk through the rough terrains automatically and successfully. The correctness and effectiveness of the proposed method is verified via simulations.
... A COG trajectory planning method for the SCalf robot to travel on rough terrain is proposed by Shuaishuai et al. 29 Stable motion of the robot in static gait is achieved by the planning of COG trajectory, but the robot cannot achieve good dynamic performance. ...
A motion control approach is proposed for hydraulic actuated quadruped robots, aiming to achieve active compliance and robust motion control. The approach is designed with a structure of three layers. Servo valve-controlled asymmetric hydraulic cylinder model is established to obtain the relationship between the desired torque and the control current signal, which is the bottom layer. The middle layer is based on the virtual model of the leg for active compliance. The upper layer considers the torso posture and velocity into planning the foot trajectories based on the spring loaded inverted pendulum model. Trotting gait simulations are conducted based on the proposed framework in the simulation software environment Webots. The motion control approach has been implemented on a robot prototype SCalf-II (SDU calf), where experiments have been conducted including omnidirectional trotting gait, lateral impact recovery and climbing slopes. The experiments demonstrate that the proposed approach can effectively control the hydraulic actuated robots.
... Also, we consider an efficient Center of Gravity (COG) trajectory planning for stable walking. Of course, the COG based stability for a quadruped robot has been widely studied in other literatures; for example, Y-sway and E-sway motions [11] , a sinusoidal sway [12] , and a composite COG trajectory composed of quintic curves and straight lines [13] . However, a COG trajectory planning for a quadruped robot in particular one with heavy legs has received little attention. ...
... The robot is expected to perform various modes of locomotion in future work by utilizing its redundant DOFs. By comparing some related works [5][6][7]12,13] , this paper has addressed that how a quadruped robot with redundant DOFs can efficiently shift its COG and walk in statically stable manner when the total weight of the legs is much heavier than the body; in this work, all the four legs takes up 80% of the robot's total weight. ...
This paper presents a new Center of Gravity (COG) trajectory planning algorithm for a quadruped robot with redundant Degrees of Freedom (DOFs). Each leg has 7 DOFs, which allow the robot to exploit its kinematic redundancy for various locomotion and manipulation tasks. Also, the robot can suitably adapt to different environment (e.g., passing through a narrow gap) by simply changing the body posture. However, the robot has significant COG movement during the leg swinging phase due to the heavy leg weights; the weight of all the four legs takes up 80% of the robot’s total weight. To achieve stable walking in the presence of undesired COG movements, a new COG trajectory planning algorithm was proposed by using a combined Jacobian of COG and centroid of a support polygon including a foot contact constraint. Additionally, the inverse kinematics of each leg was solved by modified improved Jacobian pseudoinverse (mIJP) algorithm. The mIJP algorithm could generate desired trajectories for the joints even when the robot’s leg is in a singular posture. Owing to these proposed methods, the robot was able to perform various modes of locomotion both in simulations and experiments with improved stability.
... This attribute enables legged robot to accomplish tasks in hard and hazardous environments such as minefields Fig. 1. In order to devise walking robot to achieve this facilities, a very efficient and effective controller should be implemented in the robot [1]. In the context of legged robot control, there is a vast amount of work addressing locomotion control. ...
... Another approach to controlling legged systems is to consider the body Centre of Gravity (CoG) as a control point [11], [1], [12]. Then the contribution of each leg in the motion generation of the robot body is calculated accordingly [11]. ...
In the context of control, the motion of the legged robot is very challenging compared with a traditional fixed manipulator. Recently, many studies have been done to control the motion of legged robot using different techniques. In addition, manipulation tasks have been considered in many applications. These studies solve either the mobility or the manipulation problems, but to the best of our knowledge, legged robot systems that unite both properties are still not available. In this paper, a control algorithm is presented to control both locomotion and manipulation in a six-legged robot. Joint redundancy of the robot will be exploited to generate a whole body motion behaviour. The robot will accomplish many tasks simultaneously. To verify the effectiveness of the controller, the landmines detection process is considered as a task to be performed by the robot. The purpose of this application is to accelerate the mines detection operation by performing both walking and scanning simultaneously. A simulation of the whole operation conducted using MatLab SimMechanics.
